diff --git a/2021-02/index.html b/2021-02/index.html index 78ddd5428..96573bde8 100644 --- a/2021-02/index.html +++ b/2021-02/index.html @@ -4,13 +4,13 @@ What's Newsletter February 2021 | Experimenter Docs - +
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What's Newsletter

February 2021​

Hey Nimbus customers!

Welcome to our very first edition of our “What’s New”-sletter. As our customers, we want to make sure we keep you in the loop with new Nimbus features and capabilities on a monthly basis.

Are you looking for a great Friday night movie? I'd highly recommend watching Les, Lauren, and Marina's video for a deep dive into all our new features.

Here is a sneak preview of some of the video highlights:

  • 📇 Curious to see upcoming or completed experiments? Check out our new Nimbus directory!
  • Data is now flowing into the Nimbus from Jetstream and you can navigate to experiment results from the Directory View or from the side bar!
  • 📈 P.S don’t forget to check out the results of the On/Off About:Welcome experiment
  • 📊 Speaking of metrics, we have added Days Of Use as our guardrail metrics in the visualization. We now also have added graphs in the visualizations so experiment owners can see how the metric progresses over time.
  • ⏸️ Don't have all the information to complete the page you're working on right now? Don't worry, we’ve removed some of the required fields so it’s easier to save your progress.
  • 🗂️ Have you ever felt really lost trying to juggle Confluence, Jira, Google Docs, and Nimbus to find all your docs and tickets? We sure have, so Nimbus now offers the ability to add your relevant tickets and documents into Nimbus, and you can easily access them then from our summary page in the future.
  • ⚠️ We know, there's nothing more frustrating than getting an error message and not knowing what it means! ⚠️ We just couldn't let that happen on Nimbus and now we have the right validation and error messages in our UI. Luckily, you will always know what's wrong now.

Curious about what's next?

If you're interested in seeing what visualizations might look like with primary and secondary probes, feel free to check out this storybook.

Curious for even more? You can always check out our Storybook link to see what's in the pipeline.

Notable mentions:

Interested in learning more about how to use Nimbus? Here is our Nimbus onboarding page. Let Ana or Chelsea know if you’d like to go through onboarding and start using the platform.

If you'd like your work to be added to Next months "What's New"-sletter please follow this page for instructions.

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What's New in Nimbus June 2022

Welcome​

The Nimbus team has welcomed many new team members including:

  • Yashika Khurana, Web
  • Mike Williams, Web/Data
  • Daniel Berry, Data Science
  • Barret Rennie, Desktop

We're thrilled to have them all aboard!

Exciting Changes​

Mobile Messaging​

Nimbus now supports the ability to send messages to mobile clients through messaging surfaces in each mobile application. This unlocks the ability for Nimbus to run surveys, update users about important new information, or incorporate messages into other experiments/rollouts. More detailed information can be found here.

Mobile Language Targeting​

Mobile devices use a different list of languages than desktop Firefox does, and so the locale field on the Audience page didn't work as expected for mobile experiments. To address this, we've added a new 'Language' field on the Audience page that can be used to target device languages for mobile experiments.

Mobile Country Targeting​

Mobile devices do use the same list of countries that desktop Firefox does, and now country filtering has been implemented in the mobile client and so country targeting for mobile experiments is now available on the Audience page.

Analysis Fixes​

There have been several ongoing bugs that affected the automatic analysis of many experiments, which have now been resolved. All experiments should now have access to the full analysis report including all metrics. If any experiment analyses are still missing information, please reach out on #ask-experimenter.

Reporting​

Experimenter now has the ability to export a list of all Nimbus experiments as a CSV spreadsheet using the 'Reporting' button on the landing page. This can help organize large groups of experiments for review and planning.

Feature Imports​

Nimbus Features are defined using manifest files in the code repositories of each supported Firefox application across desktop and mobile. All features for all supported applications are now automatically imported by Experimenter and automatically updated as new features or changes to existing features occur. Each Feature has a schema that is either specified in the manifest or automatically generated based on the Feature's variables, and is used to validate the JSON that is provided in each branch.

Sticky Targeting​

Every experiment or rollout in Nimbus includes a targeting expression that clients use to determine whether to enroll or unenroll. The targeting expressions is frequently re evaluated after enrollment to determine if the client should unenroll early before the experiment ends. If any part of the targeting expression evaluates to False, then the client will unenroll. Sometimes this is the desired behaviour for an experiment, sometimes it is not. For cases where clients should not unenroll even if the targeting no longer matches, we now provide the option for Sticky Targeting, which can be found on the Audience page. If an experiment uses Sticky Targeting, then clients will not be unenrolled, even if the targeting becomes False, until the experiment ends.

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What's New in Nimbus July 2022

Welcome​

The Nimbus team has welcomed a new team member: Charlie Humphreys, Mobile.

We're thrilled to have Charlie on board!

Exciting Changes​

Sticky Enrollment​

  • When creating a new experiment, users can now opt for sticky enrollment (clients will not be unenrolled, even if the targeting becomes false, until the experiment ends). This information is now visible on the experiment’s "Details" page so that the user can review if they have opted for sticky enrollment.

  • In addition to that, a new warning has been added when creating a new experiment. If an experiment is using a targeting configuration that requires sticky enrollment, sticky enrollment would be auto selected to support that targeting expression.

Normandy​

Normandy will continue to be supported until Nimbus can take over for its responsibilities. So in the meanwhile, we have restored the create button in the old Experimenter interface.

Feedback​

Now a feedback link is also available on the landing page. Don’t wait to give us any feedback!

Feature enabled​

  • Desktop​

    Firefox Desktop 104 is removing support for the feature enabled toggle, so experiments Firefox Desktop 104 and above will require feature enabled to be on for every branch.

  • Mobile​

    The feature enabled flag has been disabled entirely for mobile experiments. Every branch of a mobile experiment must specify a feature value, which may explicitly be an empty object "{}".

Stay tuned for the next month's letter!

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What's New in Nimbus August 2022

Welcome​

The Nimbus team will have a demo booth at #moz-aloha All-Hands at the end of September! Come find us, chat about experimentation, and maybe even grab a sticker!

Exciting Changes​

Mobile First Run​

Mobile First Run refers to the work done in Nimbus to allow experiments during an user's first run of the Firefox mobile app.

  • Instrument Nimbus SDK in telemetry in order to provide experimental treatments during the onboarding experience.
    • This was affecting more than 100,000 clients and over 1.5M events/requests.

Improved Rollout Support​

A Rollout is an experiment where instead of configuring control and treatment branches, a feature is slowly rolled out to a population of clients. Experimenter now has improved support for Rollouts.

  • A client can:
    • enroll in a rollout, receive feature values, send telemetry
    • explicitly unenroll from the client UI
    • be automatically unenrolled by unpublishing the record from RS
    • enroll in a rollout for a feature, and then ALSO enroll in an experiment for the same feature, receive the experiment feature configuration while enrolled in the experiment, then be unenrolled from the experiment and fall back to the rollout feature configuration
  • User Interface
    • Now just check a box to make your experiment a rollout!
    • For rollout experiments, branch configuration UI is removed.

Cancel Review​

If you launch, end, or end enrollment for an experiment it automatically goes to Review. Now, there is an option to cancel that change and go back to an idle state where changes can be made.

UI/UX Updates​

Various user experience updates to streamline workflows and provide more consistency throughout the tool, like:

  • Improved labels
  • Analysis Results Time -- now you can see on the Results page when the last analysis started
  • Performance improvements
  • All displayed dates now include the year

Improved Testing and Validation​

  • Better integration testing
  • Better validation of configurations

Stay tuned for snakes, camels, and more in next month's edition of What's New in Nimbus!

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What's New in Nimbus March 2023

Rollouts​

Nimbus now supports edits for live rollouts! This allows owners to make changes to the population percentage and publish them. See our rollouts deep-dive for more info!

Nimbus Gradle Plugin​

The Nimbus gradle plugin has been moved from Android Components to Application Services. Updated information for utilizing the plugin can be found here.

Testing tooling​

For injecting experimental feature configurations for integration tests, we now have the following:

  • Added a malformedConfiguration event for app features to report bad configurations.
  • Added a record_past_event method to test display triggers based on the event store.

Messaging​

We’re in the last stretch of QA for adding a notification surface to the Nimbus Messaging system on Android (thank you @twhite and @mheres). For iOS, the survey surface has landed (thank you to @yrios and @roux).

More exciting changes coming next month!​

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Page Not Found

We could not find what you were looking for.

Please contact the owner of the site that linked you to the original URL and let them know their link is broken.

- + \ No newline at end of file diff --git a/access/index.html b/access/index.html index e890f66c8..b0705e3eb 100644 --- a/access/index.html +++ b/access/index.html @@ -4,7 +4,7 @@ Experiment Reviewers | Experimenter Docs - + @@ -12,7 +12,7 @@
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Experiment Reviewers

With Nimbus experiments, instead of a centralized committee responsible for launching and running experiments we want you, the experiment owner, to drive the process from ideation to completion.

If you are interested in learning more about your responsibilities, you can find our complete Access Control Policy here.

Onboarding for New Authors/Owners (L2)​

In order to use the Nimbus platform to create and launch experiments, you will need to go through the Experiment Owner onboarding process. Please ask in #ask-experimenter if you have any questions and for a shadow on the first experiments(s) you create.

Onboarding for New Reviewers (L3)​

Assuming you have been vouched for by a Nimbus administrator + a Product Owner (product manager or engineering manager if the team doesn’t have a dedicated PM), you should go through the following steps to get review access on Nimbus:

Basic access​

  • Read through rules and responsibilities for L3 users. Please note that you will have access to making changes in production for all experiments, and you should decline if you don't feel sufficiently qualified to review an experiment
  • Watch the half hour reviewer training video. The training slides are here for reference and links.
  • Follow the steps setup VPN
  • File a bug using this template to be added to either or all of the following collections: nimbus-experiments-desktop and/or nimbus-experiments-mobile and/or nimbus-web-experiments for your LDAP to be added on staging and production.
  • Note: on your first review(s) - please ask in #ask-experimenter for a shadow. It's very easy to partner with you sharing a screen to make sure you don't have any questions or uncertainties.

Testing Review Workflow on Staging​

  • Connect to the VPN
  • Go to Nimbus Staging (not Production!). Ask someone to create a dummy experiment and request review.
  • Click "Approve" on the dummy experiment, and then "Open Remote Settings": imageimage
  • Sign in. You should see a review request like this, if you don't refresh the page image
  • CHECK TO MAKE SURE THE SLUG OF THE DIFF MATCHES THE CHANGE YOU ARE APPROVING This is important!!
  • Press approve if everything looks good. If anything looks wrong, Reject and alert nimbus core team in #ask-experiments
  • Congrats, you have tested the workflow. You are now ready to review real experiments on production!

Reviews​

All changes to experiments and rollouts that impact production must be approved by a single L3 Nimbus reviewer, which you can request via the Nimbus console interface. You can find a list of recommended reviewers here.

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Adding new targeting attributes to Android

This page demonstrates how to add new targeting attributes to Android, enabling experiment creators more specific targeting. For more general documentation on targeting custom audiences, check out the custom audiences docs

Adding the attribute to the application​

The Nimbus SDK exposes a new customTargetingAttributes parameter in its initializer that is a Map<String, String> map. We can take advantage of this parameter to pass in new targeting attributes without modifying the Nimbus SDK at all.

danger

A current limitation is that both the key and the value of the targeting attribute are strings. Please reach out to the Nimbus SDK team for any targeting attributes that require integer comparison, or any other richer JEXL expressions that cannot be done with strings.

How to add a new attribute​

In NimbusSetup.kt NimbusAppInfo now optionally takes in a map customTargetingAttributes that will be used to add custom targeting. Simply add a new key-value pair to the map and it will be available for targeting. For example:

val appInfo = NimbusAppInfo(
    appName = "fenix",
    channel = BuildConfig.BUILD_TYPE,
    customTargetingAttributes = mapOf(
        "newTargetingAttributeName" to "targetingAttributeValue",
    )
)

Note that since we need to add the targeting attributes on the client code, the attribute changes will have to ride the trains before they are available for targeting.

Adding the attribute on experimenter​

After the targeting attribute is ready on the app, you will need to modify experimenter to allow creating experiments that target the attribute you created. Follow the instructions on the custom audiences page to add the new targeting on experimenter.

danger

The targeting JEXL expression on experimenter must use the same name as the key given to the SDK. For example, if the app defines a key-value pair, with key isFirstRun. experimenter expression must use the same name (i.e isFirstRun).

Example​

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set up well for this project, then this\n // shouldn\'t be needed.\n channels = [\n debug: "debug",\n nightly: "nightly",\n beta: "beta",\n release: "release",\n ]\n \n // This is generated by the FML and should be checked into git.\n // It will be fetched by Experimenter (the Nimbus experiment website)\n // and used to inform experiment configuration.\n //\n // *NOTE*: This value is optional, and is not necessary when Nimbus is being used\n // as part of a library.\n experimenterManifest = ".experimenter.yaml"\n \n // This is an optional value, and updates the plugin to use a copy of application\n // services. The path should be relative to the root project directory.\n // *NOTE*: This example will not work for all projects, but should work for Fenix, Focus, and Android Components\n applicationServicesDir = gradle.hasProperty(\'localProperties.autoPublish.application-services.dir\') \n ? gradle.getProperty(\'localProperties.autoPublish.application-services.dir\') : null\n}\n')),(0,r.kt)("p",null,"In this case, it should generate a file named in the ",(0,r.kt)("inlineCode",{parentName:"p"},"nimbus.fml.yaml")," file. In the case of Fenix, this is called ",(0,r.kt)("inlineCode",{parentName:"p"},"FxNimbus"),"."),(0,r.kt)("h1",{id:"the-start-up-sequence"},"The start-up sequence"),(0,r.kt)("p",null,"Before using Nimbus in your Android app, you need to start it."),(0,r.kt)("p",null,"The Nimbus SDK is a configuration store, making configuration available to the any thread, and \u2014 to a first approximation\u2014 to be immutable within the same session of the app."),(0,r.kt)("p",null,"For this reason, we want to be starting the Nimbus SDK as close to the beginning of the start of the app as possible."),(0,r.kt)("p",null,"In Firefox for Android and Focus for Android, this is done at the beginning of the ",(0,r.kt)("inlineCode",{parentName:"p"},"Application#onCreate()")," method."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"},'class MyApplication: Application() {\n\n lateinit var nimbus: NimbusInterface\n\n override fun onCreate() {\n beginNimbusSetup()\n\n\n // do the rest of the set up here.\n\n finishNimbusSetup()\n }\n\n fun beginNimbusSetup() {\n Megazord.init()\n\n nimbus = createNimbus(this, NIMBUS_REMOTE_SETTINGS_ENDPOINT)\n }\n\n fun finishNimbusSetup() {\n nimbus.fetchExperiments()\n }\n\n fun createNimbus(context: Context, urlString: String): NimbusInterface {\n val isAppFirstRun = context.settings().isFirstRun\n val customTargetingAttibutes = JSONObject().apply {\n // Put any custom attributes you want to use to segment an audience on to\n // target your experiments.\n put("is_first_run", isAppFirstRun)\n }\n\n val appInfo = NimbusAppInfo(\n appName = "my-app-name",\n channel = BuildConfig.BUILD_TYPE,\n customTargetingAttributes = customTargetingAttributes\n )\n\n // Use the Nimbus builder to build a NimbusInterface object.\n return NimbusBuilder(context).apply {\n url = urlString\n errorReporter = { message, e ->\n Logger.error("Nimbus error: $message", e)\n }\n\n }.build(appInfo)\n }\n}\n')),(0,r.kt)("p",null,"Notes:"),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"Megazord.init()")," is called before ",(0,r.kt)("inlineCode",{parentName:"li"},"createNimbus()"),"."),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"createNimbus")," uses a ",(0,r.kt)("inlineCode",{parentName:"li"},"NimbusBuilder")," to create the Nimbus object."),(0,r.kt)("li",{parentName:"ol"},"We build a ",(0,r.kt)("inlineCode",{parentName:"li"},"JSONObject")," of custom targeting attributes."),(0,r.kt)("li",{parentName:"ol"},"The ",(0,r.kt)("inlineCode",{parentName:"li"},"nimbus.fetchExperiments()")," method is called sometime at or after the app has started.")),(0,r.kt)("h2",{id:"nimbusbuilder-configuration"},(0,r.kt)("inlineCode",{parentName:"h2"},"NimbusBuilder")," configuration"),(0,r.kt)("h3",{id:"getting-errors-out-of-nimbus"},"Getting errors out of ",(0,r.kt)("inlineCode",{parentName:"h3"},"Nimbus")),(0,r.kt)("p",null,"By design, Nimbus is deliberately unobtrusive; if it fails then it should not crash, but continue as if not enrolled in any experiments."),(0,r.kt)("p",null,"The ",(0,r.kt)("inlineCode",{parentName:"p"},"errorReporter")," callback is there to connect ",(0,r.kt)("inlineCode",{parentName:"p"},"Nimbus")," to any error reporting framework in the rest of the app."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"},' return NimbusBuilder(context).apply {\n //\xa0\u2026\n errorReporter = { message, e ->\n Logger.error("Nimbus error: $message", e)\n }\n // \u2026\n }.build(appInfo)\n')),(0,r.kt)("h3",{id:"connecting-the-nimbusinterface-to-fml-generated-code"},"Connecting the ",(0,r.kt)("inlineCode",{parentName:"h3"},"NimbusInterface")," to FML generated code"),(0,r.kt)("p",null,"The FML generated code has a runtime dependency on the ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusInterface"),"."),(0,r.kt)("p",null,"To connect it to the Nimbus object, we need to tell the ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusBuilder"),". In this case, the generated class is ",(0,r.kt)("inlineCode",{parentName:"p"},"FxNimbus"),"."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"}," return NimbusBuilder(context).apply {\n // \u2026\n // Connect FxNimbus to the Nimbus SDK.\n featureManifest = FxNimbus\n // \u2026\n }.build(appInfo)\n")),(0,r.kt)("h3",{id:"handling-first-run-experiments"},"Handling First Run experiments"),(0,r.kt)("p",null,"Since ",(0,r.kt)("inlineCode",{parentName:"p"},"fetchExperiments")," from the remote settings URL is slow, and we wish to be able have access to the Nimbus experimental configuration as early in start up as possible, Nimbus downloads and caches the experiment recipes on the ",(0,r.kt)("inlineCode",{parentName:"p"},"n"),"th run of the app and only applies them and makes them available to the app at the beginning of the ",(0,r.kt)("em",{parentName:"p"},"next")," i.e. the ",(0,r.kt)("inlineCode",{parentName:"p"},"(n + 1)"),"th run of the app."),(0,r.kt)("p",null,"Astute readers will notice that when ",(0,r.kt)("inlineCode",{parentName:"p"},"n = 0"),", i.e. the very first time the app is run, there are no experiment recipes downloaded. If Remote Settings experiment recipes JSON payload is available as a ",(0,r.kt)("inlineCode",{parentName:"p"},"raw/")," resource, it can be loaded in at first run:"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"}," return NimbusBuilder(context).apply {\n // \u2026\n isFirstRun = isAppFirstRun\n initialExperiments = R.raw.initial_experiments\n timeoutLoadingExperiment = TIME_OUT_LOADING_EXPERIMENT_FROM_DISK_MS // defaults to 200 (ms)\n // \u2026\n }.build(appInfo)\n")),(0,r.kt)("p",null,"The ",(0,r.kt)("inlineCode",{parentName:"p"},"initial_experiments.json")," file can be downloaded, either as part of the build, or in an automated/timed job. e.g. this is the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/mozilla-mobile/fenix/blob/main/.github/workflows/fenix-update-nimbus-experiments.yml"},"Github Action workflow used by Fenix"),"."),(0,r.kt)("h4",{id:"to-check-if-the-firstrun-experiment-merged-into-beta-to-catch-the-next-release"},"To check if the firstrun experiment merged into beta to catch the next release"),(0,r.kt)("p",null,"First run experiments need to be in the beta build 8-11 days before release, so that they are in the release candidate. Final build happens 8 days before release on Monday - so best to get in and uplift approved by Friday at the latest. On Android the Release Candidate goes out to 5% of users a week before general release."),(0,r.kt)("p",null,"After the change is made in Nimbus/Experimenter to launch, enrollment end, or end the experiment - a github action kicks off the PR automatically to update 'initial_experiments.json'. Then a mobile engineer needs to r+ that PR and request uplift to Beta. If you replace 'version number' in the following file name, you can check this file to see if the experiment config is in the right state before release candidate build ",(0,r.kt)("a",{parentName:"p",href:"https://raw.githubusercontent.com/mozilla-mobile/firefox-android/releases_v'version"},"https://raw.githubusercontent.com/mozilla-mobile/firefox-android/releases_v'version")," number'/fenix/app/src/main/res/raw/initial_experiments.json."),(0,r.kt)("h3",{id:"using-the-experiments-preview-collection"},"Using the experiments preview collection"),(0,r.kt)("p",null,"The preview collection is a staging area for new experiments to be tested on the device. This should be toggleable via the UI, but should trigger a restart."),(0,r.kt)("p",null,"Adding the ",(0,r.kt)("inlineCode",{parentName:"p"},"usePreviewCollection")," flag allows the builder to configure a ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusInterface")," object connected to the experiment recipes in the preview collection."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"}," // Use the Nimbus builder to build a NimbusInterface object.\n return NimbusBuilder(context).apply {\n // \u2026\n usePreviewCollection = context.settings().nimbusUsePreview\n // \u2026\n }.build(appInfo)\n")),(0,r.kt)("h2",{id:"a-complete-nimbusbuilder-example"},"A complete ",(0,r.kt)("inlineCode",{parentName:"h2"},"NimbusBuilder")," example"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"},' return NimbusBuilder(context).apply {\n url = urlString\n errorReporter = { message, e ->\n Logger.error("Nimbus error: $message", e)\n }\n initialExperiments = R.raw.initial_experiments\n usePreviewCollection = context.settings().nimbusUsePreview\n isFirstRun = isAppFirstRun\n sharedPreferences = context.settings().preferences\n // Optional callbacks.\n onCreateCallback = { nimbus ->\n // called when nimbus is set up\n }\n onFetchCallback = {\n // called each time the app fetches experiments\n }\n onApplyCallback = {\n // called each time the applies the fetched experiments.\n }\n }.build(appInfo)\n')),(0,r.kt)("h2",{id:"instrumenting-the-app-for-testing"},"Instrumenting the app for testing"),(0,r.kt)("p",null,"The ",(0,r.kt)("a",{parentName:"p",href:"/nimbus-cli"},(0,r.kt)("inlineCode",{parentName:"a"},"nimbus-cli"))," allows QA and engineers to launch the app in different experimental configurations. It largely obviates the need for configuring Nimbus to use the preview collection, above."),(0,r.kt)("p",null,"To connect the ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusInterface")," object to the command line, we need to feed the intent from the app's launch activity to the ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusInterface"),"."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"},"\nimport org.mozilla.experiments.nimbus.initializeTooling\n\nopen class HomeActivity : AppCompatActivity() {)\n override fun onCreate(savedInstanceState: Bundle?) {\n // Find the nimbus singleton\n val app = application as MyApplication\n val nimbus = app.nimbus\n // Pass it the launch intent\n nimbus.initializeTooling(applicationContext, intent)\n // \u2026\n }\n")))}m.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/6c2d93c2.6527012b.js b/assets/js/6c2d93c2.6527012b.js deleted file mode 100644 index 845890a3d..000000000 --- a/assets/js/6c2d93c2.6527012b.js +++ /dev/null @@ -1 +0,0 @@ -"use strict";(self.webpackChunkexperimenter_docs=self.webpackChunkexperimenter_docs||[]).push([[2708],{3905:(e,n,t)=>{t.d(n,{Zo:()=>u,kt:()=>g});var i=t(7294);function r(e,n,t){return n in e?Object.defineProperty(e,n,{value:t,enumerable:!0,configurable:!0,writable:!0}):e[n]=t,e}function a(e,n){var t=Object.keys(e);if(Object.getOwnPropertySymbols){var i=Object.getOwnPropertySymbols(e);n&&(i=i.filter((function(n){return Object.getOwnPropertyDescriptor(e,n).enumerable}))),t.push.apply(t,i)}return t}function o(e){for(var n=1;n=0||(r[t]=e[t]);return r}(e,n);if(Object.getOwnPropertySymbols){var a=Object.getOwnPropertySymbols(e);for(i=0;i=0||Object.prototype.propertyIsEnumerable.call(e,t)&&(r[t]=e[t])}return r}var s=i.createContext({}),p=function(e){var n=i.useContext(s),t=n;return e&&(t="function"==typeof e?e(n):o(o({},n),e)),t},u=function(e){var n=p(e.components);return i.createElement(s.Provider,{value:n},e.children)},d="mdxType",m={inlineCode:"code",wrapper:function(e){var n=e.children;return i.createElement(i.Fragment,{},n)}},c=i.forwardRef((function(e,n){var t=e.components,r=e.mdxType,a=e.originalType,s=e.parentName,u=l(e,["components","mdxType","originalType","parentName"]),d=p(t),c=r,g=d["".concat(s,".").concat(c)]||d[c]||m[c]||a;return t?i.createElement(g,o(o({ref:n},u),{},{components:t})):i.createElement(g,o({ref:n},u))}));function g(e,n){var t=arguments,r=n&&n.mdxType;if("string"==typeof e||r){var a=t.length,o=new Array(a);o[0]=c;var l={};for(var s in n)hasOwnProperty.call(n,s)&&(l[s]=n[s]);l.originalType=e,l[d]="string"==typeof e?e:r,o[1]=l;for(var p=2;p{t.r(n),t.d(n,{assets:()=>s,contentTitle:()=>o,default:()=>m,frontMatter:()=>a,metadata:()=>l,toc:()=>p});var i=t(7462),r=(t(7294),t(3905));const a={id:"getting-started-for-android-engineers",title:"Getting Started for Android Engineers",slug:"getting-started-for-android-engineers"},o="Introduction",l={unversionedId:"getting-started/engineers/getting-started-for-android-engineers",id:"getting-started/engineers/getting-started-for-android-engineers",title:"Getting Started for Android Engineers",description:"Nimbus is an experimentation platform from Mozilla.",source:"@site/docs/getting-started/engineers/01-android-integration.md",sourceDirName:"getting-started/engineers",slug:"/getting-started/engineers/getting-started-for-android-engineers",permalink:"/getting-started/engineers/getting-started-for-android-engineers",draft:!1,editUrl:"https://github.com/mozilla/experimenter-docs/edit/main/docs/getting-started/engineers/01-android-integration.md",tags:[],version:"current",sidebarPosition:1,frontMatter:{id:"getting-started-for-android-engineers",title:"Getting Started for Android Engineers",slug:"getting-started-for-android-engineers"},sidebar:"sidebar",previous:{title:"Getting started with implementation",permalink:"/getting-started/engineers/for-engineers"},next:{title:"Getting Started for iOS Engineers",permalink:"/getting-started/engineers/getting-started-for-ios-engineers"}},s={},p=[{value:"Building with the Nimbus FML gradle plugin",id:"building-with-the-nimbus-fml-gradle-plugin",level:2},{value:"NimbusBuilder configuration",id:"nimbusbuilder-configuration",level:2},{value:"Getting errors out of Nimbus",id:"getting-errors-out-of-nimbus",level:3},{value:"Connecting the NimbusInterface to FML generated code",id:"connecting-the-nimbusinterface-to-fml-generated-code",level:3},{value:"Handling First Run experiments",id:"handling-first-run-experiments",level:3},{value:"Using the experiments preview collection",id:"using-the-experiments-preview-collection",level:3},{value:"A complete NimbusBuilder example",id:"a-complete-nimbusbuilder-example",level:2},{value:"Instrumenting the app for testing",id:"instrumenting-the-app-for-testing",level:2}],u={toc:p},d="wrapper";function m(e){let{components:n,...t}=e;return(0,r.kt)(d,(0,i.Z)({},u,t,{components:n,mdxType:"MDXLayout"}),(0,r.kt)("h1",{id:"introduction"},"Introduction"),(0,r.kt)("p",null,"Nimbus is an experimentation platform from Mozilla."),(0,r.kt)("p",null,"This document shows you how to set up the Nimbus SDK with a new Android app. It assumes that your app is already using the Glean SDK and Android Components."),(0,r.kt)("h1",{id:"building-with-nimbus"},"Building with Nimbus"),(0,r.kt)("p",null,"Nimbus is distributed through bundled Rust code as part of Mozilla's Application Services ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/mozilla/application-services/blob/main/docs/design/megazords.md"},'"Megazord"'),"."),(0,r.kt)("p",null,"In ",(0,r.kt)("inlineCode",{parentName:"p"},"app/build.gradle"),", in the ",(0,r.kt)("inlineCode",{parentName:"p"},"dependencies")," block, include the ",(0,r.kt)("inlineCode",{parentName:"p"},"implementation")," line for Nimbus:"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-gradle"},'dependencies {\n \u2026\n implementation "org.mozilla.appservices:nimbus:${Versions.mozilla_appservices}"\n \u2026\n}\n')),(0,r.kt)("h2",{id:"building-with-the-nimbus-fml-gradle-plugin"},"Building with the Nimbus FML gradle plugin"),(0,r.kt)("p",null,"The ",(0,r.kt)("a",{parentName:"p",href:"/fml-spec"},"Feature Manifest Language")," provides type-safe access to configuration coming out of the Nimbus SDK, and is used to configure your application features, by\ngenerating Kotlin from a Feature Manifest."),(0,r.kt)("p",null,"The ",(0,r.kt)("inlineCode",{parentName:"p"},"tooling-nimbus-gradle")," plugin manages the download of the tooling, the generating of the Kotlin code, and is configured by gradle."),(0,r.kt)("p",null,"In your top-level ",(0,r.kt)("inlineCode",{parentName:"p"},"build.gradle"),":"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-gradle"},'buildscript {\n dependencies {\n classpath "org.mozilla.appservices:tooling-nimbus-gradle:${Versions.mozilla_appservices}"\n }\n}\n')),(0,r.kt)("p",null,"and in ",(0,r.kt)("inlineCode",{parentName:"p"},"app/build.gradle"),":"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-gradle"},'apply plugin: "org.mozilla.appservices.nimbus-gradle-plugin"\n\nnimbus {\n // The path to the Nimbus feature manifest file\n manifestFile = "nimbus.fml.yaml"\n\n // Map from the variant name to the channel as experimenter and nimbus understand it.\n // If nimbus\'s channels were accurately set up well for this project, then this\n // shouldn\'t be needed.\n channels = [\n debug: "debug",\n nightly: "nightly",\n beta: "beta",\n release: "release",\n ]\n \n // This is generated by the FML and should be checked into git.\n // It will be fetched by Experimenter (the Nimbus experiment website)\n // and used to inform experiment configuration.\n //\n // *NOTE*: This value is optional, and is not necessary when Nimbus is being used\n // as part of a library.\n experimenterManifest = ".experimenter.yaml"\n \n // This is an optional value, and updates the plugin to use a copy of application\n // services. The path should be relative to the root project directory.\n // *NOTE*: This example will not work for all projects, but should work for Fenix, Focus, and Android Components\n applicationServicesDir = gradle.hasProperty(\'localProperties.autoPublish.application-services.dir\') \n ? gradle.getProperty(\'localProperties.autoPublish.application-services.dir\') : null\n}\n')),(0,r.kt)("p",null,"In this case, it should generate a file named in the ",(0,r.kt)("inlineCode",{parentName:"p"},"nimbus.fml.yaml")," file. In the case of Fenix, this is called ",(0,r.kt)("inlineCode",{parentName:"p"},"FxNimbus"),"."),(0,r.kt)("h1",{id:"the-start-up-sequence"},"The start-up sequence"),(0,r.kt)("p",null,"Before using Nimbus in your Android app, you need to start it."),(0,r.kt)("p",null,"The Nimbus SDK is a configuration store, making configuration available to the any thread, and \u2014 to a first approximation\u2014 to be immutable within the same session of the app."),(0,r.kt)("p",null,"For this reason, we want to be starting the Nimbus SDK as close to the beginning of the start of the app as possible."),(0,r.kt)("p",null,"In Firefox for Android and Focus for Android, this is done at the beginning of the ",(0,r.kt)("inlineCode",{parentName:"p"},"Application#onCreate()")," method."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"},'class MyApplication: Application() {\n\n lateinit var nimbus: NimbusInterface\n\n override fun onCreate() {\n beginNimbusSetup()\n\n\n // do the rest of the set up here.\n\n finishNimbusSetup()\n }\n\n fun beginNimbusSetup() {\n Megazord.init()\n\n nimbus = createNimbus(this, NIMBUS_REMOTE_SETTINGS_ENDPOINT)\n }\n\n fun finishNimbusSetup() {\n nimbus.fetchExperiments()\n }\n\n fun createNimbus(context: Context, urlString: String): NimbusInterface {\n val isAppFirstRun = context.settings().isFirstRun\n val customTargetingAttibutes = JSONObject().apply {\n // Put any custom attributes you want to use to segment an audience on to\n // target your experiments.\n put("is_first_run", isAppFirstRun)\n }\n\n val appInfo = NimbusAppInfo(\n appName = "my-app-name",\n channel = BuildConfig.BUILD_TYPE,\n customTargetingAttributes = customTargetingAttributes\n )\n\n // Use the Nimbus builder to build a NimbusInterface object.\n return NimbusBuilder(context).apply {\n url = urlString\n errorReporter = { message, e ->\n Logger.error("Nimbus error: $message", e)\n }\n\n }.build(appInfo)\n }\n}\n')),(0,r.kt)("p",null,"Notes:"),(0,r.kt)("ol",null,(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"Megazord.init()")," is called before ",(0,r.kt)("inlineCode",{parentName:"li"},"createNimbus()"),"."),(0,r.kt)("li",{parentName:"ol"},(0,r.kt)("inlineCode",{parentName:"li"},"createNimbus")," uses a ",(0,r.kt)("inlineCode",{parentName:"li"},"NimbusBuilder")," to create the Nimbus object."),(0,r.kt)("li",{parentName:"ol"},"We build a ",(0,r.kt)("inlineCode",{parentName:"li"},"JSONObject")," of custom targeting attributes."),(0,r.kt)("li",{parentName:"ol"},"The ",(0,r.kt)("inlineCode",{parentName:"li"},"nimbus.fetchExperiments()")," method is called sometime at or after the app has started.")),(0,r.kt)("h2",{id:"nimbusbuilder-configuration"},(0,r.kt)("inlineCode",{parentName:"h2"},"NimbusBuilder")," configuration"),(0,r.kt)("h3",{id:"getting-errors-out-of-nimbus"},"Getting errors out of ",(0,r.kt)("inlineCode",{parentName:"h3"},"Nimbus")),(0,r.kt)("p",null,"By design, Nimbus is deliberately unobtrusive; if it fails then it should not crash, but continue as if not enrolled in any experiments."),(0,r.kt)("p",null,"The ",(0,r.kt)("inlineCode",{parentName:"p"},"errorReporter")," callback is there to connect ",(0,r.kt)("inlineCode",{parentName:"p"},"Nimbus")," to any error reporting framework in the rest of the app."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"},' return NimbusBuilder(context).apply {\n //\xa0\u2026\n errorReporter = { message, e ->\n Logger.error("Nimbus error: $message", e)\n }\n // \u2026\n }.build(appInfo)\n')),(0,r.kt)("h3",{id:"connecting-the-nimbusinterface-to-fml-generated-code"},"Connecting the ",(0,r.kt)("inlineCode",{parentName:"h3"},"NimbusInterface")," to FML generated code"),(0,r.kt)("p",null,"The FML generated code has a runtime dependency on the ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusInterface"),"."),(0,r.kt)("p",null,"To connect it to the Nimbus object, we need to tell the ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusBuilder"),". In this case, the generated class is ",(0,r.kt)("inlineCode",{parentName:"p"},"FxNimbus"),"."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"}," return NimbusBuilder(context).apply {\n // \u2026\n // Connect FxNimbus to the Nimbus SDK.\n featureManifest = FxNimbus\n // \u2026\n }.build(appInfo)\n")),(0,r.kt)("h3",{id:"handling-first-run-experiments"},"Handling First Run experiments"),(0,r.kt)("p",null,"Since ",(0,r.kt)("inlineCode",{parentName:"p"},"fetchExperiments")," from the remote settings URL is slow, and we wish to be able have access to the Nimbus experimental configuration as early in start up as possible, Nimbus downloads and caches the experiment recipes on the ",(0,r.kt)("inlineCode",{parentName:"p"},"n"),"th run of the app and only applies them and makes them available to the app at the beginning of the ",(0,r.kt)("em",{parentName:"p"},"next")," i.e. the ",(0,r.kt)("inlineCode",{parentName:"p"},"(n + 1)"),"th run of the app."),(0,r.kt)("p",null,"Astute readers will notice that when ",(0,r.kt)("inlineCode",{parentName:"p"},"n = 0"),", i.e. the very first time the app is run, there are no experiment recipes downloaded. If Remote Settings experiment recipes JSON payload is available as a ",(0,r.kt)("inlineCode",{parentName:"p"},"raw/")," resource, it can be loaded in at first run:"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"}," return NimbusBuilder(context).apply {\n // \u2026\n isFirstRun = isAppFirstRun\n initialExperiments = R.raw.initial_experiments\n timeoutLoadingExperiment = TIME_OUT_LOADING_EXPERIMENT_FROM_DISK_MS // defaults to 200 (ms)\n // \u2026\n }.build(appInfo)\n")),(0,r.kt)("p",null,"The ",(0,r.kt)("inlineCode",{parentName:"p"},"initial_experiments.json")," file can be downloaded, either as part of the build, or in an automated/timed job. e.g. this is the ",(0,r.kt)("a",{parentName:"p",href:"https://github.com/mozilla-mobile/fenix/blob/main/.github/workflows/fenix-update-nimbus-experiments.yml"},"Github Action workflow used by Fenix"),"."),(0,r.kt)("h3",{id:"using-the-experiments-preview-collection"},"Using the experiments preview collection"),(0,r.kt)("p",null,"The preview collection is a staging area for new experiments to be tested on the device. This should be toggleable via the UI, but should trigger a restart."),(0,r.kt)("p",null,"Adding the ",(0,r.kt)("inlineCode",{parentName:"p"},"usePreviewCollection")," flag allows the builder to configure a ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusInterface")," object connected to the experiment recipes in the preview collection."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"}," // Use the Nimbus builder to build a NimbusInterface object.\n return NimbusBuilder(context).apply {\n // \u2026\n usePreviewCollection = context.settings().nimbusUsePreview\n // \u2026\n }.build(appInfo)\n")),(0,r.kt)("h2",{id:"a-complete-nimbusbuilder-example"},"A complete ",(0,r.kt)("inlineCode",{parentName:"h2"},"NimbusBuilder")," example"),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"},' return NimbusBuilder(context).apply {\n url = urlString\n errorReporter = { message, e ->\n Logger.error("Nimbus error: $message", e)\n }\n initialExperiments = R.raw.initial_experiments\n usePreviewCollection = context.settings().nimbusUsePreview\n isFirstRun = isAppFirstRun\n sharedPreferences = context.settings().preferences\n // Optional callbacks.\n onCreateCallback = { nimbus ->\n // called when nimbus is set up\n }\n onFetchCallback = {\n // called each time the app fetches experiments\n }\n onApplyCallback = {\n // called each time the applies the fetched experiments.\n }\n }.build(appInfo)\n')),(0,r.kt)("h2",{id:"instrumenting-the-app-for-testing"},"Instrumenting the app for testing"),(0,r.kt)("p",null,"The ",(0,r.kt)("a",{parentName:"p",href:"/nimbus-cli"},(0,r.kt)("inlineCode",{parentName:"a"},"nimbus-cli"))," allows QA and engineers to launch the app in different experimental configurations. It largely obviates the need for configuring Nimbus to use the preview collection, above."),(0,r.kt)("p",null,"To connect the ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusInterface")," object to the command line, we need to feed the intent from the app's launch activity to the ",(0,r.kt)("inlineCode",{parentName:"p"},"NimbusInterface"),"."),(0,r.kt)("pre",null,(0,r.kt)("code",{parentName:"pre",className:"language-kotlin"},"\nimport org.mozilla.experiments.nimbus.initializeTooling\n\nopen class HomeActivity : AppCompatActivity() {)\n override fun onCreate(savedInstanceState: Bundle?) {\n // Find the nimbus singleton\n val app = application as MyApplication\n val nimbus = app.nimbus\n // Pass it the launch intent\n nimbus.initializeTooling(applicationContext, intent)\n // \u2026\n }\n")))}m.isMDXComponent=!0}}]); \ No newline at end of file diff --git a/assets/js/runtime~main.a8c3b06c.js b/assets/js/runtime~main.6a2c61a7.js similarity index 80% rename from 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e={1303:0,532:0};r.f.j=(a,f)=>{var b=r.o(e,a)?e[a]:void 0;if(0!==b)if(b)f.push(b[2]);else if(/^(1303|532)$/.test(a))e[a]=0;else{var d=new Promise(((f,d)=>b=e[a]=[f,d]));f.push(b[2]=d);var c=r.p+r.u(a),t=new Error;r.l(c,(f=>{if(r.o(e,a)&&(0!==(b=e[a])&&(e[a]=void 0),b)){var d=f&&("load"===f.type?"missing":f.type),c=f&&f.target&&f.target.src;t.message="Loading chunk "+a+" failed.\n("+d+": "+c+")",t.name="ChunkLoadError",t.type=d,t.request=c,b[1](t)}}),"chunk-"+a,a)}},r.O.j=a=>0===e[a];var a=(a,f)=>{var b,d,c=f[0],t=f[1],o=f[2],n=0;if(c.some((a=>0!==e[a]))){for(b in t)r.o(t,b)&&(r.m[b]=t[b]);if(o)var i=o(r)}for(a&&a(f);n Experiment Sizing Command-line Interface | Experimenter Docs - +

Experiment Sizing Command-line Interface

The sample size calculation functionality contained in the Mozanalysis library is accessible via a command-line interface (CLI), auto-sizing. This CLI is intended to enable rapid analyses for simple experiments or experiments with targeting similar to past experiments.

Sizing job configuration​

The sizing CLI relies on a local TOML file to configure the job. This TOML file contains the metrics, segments, and parameters used to carry out the analysis. Sample sizes based on these parameters are calculated using the mozanalysis.frequentist_stats.sample_size.z_or_t_ind_sample_size_calc method. Note that segments in Mozanalysis refer to the filters used to identify clients that will satisfy targeting for an experiment (whereas segments in Jetstream denote groups of clients to examine during post-hoc analysis).

TOML file layout​

The TOML configuration file must contain a metrics, data_sources, segments, and segments.data_sources section, each containing the definitions for those of interest for the experiments. The definition of each of these follows the same patterns as Jetstream, and details on how to define your own inside of the TOML file can be found here.

The TOML file can also contain references to metrics, segments, data sources, and segment data sources that are already contained in metric-hub. To reference these pre-defined objects, an import_from_metric_hub list can be included in the TOML file. For instance, to import the active_hours metric for Firefox Desktop, the following is included in the TOML config file:

[metrics.import_from_metric_hub]
firefox_desktop = ["active_hours"]

A parameters section in the TOML file is used to define the data collection period for the analysis and the parameters used to calculate sample sizes. This section consists of two subsections: parameters.sizing and parameters.dates:

  1. parameters.sizing: Contains two tags, power and effect_size. These tags should contain lists of values for each parameter, and a sample size will be calculated for all metrics provided in the TOML file for each combination of power and effect size in those lists.
  2. parameters.dates: Contains the start_date (in "%Y-%m-%d" format, e.g. "2023-01-01"), num_dates_enrollment, and analysis_length values. For details on how those values are used to query historical data, see the Mozanalysis documentation.

CLI commands​

The sizing CLI is invoked using the command auto_sizing run. The following options are available at invocation:

OptionDescription
--project_id, --project-idBigQuery project to write metrics table to
--dataset_id, --dataset-idBigQuery dataset to write metrics table to
--bucketGCP bucket to write output JSON to. If not provided, JSON will be saved to the same directory as the config TOML
--target_slugName for the experiment. Used when naming metrics table and output file
--local_configPath to the configuration TOML file

CLI output​

Results for experiment sizing are saved in JSON format. If a GCP bucket is provided in the --bucket option at invocation, this JSON file is saved in a sample_sizes folder in that bucket. If no bucket is provided, the JSON results are saved to the same folder as the TOML configuration file.

The results JSON will include an entry for each combination of power and effect size provided in the config file. Each of these has an entry for each metric, where the required population percent and sample size per branch to achieve that power with that effect size is recorded. A final tag is included with a parameters dictionary, storing the power and effect size values. The following example shows the results for a sizing job with the metrics uri_count and active_hours:

{"Power0.8EffectSize0.01": {
        "uri_count": {"sample_size_per_branch": 475269, "population_percent_per_branch": 6.25}, 
        "active_hours": {"sample_size_per_branch": 327233, "population_percent_per_branch": 4.3}, 
        "parameters": {"power": 0.8, "effect_size": 0.01}}, 
"Power0.8EffectSize0.02": {
        "uri_count": {"sample_size_per_branch": 118817, "population_percent_per_branch": 1.56}, 
        "active_hours": {"sample_size_per_branch": 81808, "population_percent_per_branch": 1.08}, 
        "parameters": {"power": 0.8, "effect_size": 0.02}}}
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/bucketing/index.html b/bucketing/index.html index 0e413d3da..67ba2e39a 100644 --- a/bucketing/index.html +++ b/bucketing/index.html @@ -4,13 +4,13 @@ Bucketing | Experimenter Docs - +

Bucketing

Bucketing is the process of randomly assigning users to experiment branches. When a user is “bucketed” into an experiment, it means that the configuration in one of its branches (such as a change to part of the UI) can be activated, and that any interactions we record from that moment on can be associated with the experiment and branch identifier.

which experiments?

This documentation applies to experiments launched to Desktop, iOS, and Android Firefox through the "Nimbus" or "Normandy" systems. Differences between platforms are noted when relevant.

Assumptions​

In order to support the analysis of controlled experiments, we must be able to satisfy the following functional requirements:

  • We can randomly assign users to one or more branches of of an experiment.
  • A single user can enroll in multiple experiments simultaneously.
  • We can specify certain characteristics about a client that must be met for a client to bucket into an experiment, such as region.
  • We can assign users to unevenly distributed branches(e.g. 10% to A, 90% to B)
  • We can control interactions between experiments (i.e. ensure experiments do not overlap) when we want to.
  • We can observe which users have bucketed into which experiments/branches and when.

We assume the following statistical requirements:

  • Assignment of targeted clients to branches is uniformly random with respect to all observables. If we were to look at the set of users for each branch (where unique users are identified by the randomization unit), we should see roughly the same distribution of locale, location, profile age, etc.
  • Branch assignment must not depend on anything the user can influence.
  • Actual enrollment is probabilistically equal to the percentage of total traffic allocated to that branch. e.g. If we configured an experiment with two equal branches to enroll 10% of the population, we should see 5% of the total population enroll in each branch.
  • Enrollment in a branch is deterministic. Given the same experiment configuration, interaction rules, and user identifier, the result should always be the same. Shipping a new experiment must not change the basis for assigning a client to a branch.
  • Enrollment in a branch is persistent. Once a user is bucketed into a branch, they should continue to see the same branch for the duration of the experiment.
  • We should be able to control undesired interactions between experiments based on the specific requirements of our system. For example, as a first step, we can’t enroll users in more than one branch that contains configuration for the same feature.

Implementation​

At a high level, we bucket users into experiments client-side by taking a hash of a randomly generated user id and some configuration delivered from our experimentation servers. Assignment happens when configuration is synced to the client and sends enrollment telemetry.

Configuration​

note

This example uses the Nimbus experiment format. While the Normandy format is different, the client-side algorithm is almost identical. Many fields have been omitted for brevity.

{
  "slug": "my-cool-test",
  "targeting": "browserSettings.update.channel == 'release'",
  "bucketConfig": {
    "start": 5000,
    "count": 2000,
    "total": 10000,
    "namespace": "aboutwelcome-1",
    "randomizationUnit": "normandy_id"
  },

  "branches": [
    { "slug": "control", "ratio": 1 },
    { "slug": "treatment", "ratio": 1 }
  ]
}
  • targeting specifies conditions that must be met before the client can be considered. In this case, the user must in the release channel (beta or nightly users will not be considered).
  • count is a fraction of total representing the chance of getting bucketed. In this case, the chance is 20%.
  • start is an integer representing a "range" of buckets, which allows for isolation of experiments along a single namespace. In this example, the start is set to 5000, which would isolate it from users in an existing experiment with a start of 0 and a count of 5000.

Randomization Unit​

Bucketing uses a stable unique identifier generated at startup. Note that this identifier is not client_id, which is the standard unit for aggregation for most data analysis in Firefox.

Desktop experiments use the normandy_id, a unique stable identifier generated by the ClientEnvironment module during first run and stored in a preference (see implementation). It differs from client_id in that it is not exposed to Telemetry and it is not synced across profiles / accounts.

Mobile experiments use the nimbus_id, a unique identifier generated by the Nimbus client during first run stored in the experiments database (see implementation).

Experiment assignment​

In order to randomize clients into experiments, we take a SHA-256 hash of the namespace and the randomization_unit truncated to 12 characters and check if that falls between the bucket range configured in the experiment.

Consider this example:

{
  "slug": "experiment-B",
  "bucketConfig": {
    "start": 3000,
    "count": 2000,
    "total": 10000,
    "namespace": "rutabaga",
    "randomizationUnit": "normandy_id"
  }
}

A client will be bucketed into the experiment if the input hash falls in the range 3000 to 4999:

                  start
                  |     hash
                  v     v
  [0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000]
                              ^
                              end

Namespace rollovers​

When a namespace is fully consumed (i.e., an experiment requests a bucket range beyond 9999), the namespace "rolls over". The namespace ID is updated from something like <application>-<feature>-<channel>-1 to <application>-<feature>-<channel>-2 and the clients are effectively rehashed to form 10000 new buckets. When an experiment launches on this new namespace, some fraction of the requested client range is already enrolled into an experiment (unlike a non-rollover situation where the requested client range is guaranteed to yield available clients) and so experiments will under-enroll their desired amount.

As an example, suppose that 75% of the namespace has been consumed by experiments that have ended and that 20% of the namespace is consumed by an active experiment called A (i.e., A is live with a bucketConfig of {"start":7500, "end":9500", "total":10000}). Suppose further that another 20% experiment called B is launched. In that situation, the namespace will rollover and buckets 0000 to 2000 of the new namespace will be allocated to B. Some fraction of B's clients (approximately 20%) will still be enrolled in A, so even though they meet the enrollment criteria, they will not enroll. Thus, B will actually enroll 16% of the available userspace, even though it targeted 20%.

In practices, experiments can run indefinitely and namespaces can rollover indefinitely so one must account for any running experiment on any prior iteration of the namespace, not just the previous iteration. That said, the impact of this effect is moderated by retention (or lack thereof) in that a 1 year old experiment using 20% of the clients will only block some fraction less, maybe 10%, because clients have churned and many of the clients in that experiment have dropped while new clients have joined after that experiment enrolled. Further, the impact of this effect depends on the targeting itself. For example, experiments targeting new clients do not suffer from this problem (all clients who meet the targeting are guaranteed to not be in any previous experiments because they are new) while experiments that target the same client subset over and over are most impacted.

Branch assignment​

Assuming a client has satisfied all targeting conditions and bucketed into an experiment, we will randomly assign a branch. Unlike experiments, branches cannot specify targeting conditions, and hashes are re-randomized for every experiment. We do this by:

  1. Assigning buckets equal to the ratios specified in each branch
  2. Taking a SHA-256 hash of the randomization unit and the experiment identifier (which is unique per experiment)
  3. Checking which range the input hash falls into

For example, given the following branch ratios:

{
  "slug": "experiment-123",
  "branches": [
    { "slug": "a", "ratio": 2 },
    { "slug": "b", "ratio": 5 },
    { "slug": "c", "ratio": 3 }
  ]
}

We will assign 20% of the buckets to branch a, 50% to b, and 30% to c. We take a hash of the client's normandy_id and the experiment slug (experiment-123) and see which bucket range it falls into:

                     hash
                     v
  [a, a, b, b, b, b, b, c, c, c]

Controlling interactions​

By default, all experiments are allowed to interact and clients can bucket into multiple experiments simultaneously. However, sometimes we do want experiments to be exclusive, such as when they change the same set of variables.

In practice, we have three methods of preventing interactions between experiments:

Bucket range exclusion​

Experiments that configure the same namespace will bucket identically for the same user identifier. This means we can exclude experiments by giving the same namespace and have them specify non-interacting ranges (start / count).

Consider two experiments with the following configurations:

{
  "slug": "experiment-A",
  "bucketConfig": {
    "start": 0,
    "count": 3000,
    "total": 10000,
    "namespace": "rutabaga",
    "randomizationUnit": "normandy_id"
  }
},
{
  "slug": "experiment-B",
  "bucketConfig": {
    "start": 3000,
    "count": 2000,
    "total": 10000,
    "namespace": "rutabaga",
    "randomizationUnit": "normandy_id"
  }
}

Say we generate a value of 4562 from our hash on a given client. The client is bucketed into experiment-B because this falls in the the range for this experiment (which is 3000 to 4999).

Note that we always re-randomize branch assignment, so we can't isolate based on branch.

Client-side rules​

In Nimbus, clients are prevented from enrolling into two experiments that target the same feature with a simple check during enrollment. For example, a user cannot be enrolled in two experiments that change the aboutwelcome feature.

In Normandy, clients are prevented from enrolling in two experiments that change the same preference.

Targeting exclusion​

For specific experiments that should be excluded from others, a targeting expression can be included with a specific experiment identifier:

{
  "targeting": "!activeExperiments['some-experiment']"
}
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/capabilities/index.html b/capabilities/index.html index cd29327f5..09fec0254 100644 --- a/capabilities/index.html +++ b/capabilities/index.html @@ -4,13 +4,13 @@ Nimbus Features | Experimenter Docs - +

Nimbus Features

Nimbus is a full-featured experimentation platform that provides configuration, analysis and client libraries for both experiments and rollouts.

Experimenter Console​

Multiple language Client integrations​

  • Android (Kotlin)
  • iOS (Swift)
  • Firefox Desktop Frontend (JS)
  • Firefox Platform (C++)

Client-side functionality​

  • Consumer opt-out
  • Integration with console preview testing
  • View experiments (about:studies)

Current client integrations​

  • Firefox Desktop
    • Frontend via JS SDK
    • Platform
    • Windows installer
  • Firefox Mobile via Nimbus-SDK Rust Component
    • Fenix
    • Firefox iOS
    • Focus Andriod
    • Focus iOS

Current feature support​

Requesting feature support​

If you aren't sure we have what you need, pop into #ask-experimenter with your questions or file an issue

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/client-sdk-states-and-lifecycle/index.html b/client-sdk-states-and-lifecycle/index.html index 552d2790e..92d020b39 100644 --- a/client-sdk-states-and-lifecycle/index.html +++ b/client-sdk-states-and-lifecycle/index.html @@ -4,7 +4,7 @@ Client SDK States & Lifecycle | Experimenter Docs - + @@ -22,7 +22,7 @@ it can annotate any custom pings that may be sent out early during app launch. The setExperimentInactive only needs to be called if we have called setExperimentActive for that experiment in the same app run.

Nimbus will also emit Glean events on key experiment state transitions:

  • An "enrollment" event, when an experiment enters the Enrolled state.
    • Event field "experiment" records the experiment slug.
    • Event field "branch" records the branch into which the client enrolled.
    • Event field "enrollmentId" contains a randomly-generated identifier for this enrollment.
  • A "disqualification" event, when an experiment enters the Disqualified state.
    • Event field "experiment" records the experiment slug.
    • Event field "branch" records the branch into which the client enrolled.
    • Event field "enrollmentId" contains the enrollment id from the corresponding "enrollment" event.
    • Event field "reason" containing the reason for disqualification, as one of the following values:
      • "optout"
      • "targeting"
  • An "unenrollment" event, when an experiment enters the WasEnrolled state.
    • Event field "experiment" records the experiment slug.
    • Event field "branch" records the branch into which the client enrolled.
    • Event field "enrollmentId" contains the enrollment id from the corresponding "enrollment" event.

Nimbus will also emit Glean events when the client code calls experiment-related APIs:

  • An "exposure" event, when client code calls activateExperiment or isFeatureEnabled for the first time, and the experiment is in Enrolled state.
    • Event field "experiment" records the experiment slug.
    • Event field "branch" records the branch into which the client enrolled.
    • Event field "enrollmentId" contains a randomly-generated identifier for this enrollment.

Client Behaviours​

When asked if an experiment is active​

  • Get the current client state and last-seen server state for the named experiment.
  • If the client state is Enrolled, return true.
  • Otherwise, return false.

When asked for the current branch of an experiment​

  • Get the current client state and last-seen server state for the named experiment.
  • If the client state is Enrolled, return the enrolled branch and its config.
  • Otherwise, return None.

When asked for the current config of a feature​

  • Get the current client state and last-seen server state for all experiments.
  • Discard any experiments whose client state is not Enrolled.
  • For each remaining experiment:
    • Get the branch config for the Enrolled branch.
    • If it does not contain config for the named feature, discard the experiment
  • If more than one experiment remains, TODO this is an error right? report it somehow.
  • If exactly one experiment remains, return the feature config from its Enrolled branch.
  • Otherwise, return None.

When a server update transitions an experiment to "Enrolling"​

  • If the current client state for the experiment is Unknown or NotEnrolled:
    • Get the current state for all known experiments that conflict with this one.
    • Evaluate targeting/bucketing/etc, if it says to enroll, set client state to Enrolled.
      • TODO: trigger a notification in the client app somehow, to redraw UI etc?
    • Otherwise, set client state to NotEnrolled.
  • If the current client state for the experiment is Enrolled:
    • TODO: what exactly? How does an existing enrollment change in response to updated experiment config?
  • Otherwise, set the current client state for the experiment to Errored and log some diagnostic telemetry.

When a server update transitions an experiment to "Paused" or "Running"​

  • If the current client state for the experiment is Enrolled:
    • TODO: trigger a notification in the client app somehow, to redraw UI etc?

When a server update transitions an experiment to "Cancelled" or "Completed"​

  • If the current client state for the experiment is Enrolled:
    • TODO: trigger a notification in the client app somehow, to redraw UI etc?
    • Set client state to WasEnrolled.
  • Otherwise, discard the experiment state.

During some periodic cleanup process​

  • For each experiment in the WasEnrolled state:
    • If it has been more than N days since we entered that state, discard the experiment state.

References​

Changelog​

2021-04-13 (dmose):​

  • Moved from Google Doc, converted to MDX format, and commited to DocsHub.

2020-11-18 (rfkelly):​

  • Renamed "activation" to "exposure" in line with terminology discussion from Nimbus Architecture Group meeting.

2020-11-13 (rfkelly):​

  • Renamed "disabled" state to "disqualified"

2020-11-11 (rfkelly):​

  • Added the "disabled" state to client states.
  • Added the "telemetry" section.

2020-10-30 (rfkelly):​

  • Added the key concept of "disablement" as distinct from unenrollment.
  • Simplified server states by:
    • combining "drafting + pending" into "preparing".
      • also removed hypothetical logic around handling of "startDate" in order to start a pending experiment; it starts when visible in Remote Settings.
    • combining "completed + cancelled" into "ended".
    • removed "paused" since it doesn't sound like we have such a state that's distinct from "running".
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/configuring/index.html b/configuring/index.html index 86889738d..ed04f04a9 100644 --- a/configuring/index.html +++ b/configuring/index.html @@ -4,13 +4,13 @@ Experiment Configuration | Experimenter Docs - +

Experiment Configuration

From our perspective as experiment owners, experiments are composed of two time periods: the enrollment period and the observation window.

The Enrollment Period​

The enrollment period is the time during which the experiment recipe is live. Clients that sync during this time will see the recipe, and potentially enroll (see this page on bucketing for more details on the enrollment mechanism).

danger

Mobile first run experiments are a very important-to-know exception to the enrollment period design.

Please consult the mobile first run experiments deep dive for more information.

General considerations​

There are a few things that experiment owners should consider when choosing the length of the enrollment period.

  • Day-of-week bias. Different types of users use the browser on different days of the week. In order to ensure that the experiment results generalize to the complete population, it's recommended to enroll for units of whole weeks (7 days, 14 days, etc). Failing to do so could lead to results that are overly weighted by one type of user (weekday users, weekend users) such than the actual impact when we launch the treatment to all users might be quite different than the experiment results.
  • Partial filling on the first day. Launching experiments mid-day results in one day of partial enrollments, which if not accounted for can mean that the experiment was smaller than intended. Therefore, we generally recommend enrolling for an extra day to account for this.

The exact choice of period length should consider the total volume of the experiment, the audience (namely, new vs existing user), and the business needs.

  • Total volume: without increasing the enrollment proportion, larger experiments will need to enroll longer. Of course, in some situations we can increase the enrollment proportion to achieve a shorter enrollment period.
  • Audience:
    • Existing user experiments: generally, these experiments see the majority of their enrollments quickly, with enrollment volume rapidly falling off as only users who haven't synced are eligible to continue enrolling. It's not uncommon with an experiment like this to see 90% of the enrollments within the first week and 10% in the later weeks. In this case, it may not be possible to extend the enrollment period to achieve larger sizes so the only option would be increasing the enrollment fraction.
    • New user experiments: generally, these experiments see enrollment volume scaling linearly with period length. Letting these experiments enroll for 2 weeks will see double the enrollments of a 1-week variant.
  • Business needs: varying business needs can necessitate varying designs.

Recommendations​

Putting this together, we generally recommend the following possible lengths for enrollment periods:

  • 8 days
  • 15 days
  • 22 days
  • etc.

Getting help​

Please bring questions about enrollment period and sizing either to Data Science Office Hours or to #ask-experimenter

The Observation Window​

Once a client has enrolled in an experiment, their Observation Window has begun, during which time we can monitor their telemetry and compute metrics. The windows is usually expressed as the number of days since enrollment and is the same for all clients, regardless of when they enroll.

The observation window is an abstraction that represents the longest period of time we can observe any individual client for and can be calculated by differencing the enrollment period from the overall runtime for the experiment (observation window = total runtime - enrollment period).

Relationship with unenrollment and the end of the experiment.​

Clients can unenroll from experiments during the middle of the observation window for a variety of reasons (they've disabled Studies, they no longer meet the targeting, opt-ing out of that specific experiment, etc.). To avoid bias, results are calculated over all clients that ever enrolled (unenrolling from the experiment does not remove a client from the analysis). The only situation that may exclude a client from analysis would be opt-ing out of telemetry.

Ending the experiment in Experimenter finalizes the total runtime of the experiment and enables us to determine how long the observation window can be. As a result, for most clients, the observation window ends before they're actually unenrolled from the experiment.

Visual explanation​

Enrollment &amp; Observation period workflow

The above visual explains how the system works for a hypothetical experiment that launched on 9/1, was ended on 9/25 (lasting a total of 24 days), and had an enrollment period of 8 days. This results in an observation window of 24-8=16 days.

  • For clients enrolling on day 1 (9/1), their observation window runs from 9/2 through 9/17.
  • For clients enrolling on day 2 (9/2), their observation windows runs from 9/3 through 9/18.
  • ...
  • For clients enrolling on day 8 (9/8), their observation windows runs from 9/9 through 9/24.

Relationship with Analysis Windows​

All experiment metrics are calculated over an Analysis Window which is a subset of the Observation Window. Analysis windows can be at a daily, weekly, or overall level. In this example, there are:

  • 1 overall window, representing the 16 days after enrollment.
  • 2 weekly windows. The first week (indexed as 0 in Jetstream and Partybal) is shown in magenta and the second (1st) week is shown in orange. There wasn't enough days to form a 3rd complete week, so metrics can only be calculated over weeks 0 or 1.
  • 16 daily windows.

Getting help​

Please bring questions about observation windows and analysis to Data Science Office Hours or to #ask-experimenter

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/contributing/index.html b/contributing/index.html index 891dbad8b..6a87adb41 100644 --- a/contributing/index.html +++ b/contributing/index.html @@ -4,13 +4,13 @@ Contributing | Experimenter Docs - +

Contributing

The Experimenter documentation hub is managed in the mozilla/experimenter-docs repository. You will need a GitHub account to contribute, and if you are not already in the Project Nimbus GitHub team, you may need to request write access in the #nimbus-project Slack channel.

info

Please file issues for this repository in Experimenter and add the experimenter-docs label.

This page will walk you through how to edit an existing document or create a new one, how to display docs in the sidebar where desired, and how to make these changes in GitHub. See the in-page header navigation bar on the right-hand side of this doc for handy quicklinks.

Documents are written in Markdown (.md) files using GitHub-flavored Markdown syntax and are compiled into static web pages. MDX is also supported, meaning you may embed React components in content as needed. Additionally, Docusaurus offers admonitions which can be useful when placing emphasis on pieces of documentation. Refer to Docusaurus Markdown features for more technical information about these Markdown pages.

If you need custom CSS styles, you can edit the src/css/custom.css file. Try to use classes when writing MDX with names that will be easy to reuse later.

Configuring Your Notifications​

If you would like to subscribe to notifications for this repository, including for when someone requests doc changes with a pull request that you can potentially review and approve, be sure to "Watch" the repository by clicking on the repository's "Notification settings" menu and selecting "All Activity."

Also, see configuring notifications in GitHub.

Alternatively, consider subscribing to this repository in the GitHub Slack app by searching for it under "Apps" inside Slack.

GitHub Watch

GitHub Workflow​

All documentation edits and additions will require a pull request into the main branch because merged changes will trigger an automatic deployment, but fear not if you don't prefer pulling down repositories locally and editing files - you probably don't need to.1

Editing in the GitHub UI​

If you aren't familiar with using Git via the command line, we recommend using the GitHub user interface to make changes to docs. See the GitHub UI Docs if you need further details than what's provided here.

In the following sections, you'll find corresponding links to the GH UI directly under each section title. If you need to edit a file, you'll click on the "Edit" button:

edit this file

You'll be brought to the GitHub editor page for that file. If you're creating a new document, you'll see a similar editor.

When you're satisfied with your changes or new document, at the bottom of the page, enter a commit message and optional description. Be sure to have the option "Create a new branch" selected, the name of which can be anything, but if you also need to make edits to the sidebar or add images, you'll want to keep the branch name in mind.

Commit changes

Click Commit changes and you'll be brought to a pull request view comparing your new branch to main. If you don't need to make updates to the sidebar, see the Pull Request Workflow section and click Create pull request.

Doc Edits + Sidebar Edits​

If you also need to make updates to the sidebar, you will follow the GH UI link under "Adding or Modifying Sidebar Links" but replace "main" in the URL with the name of your branch. For example, if the name of your branch is my-flying-nimbus, this would be the link you'd use to edit that file. Similarly to the instructions above, after you've made the desire changes, enter a commit message and optional description. However, this time, you will select the option commit directly to the my-flying-nimbus branch instead of creating a new branch.

After committing your changes, you'll be brought back to a pull request view comparing your new branch to main. You may need to edit the PR title, but if your files changed look good, click Create pull request and see the Pull Request Workflow section.

Pull Request Workflow​

PR Template​

After clicking Create pull request, you will be presented with a pull request template specific to this repository.

Edit the line Closes: mozilla/experimenter#0000 where experimenter may need to be changed to reflect the repository where the GitHub issue lives and where 0000 should be changed to reflect the issue number. Delete this section if merging your pull request won't close an issue.

Under "Permission Checklist," check the boxes applicable to your pull request that would make this process easier for you. If you have any specific requests regarding the checkboxes or anything else, please note them in the pull request.

Reviewing and Merging​

To make sure your pull request is reviewed, either request a specific reviewer to look at your PR, request the mozilla/project-nimbus-team, or ask the team in the #nimbus-project Slack channel to take a look. Once you've created the pull request, automatic checks will run to ensure the project can build and deploy successfully with your changes, and at least one reviewer will need to approve your PR.

Documentation is community property. Our norms for this repo are that it's fine for a reviewer to commit small adjustments directly to the branch including typo fixes, Markdown/MDX improvements, title or sidebar (ID) suggestions, etc. The reviewer may leave comments instead, at their option. Reviewers may also merge changes after approving them. These practices are different from our usual assumptions about code and are intended to reflect the lower risk of changing documentation. Reviewers should use their judgement and seek feedback from the original writer before making major changes.

You may receive feedback or questions on your PR that you can address through editing the file in question locally or in the GH UI. When your PR is approved, you should go back to merge your own pull request in case you may want to make another edit before the pull request is merged or because you may get a non-blocking suggestion. If that happens, you can make the change and then merge the PR or simply merge the PR if you disagree with the comment.

Once your PR is merged, you should see your changes reflected at the URL this hub is housed at in about 10 minutes.

Create a New Document​

note

👉 Click here to this in the GitHub UI 👈

Let's say you want to create a new document called "My Flying Nimbus" that details how to acquire and guide your very own magical, yellow cloud.

In the docs/ directory, either locally on your machine or through the GitHub UI link above, create your file, my-flying-nimbus.md. The name can be whatever you want, but it's a good general rule of thumb to keep the name similar to whatever the doc title will be.

Paste the following doc metadata, or frontmatter as it's referred to in Docusaurus, at the top of the file:

---
id: my-flying-nimbus
title: My Flying Nimbus
slug: /my-flying-nimbus
---

With this at the top of the MD file, Docusaurus will automatically make your document available for viewing at the slug /my-flying-nimbus. Write your document in MD/X and once you're satisfied with your new document, commit and push your changes to your branch or follow the instructions in the GitHub UI section to commit your changes and put in your pull request.

Modify an Existing Document​

note

👉 Click here to this in the GitHub UI 👈

Editing an existing doc is easy peasy. Click on the link above and choose the document you want to modify, or alternatively, find the "Edit this page" link at the bottom of any document inside the Doc Hub, and follow the instructions in the GitHub UI section.

Modifying the title property at the top of the document will update what the sidebar link displays.

Note that if you change the ID also found at the top of the document, you will also need to update the matching string in sidebars.js (it's case-sensitive). See the "Adding or Modifying Sidebar Links" section for more details as well as Doc Edits + Sidebar Edits in the GH UI.

Adding Images​

note

👉 Click here to this in the GitHub UI 👈

Images for your documentation should be located at static/img/your-doc-name/image-name.png. To upload these in the GH UI, change my-flying-nimbus in the link given above to the name of your documentation which will reflect a new directory that holds the images for your doc. When you've finished uploading your images, follow the instructions in Editing in the GH UI to commit this to a new branch or existing branch you've already committed documentation edits to. (Tip: if you've already made a doc change and use this link to commit image changes, make sure "main" in the URL is updated to your branch name!)

Once you know the name of your image, you can insert the following in your documentation...

my-flying-nimbus.md
<img
  src="/experimenter-docs/img/my-flying-nimbus/image-name.png"
  alt="changeme, alt text describing the image"
  className="img-lg"
/>

...where my-flying-nimbus is the name of the directory (and doc name) and image-name is the name of the image.

Unfortunately you won't be able to preview the image in the GitHub UI or custom styles you add to it. To do this, you will have to follow the repo front-page instructions to run the project locally. This is recommended if you need to right-align or left-align an image, but if you just need to display an image at full-width, you can optionally check the box in the pull request template asking for the reviewer to check into your branch and look at it for you.

You can add a className to the image, like img-lg, to adjust the size. Click here to see our custom CSS file and edit it in the GH UI here.

note

👉 Click here to this in the GitHub UI 👈

If you'd like to add a new external link, link to a new document, or modify an existing link in the sidebar, you have a few options.

Note that the id references in this section are case-sensitive. Also, if you'd like more details than what's provided here, you can check out the Docusaurus Docs on sidebars.

info

Tip: If you just need to update the sidebar label and not the page title (atypical), just add sidebar_label: Nimbuses Are Sweet to the frontmatter section at the top of the MD file.

  1. Create a new top-level internal link to the doc or modify an existing one
top-level link

To set this up, you'll have to modify the sidebars.js file locally or through the GH UI link above - all it needs is the id that is set at the top of the MD file. It will look something like this to reflect the screenshot:

sidebars.js
module.exports = {
  sidebar: [
    "welcome",
    "my-flying-nimbus", // this doc ID is all you need to add!
    ... // other unrelated sidebar items
  1. Create a new top-level external link to the doc
top-level external link

If you'd like to add a new top-level external link, sidebars.js requires an object expecting type, label, and href that will look something like this to reflect the screenshot:

sidebars.js
module.exports = {
  sidebar: [
     "welcome",
   {
     type: "link",
     label: "My Flying Nimbus",
     href: "https://magicalyellowcloud.com",
   },
   ... // other unrelated sidebar items

You'll want to make sure type is set to "link".

  1. Create a new internal link to the doc in an existing or new category
category link

In Docusaurusland, sidebar menu items with a down caret are called "categories."

To add your internal page to an existing category, find the category via the label item in sidebars.js which will match the category text you see displayed in the sidebar (see code below). Find the items array for that category and add the doc ID you wish to link internally to.

To add your internal page to a new category, copy and paste the entire object shown below where you'd like it to show up in the sidebar. Edit your label as desired and change items to items: ["my-flying-nimbus"] with your doc ID.

sidebars.js
module.exports = {
  sidebar: [
     ... // other unrelated sidebar items
    {
      type: "category",
      label: "Experiment Owners", // what you're looking for, or creating
      items: ["experiment-owners-root", "my-flying-nimbus"], // doc ID is in this array
    },
    ... // other unrelated sidebar items
  1. Create a new external link to a doc in an existing or new category
category link

The code changes for this option essentially combine options 2 and 3.

To add your external link to an existing category, find the category via the label item in sidebars.js which will match the category text you see displayed in the sidebar (see code below). Find the items array for that category and add the object shown below (also shown in option 2) to the array.

To add your external link to a new category, copy and paste the entire object shown below where you'd like it to show up in the sidebar. Edit your top-level label as desired, remove "experiment-owners-root", from the items array, and update the label you'd like to display for the link.

sidebars.js
module.exports = {
  sidebar: [
     ... // other unrelated sidebar items
    {
      type: "category",
      label: "Experiment Owners", // what you're looking for, or creating
      items: ["experiment-owners-root",
        {
          type: "link",
          label: "My Flying Nimbus",
          href:
            "https://magicalyellowcloud.com",,
        },
      }],
    },
    ... // other unrelated sidebar items
  1. If you'd like to preview your sidebar changes or if you need to preview how a document containing MDX will build, you'll likely want to check into your branch and run yarn start to see the changes. Please reach out to the Experimenter team if you need help.↩
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/cookbook/fml/fml-cookbook/index.html b/cookbook/fml/fml-cookbook/index.html index 55f61467b..cd0a02767 100644 --- a/cookbook/fml/fml-cookbook/index.html +++ b/cookbook/fml/fml-cookbook/index.html @@ -4,13 +4,13 @@ Feature Manifest Cookbook | Experimenter Docs - +

Feature Manifest Cookbook

The Feature Manifest Language is a tool and specification designed to help application developers manage tooling to interact with Nimbus. It sits on top of the Feature Variables API, and eliminates many of the weaknesses of that API:

  • No magic strings: application code now accesses configuration through generated data classes.
  • Default values are specified away from the code.
  • Channel specific configuration.
  • Decreasing interaction with experimenter for application developers. Experimenter picks up changes in the manifest each day.

This is the start of a collection of recipes and patterns that we're discovering.

  1. Managing collections not known before release
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/cookbook/fml/growable-collections/index.html b/cookbook/fml/growable-collections/index.html index b7bb08ffb..c318b2025 100644 --- a/cookbook/fml/growable-collections/index.html +++ b/cookbook/fml/growable-collections/index.html @@ -4,7 +4,7 @@ Managing collections not known before release | Experimenter Docs - + @@ -13,7 +13,7 @@ and scalars and lists are replaced, and null causes a deletion.

We'll continue our example to illustrate this in more detail:

In the release population, the default JSON for the the theming-feature patched on to the minimal configuration above:

{
    "asset-urls": {
        "default-theme": "https://www.mozilla.com/assets/wp-default/600/900"
    }
}

This came from a channel specific default within the manifest itself.

Some of the release population may be under experiment. An experiment branch sets up the feature thus:

{
    "asset-urls": {
        "protocol-theme": "https://www.mozilla.com/assets/wp-protocol/600/900"
    }
}

At the same time, another experiment may have just terminated, and a branch declared the winner. The experiment owner has decided to promote this branch to the whole population as a rollout.

{
    "asset-urls": {
        "ufi-theme": "https://www.mozilla.com/assets/wp-ufi/600/900"
    }
}

So the final configuration that the app receives for the feature is a merging of all three:

{
    "asset-urls": {
        "default-theme": "https://www.mozilla.com/assets/wp-default/600/900",
        "protocol-theme": "https://www.mozilla.com/assets/wp-protocol/600/900",
        "ufi-theme": "https://www.mozilla.com/assets/wp-ufi/600/900"
    }
}

As long as the keys are unique, the collection will grow each time a rollout or experiment affects the feature.

Finally, successful rollouts are likely going to be persisted: now we have learned a particular asset performs well, we should make it part of the next release.

On the next release, rollouts of successful assets would likely be folded back into the manifest itself, and so the manifest becomes the repository of successful assets.

As time goes by, we have a growing collection of asset-urls, without needing to change the code at all.

This is a very powerful pattern which is used in multiple places, so we'll name this pattern "Growable Collections".

Exposure events​

Features with a growable collection of things may need to give some care about exposure events.

Recall: exposure events should be sent when the user is exposed to the treatment.

If we wish to experiment with a particular asset, the application feature should detect which asset being shown, and then only record an exposure only when that asset is being shown. We can do this by adding an extra variable into the feature.

      asset-under-experiment:
        description: The key into the asset urls map of the asset we wish to test.
        type: Option<AssetName>
        default: null

This allows us to be specific in the experiment payload that the protocol-theme is the asset we wish to experiment with.

{
    "asset-urls": {
        "protocol-theme": "https://www.mozilla.com/assets/wp-protocol/600/900"
    },
    "asset-under-experiment": "protocol-theme"
}

In the application code, we check to see if the asset being displayed is the one we're interested in, and only then record an exposure.

let config = MyNimbus.features.themingFeature.value()
let key = selectKey(from: themingFeature.assetMap)

if key == config.assetUnderExperiment {
    MyNimbus.features.themingFeature.recordExposure()
}

displayAsset(url: config.assetMap[key])
val config = MyNimbus.features.themingFeature.value()
val key = selectKey(from: themingFeature.assetMap)

if (key == config.assetUnderExperiment) {
    MyNimbus.features.themingFeature.recordExposure()
}

displayAsset(config.assetMap[key])

Local development​

Channel specific defaults allow us to specify a list of assets prepopulated with placeholders. The defaults for the debug channel for this feature are derived directly from manifest.

{
    "assets-urls": {
        "kittens": "https://placekitten.com/600/900",
        "bill-murray": "https://www.fillmurray.com/600/900",
        "flickr": "https://loremflickr.com/600/900"
    }
}

Local development can then proceed with these placeholders, while other channels do not.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/experimenter/branches-page/index.html b/deep-dives/experimenter/branches-page/index.html index af57a2ff0..ea5bf2c80 100644 --- a/deep-dives/experimenter/branches-page/index.html +++ b/deep-dives/experimenter/branches-page/index.html @@ -4,14 +4,14 @@ Branches | Experimenter Docs - +

Branches

Feature Manifest Language errors (mobile only)​

tip

Want more info on the Feature Manifest Language? Check out the documentation here

For non-desktop applications, the Feature Manifest Language (FML) is used to surface errors when we are configuring our branches. This is particularly helpful for deeply nested features, which can be difficult to configure on the Branches page.

You can see that the save button validation has given us usable FML errors, including the line/col number that the error occurs at:

The FML generates errors based on the feature you are using, the application, the versions of the application that you are targeting, and the channel. Changing the channel on your experiment may result in different FML errors, since there may be differences between the feature manifests for each version and channel.

Desktop​

When configuring your branches for a desktop experiment, you will see autocomplete as you type, as well as JSON linting. FML errors are not present on desktop, since desktop does not use the Feature Manifest Language.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/experimenter/experimenter-state/index.html b/deep-dives/experimenter/experimenter-state/index.html index 8b31f8ddc..ec18575b9 100644 --- a/deep-dives/experimenter/experimenter-state/index.html +++ b/deep-dives/experimenter/experimenter-state/index.html @@ -4,13 +4,13 @@ Experimenter state and Kinto integration | Experimenter Docs - +

Experimenter state and Kinto integration

Learn more about experiment states, lifecycles, and workflows in Experimenter here!

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/experimenter/rollouts/index.html b/deep-dives/experimenter/rollouts/index.html index 3bdd5163c..7fcd9dc2c 100644 --- a/deep-dives/experimenter/rollouts/index.html +++ b/deep-dives/experimenter/rollouts/index.html @@ -4,7 +4,7 @@ Rollouts | Experimenter Docs - + @@ -12,7 +12,7 @@

Rollouts

tip

Want more info on rollouts? Reach out to us in #ask-experimenter on Slack.

Rollouts are single-branch experiments that differ from a traditional experiment in a number of ways:

  • A rollout only has a single branch.
  • A client can be enrolled in both a single experiment AND rollout for a given feature.
  • The experiment feature value takes precedence over the rollout feature value.
  • Rollouts use a separate bucketing namespace from experiments so you don't need to worry about the populations colliding.

What is a rollout?​

When should I use a rollout instead of an experiment?​

  • Launching a winning branch of an experiment faster than the trains.
  • Launching a configuration to non-experiment users during an experiment after a short period of verification.
  • Configuring different settings for a feature for different audiences remotely.
  • A "kill switch" if you want to launch a feature but then turn it off if something goes wrong.

When should I not use a rollout?​

If the feature has not yet been extensively tested, isn't production quality, or needs a period of validation on the trains.

Can I run a Nimbus experiment and a rollout simultaneously?​

It's possible, but bear in mind that rollouts are not measurement instruments. Experiments are.

If you have uncertainty about the effect of the feature, you may wish to be guided by experiment results instead of deploying the feature immediately.

Before you do this, you should consider:

  • Future experimentation needs
    • Once you deploy the feature to someone, you lose the ability to observe what happens when you introduce that feature to that user.
    • Consider whether you have a need for holdbacks.
  • Decision criteria
    • Identify the risks you're trying to mitigate with a rollout and decide whether you need multiple stages or not.
    • If you have multiple stages, how will you know whether to advance or roll back?
    • What signals will help you make your decision? Where will they come from?
    • If you are relying on the experiment to guide you, make sure that the timelines are compatible.
    • Consult data science before relying on signals derived from the behavior of the rollout group, since rollouts are not measurement tools and lack a control.

You would need to:

  1. Launch an experiment that targets a fixed portion of the population (sized appropriately for whatever you are trying to measure)
  2. When you are ready, launch a rollout using the steps below at a low percentage of the population
  3. As the rollout proceeds, consult your decision criteria. Change the percentage of the rollout by editing the population percentage.

Keep in mind that if you do plan to release the experience to 100% of users, you should make sure it meets production quality standards.

Are there typical rollout patterns I should follow?​

This document covers common patterns for people using a rollout to mitigate risk around technical issues, brand perception, and load capacity/service scaling.

How do I create a rollout in Experimenter?​

There are two ways to make a rollout from the Experimenter UI. When I have a “winning branch” from an in-flight experiment, I want to easily set some percentage of the non-experiment population to that feature configuration.

  1. To create a rollout from an existing experiment, a branch can be cloned and a new rollout created (similar to the way "Clone" works for experiments). This is done using the "Promote to Rollout" buttons on the Summary page of an experiment:

  1. A rollout can also be manually created (without cloning) through the "Create new" button on the Experimenter home screen. To mark the new item as a rollout, check the "This is a rollout" box on the "Branches" page:

Incrementing your rollout's population percentage​

Live editability​

The population percent of a rollout ("Percentage of clients" on the Audience page) can be edited once a rollout is launched. To make changes to this field, open your rollout, and from the left sidebar click on the Audience page. From there, you will be able to make edits and save.

Once an edit has been made, you will see a "Request update" button on the summary page under the available Actions:

Once you request an update, you will follow the same review flow as launching an experiment (approval on Experimenter, and approval on Remote Settings).

Multiple edits can be made to your rollout without needing to request an update, and you will be able to see if there are unpublished changes by the red "Unpublished changes" status pill that is located by "Timeline":

Without using live editability​

For each segment of the population that you are targeting, you will create a new rollout. For example, say that you have created a rollout entitled "Important spotlight rollout" and set the population percentage to 1%. When you increase the population percentage, you don't want the users who are already enrolled in "Important spotlight rollout" to be unenrolled when the population is increased (see Bucketing for more info on this topic).

Therefore, when it is time to increase your population percent, clone your existing rollout (tip: pick a helpful title such as "Important spotlight rollout - Phase 2") and set your population percentage to target your next segment of users. If you want to "increase" the population from 1% to 5%, this new "Phase 2" rollout will have a population percent equal to 4%.

Where can I find rollouts?​

This can be done using the filters on the Experimenter home page. You can either sort by feature to see all experiment/rollouts for said feature, or you can search by experiment type to filter by "experiment" or "rollout":

Supported platforms and minimum version targeting​

Experimenter currently supports the following platforms:

  • Desktop
  • Fenix (Firefox for Android)
  • Focus Android
  • Firefox iOS
  • Focus iOS

The minimum version supported for rollouts on all platforms (listed above) is currently 105. See Experimenter for more details.

Automated analysis​

  • The dashboards for rollouts that OpMon generates follow this URL pattern:
    https://mozilla.cloud.looker.com/dashboards/operational_monitoring::<slug with underscores>
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/adding-a-platform/index.html b/deep-dives/jetstream/adding-a-platform/index.html index 475e5154e..9d0d027c7 100644 --- a/deep-dives/jetstream/adding-a-platform/index.html +++ b/deep-dives/jetstream/adding-a-platform/index.html @@ -4,13 +4,13 @@ Adding a Platform | Experimenter Docs - +

Adding a Platform

Jetstream runs analyses for experiments launched on several different platforms, such as Fenix or Firefox Desktop. When adding a new platform in Experimenter, the new platform also needs to be configured in Jetstream to enable automated analyses for launched experiments.

Add support for platform in jetstream​

An example of desktop configuration

[platform.firefox_desktop]
enrollments_query_type = "normandy"
app_id = "firefox-desktop"

Configuration breakdown​

  • [platform.platform_name] - Specify platform name
  • enrollments_query_type - whether enrollments should be determined based on Glean events (glean-event) data or Normandy data (normandy) (default: glean-event)
  • app_id - application ID as defined in probe-scraper repository.yaml
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/configuration/index.html b/deep-dives/jetstream/configuration/index.html index 04047621b..674038913 100644 --- a/deep-dives/jetstream/configuration/index.html +++ b/deep-dives/jetstream/configuration/index.html @@ -4,7 +4,7 @@ Configuring Jetstream | Experimenter Docs - + @@ -49,7 +49,7 @@ Outcome snippets look, for example, like:

friendly_name = 'Example config'
description = 'Example outcome snippet'

# parameters definition (Optional)
[parameters.search_engine]
friendly_name = "Search engine"
description = "Search engine we want to track"
default = "google"  # this will be the default value if not overwritten in an external config
distinct_by_branch = false  # if set to true, value provided in config needs to specify value and corresponding id.
[metrics.total_amazon_search_count]
select_expression = "SUM(CASE WHEN engine like 'amazon%' then sap else 0 end)"
data_source = "search_clients_engines_sources_daily"
[metrics.total_amazon_search_count.statistics.bootstrap_mean]
[metrics.total_amazon_search_count.statistics.deciles]

[metrics.urlbar_amazon_search_count]
select_expression = """
SUM(CASE
        WHEN source = 'alias' AND engine LIKE 'amazon%' THEN sap
        WHEN source = 'urlbar' AND engine LIKE 'amazon%' THEN sap
        WHEN source = 'urlbar-searchmode' AND engine LIKE 'amazon%' THEN sap
        else 0 end)"""
data_source = "search_clients_engines_sources_daily"

[metrics.dummy_metric]
select_expression = """
COUNTIF(engine = '{{parameters.search_engine}}')
"""
data_source = "search_clients_engines_sources_daily"
[metrics.urlbar_amazon_search_count.statistics.bootstrap_mean]
[metrics.urlbar_amazon_search_count.statistics.deciles]

Overwriting Outcomes parameters​

distinct_by_branch set to false example:

External config:

description = "Amazon Search"

[parameters.search_engine]
value = "amazon"

select_expression for metric metrics.dummy_metric will now look like this:

COUNTIF(engine = 'amazon')

distinct_by_branch set to true example:

External config:

description = "Amazon Search"

[parameters.id]
distinct_by_branch = true
# value.[corresponding_branch_name] = [value]
value.experiment_branch_name_1 = "google"
value.experiment_branch_name_2 = "amazon"

select_expression for metric metrics.dummy_metric will now look like this:

COUNTIF(CASE e.branch_name WHEN "experiment_branch_name_1" THEN "google" WHEN "experiment_branch_name_2" THEN "amazon" END)

Defining Exposure Signals​

Many Nimbus features will send a Nimbus exposure event automatically when the feature configuration is consulted; these are normandy#expose events on desktop and nimbus_events.exposure events in Glean. However, it is also possible to define custom exposure events:

[experiment.exposure_signal]
name = "nimbus"
friendly_name = "Nimbus exposure signal"
description = "Nimbus desktop feature exposure signal"
data_source = "events"
select_expression = "
   event_category = 'normandy'
   AND event_method = 'expose'
   AND event_object = 'nimbus_experiment'
   AND event_string_value = 'experiment-slug'
   AND normalized_channel = 'release'
"
window_start = 0                    # optional
window_end = "analysis_window_end"  # optional
  • select_expression: Defines the condition for when an exposure happens.
  • data_source: Specifies the dataset on which to apply the select_expression. Can use predefined or custom data sources.
  • window_start and window_end: Optional parameters that specify the date range when clients are checked for exposure. Defaults to window_start = 'enrollment_start' and window_end = 'enrollment_end'. Other valid values include:
    • Any positive integer: The number of days after the first enrollment date.
    • Any negative integer: The number of days before the first enrollment date.
    • enrollment_start: Equivalent to using 0
    • enrollment_end: Equivalent to using the enrollment period length in days
    • analysis_window_start: The start of the current analysis window
    • analysis_window_end: The end of the current analysis window

Metrics based on clients that have seen the exposure signal are only computed for those that specify exposures as one of their analysis_bases:

[metrics.ad_clicks]
analysis_bases = ["exposures", "enrollments"]

Using window_start and window_end it is possible to consider clients as exposed/non-exposed during the observation period. For example, if clients should only be considered as exposed during an analysis window:

[experiment.exposure_signal]
# ...
window_start = "analysis_window_start"
window_end = "analysis_window_end"

Or, if clients should be considered as exposed if they have received the exposure signal during the current analysis window or any time before:

[experiment.exposure_signal]
# ...
window_start = "enrollment_start"
window_end = "analysis_window_end"

Results for exposure based metrics are currently not visualized in Experimenter. To access results, the BigQuery tables need to be queried directly.

Testing configurations​

For more information on how to test configurations see Testing Jetstream Configs

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/data-products/index.html b/deep-dives/jetstream/data-products/index.html index ef923efea..585ad4b82 100644 --- a/deep-dives/jetstream/data-products/index.html +++ b/deep-dives/jetstream/data-products/index.html @@ -4,13 +4,13 @@ Jetstream Data Products | Experimenter Docs - +

Jetstream Data Products

Jetstream writes analysis results and enrollments information to BigQuery. Statistics data and metadata gets exported to GCS to make it accessible to the Experimenter console.

BigQuery Datasets​

Results Datasets​

The datasets that back the Experimenter results dashboards are available in BigQuery in the mozanalysis dataset in moz-fx-data-experiments. Technical documentation is available in the Mozilla data docs.

Monitoring Datasets​

Datasets used for monitoring the operation of Jetstream are part of the monitoring dataset in moz-fx-data-experiments.

Error Logs​

Jetstream logs errors and warning encountered during its analysis runs to monitoring.logs. This datasets is used as basis for the Jetstream error dashboard and for setting up alerts.

The logs table has the following schema:

Column nameTypeDescription
timestampTIMESTAMPTimestamp of when the log event was recorded
experimentSTRINGExperiment slug for which event was recorded
messageSTRINGLog message
log_levelSTRINGLog level: ERROR, WARNING
exceptionSTRINGRaised exception object
filenameSTRINGName the Jetstream code file the exception was raised
func_nameSTRINGName the Jetstream function the exception was raised
exception_typeSTRINGClass name the exception raised

Query Cost​

The monitoring.query_cost_v1 dataset contains the cost of each query run when analysing experiments. The dataset is updated daily and scrapes the cost information from the BigQuery logs. The query for determining the costs is part of bigquery-etl. The dataset is basis for the jetstream cost monitoring dashboard and alerts set up to send notifications when an analysis query exceeds a certain threshold.

The query_cost_v1 table has the following schema:

Column nameTypeDescription
submission_timestampTIMESTAMPTimestamp of when the query was executed
destination_tableSTRINGName of the table query was writing data to
querySTRINGSQL of the executed query
total_bytes_processedINT64Number of bytes the query processed
cost_usdFLOATCost of the query in USD based on BigQuery pricing

Experimenter Experiments​

For monitoring Nimbus experiments, some common failure cases are exposed as part of the Experiments Enrollments dashboard. These monitoring rules will require access to collected experiments enrollment data which is available in monitoring.experimenter_experiments_v1. This dataset is part of bigquery-etl and updated every 10 minutes by fetching data from the Experimenter API.

GCS Data Export​

Jetstream exports statistics data and metadata of analysed experiments to the mozanalysis GCS bucket.

Statistics Data​

After each analysis run has completed, Jetstream exports the statistics results of each experiments to the statistics sub-directory as JSON. The JSON files follow the naming format:

statistics_<experiment_slug>_<period>.json

Each file contains a JSON object for every row in the corresponding statistics table. The JSON files are pulled in by Experimenter and used for visualizing results on the Experimenter results page.

Metadata​

Metadata of analyzed experiments contains information about all metrics and outcomes that are computed during any analysis period. Metadata is written to JSON files into the metadata sub-directory with the following naming schema:

metadata_<experiment_slug>.json

Each JSON metadata file contains the following information:

"metrics": {
    "metric_slug": {
        "friendly_name": "Friendly metric name",
        "description": "Metric description defined in mozanalysis or metric-hub",
        "bigger_is_better": true
    }
    // ...
},
"outcomes": {
    "outcome_slug": {
        "slug": "outcome_slug",
        "friendly_name": "Friendly outcome name",
        "description": "Outcome description defined in metric-hub",
        "metrics": [    // metrics computed as part of outcome
            "metric_slug",
            "another_metric_slug"
        ],
        // commit hash of outcome version that was used in analysis
        "commit_hash": "74e45eb4c3bf4ea7f1d65f888a70bfa0f6a86c1e" 
    }
    // ...
}

Metadata of metrics and outcomes is used to show names, descriptions and whether larger numbers are better in the Experimenter results.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/metrics/index.html b/deep-dives/jetstream/metrics/index.html index e918e425c..41ccf8d23 100644 --- a/deep-dives/jetstream/metrics/index.html +++ b/deep-dives/jetstream/metrics/index.html @@ -4,7 +4,7 @@ Metrics | Experimenter Docs - + @@ -23,7 +23,7 @@ These are defined for each platform in the metric-hub repository in jetstream/defaults/. Look at the file for your platform. At the top you will see the metrics collected and the timeframe they are available: daily, weekly, or overall (at the end of the experiment). For help understanding any aspect of guardrail metrics on your platform - link to the file and ask in #ask-experimenter.

Outcomes are collections of metrics that relate to each other. You can associate an Outcome with your experiment in the Experiment Console - Metrics section. Data scientists can extend and define outcomes in the outcomes path of the metric-hub repository. See what Outcomes are available.

If the metrics you need are not covered by Guardrail/Core metrics or existing Outcomes - you can add a metric to your experiment by working with a data scientist to write a custom configuration for your experiment.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/operations/index.html b/deep-dives/jetstream/operations/index.html index 8966ce243..a43db443f 100644 --- a/deep-dives/jetstream/operations/index.html +++ b/deep-dives/jetstream/operations/index.html @@ -4,7 +4,7 @@ Jetstream Architecture and Operations | Experimenter Docs - + @@ -13,7 +13,7 @@ High-level overview of Cirrus

Jetstream is scheduled to run in Airflow daily. The daily runs will analyze all experiments that are currently active or just ended the day before and write metrics, statistics and errors for each experiment to BigQuery. Active V1 experiments and V6 experiments (Nimbus experiments) are retrieved from the Experimenter API.

Jetstream also fetches custom experiment and outcome configs from the jetstream/ directory in metric-hub for analysis. When a new custom config gets merged into metric-hub, the CI will trigger Jetstream to re-run all analyses for the experiment affected by the config. CircleCI will report on the status of the analysis run and link to the Cloud Logging logs.

After writing analyses results to BigQuery, statistics data is exported to the mozanalysis bucket in GCS as JSON. The JSON data is accessed by the analysis dashboard to display results.

Architecture for Scaling Jetstream​

To ensure analysis results are available in a timely manner, Jetstream implements two approaches for reducing the time required to run experiment analyses:

  • Parallelization of experiment analyses using Argo
  • Parallelization of lower-level calculations (statistics, segments, ...) using Dask

Parallelizing experiment analyses​

Argo is a light-weight workflow engine for orchestrating parallel jobs on Kubernetes and is capable of creating tasks dynamically that will be executed in parallel. Using Argo, the analyses for different experiments and analysis dates are split into separate jobs that run in parallel on the jetstream Kubernetes cluster in the moz-fx-data-experiment-analysis GCP project.

Argo expects each step in the workflow to be a container. The existing Jetstream container, which has the Jetstream CLI installed, can be used for each of these steps. The full workflow definition is defined in the workflows/run.yaml file.

Depending on how Jetstream is invoked (rerun, run-argo, or rerun_config_changed), Jetstream will determine the dates and experiments that are to be analyzed and injects them as parameters into run.yaml before launching the workflow. Argo will create separate jobs for each experiment and each analysis date. Once the analysis is complete, data gets exported as JSON to GCS.

Parallelizing lower-level calculations​

In addition to running experiment analyses in parallel, Dask is used to parallelize lower-level calculations. The following steps could be executed in parallel:

  • Analyses for each analysis period (daily, 28day, weekly, overall)
  • Analyses for different segments
  • Calculating statistics defined for an experiment analysis

The dask.delayed interface is used to turn the functions executing these steps into tasks that are added to a task graph which executes these steps in parallel. Dask is configured to use as many cores as are available on the machine by default, with 1 worker for each core. Multi-threading being avoided, instead processes are used since the code is dominated by Python code, otherwise there wouldn't be any speedup due Python's Global Interpreter Lock. To manually restrict the number of processes, the JETSTREAM_PROCESSES environment variable can be used.

Installation​

Jetstream is executed on the jetstream Kubernetes cluster in the moz-fx-data-experiments project which is set up following Argo's installation guide:

  • When creating or re-creating the cluster, BigQuery and Compute Engine read/write permissions need to be enabled
  • Installing Argo:
    • Create an argo namespace: kubectl create ns argo
    • Install commonly used components: kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.5/install.yaml
    • Create new clusterrole: kubectl create rolebinding default-admin --clusterrole=admin --serviceaccount=argo:default --namespace=argo
  • The jetstream DAG in Airflow triggers the run-argo job daily and either requires Compute Engine API access or the parameters cluster_ip and cluster_cert need to be provided
    • Currently the Airflow cluster does not have Compute Engine API access, so the cluster IP and certificate are stored as secrets and used for running Jetstream

Argo Workflow UI​

Argo provides a Web UI to access running workflows. Users need to authenticate using a Bearer token:

  • set export CLOUDSDK_CORE_PROJECT=moz-fx-data-experiments
  • Get Bearer token and copy: gcloud container clusters get-credentials jetstream --region=us-central1-a && kubectl -n argo exec $(kubectl get pod -n argo -l 'app=argo-server' -o jsonpath='{.items[0].metadata.name}') -- argo auth token
  • Connect to the Workflow UI using port forwarding: kubectl -n argo port-forward svc/argo-server 2746:2746
  • Open https://localhost:2746
  • Use the generated Bearer token (including the word Bearer) for authentication

Deleting Old Workflows​

  • The Workflow UI might get less responsive the more workflows have been run in the past
  • To delete workflows that are older than 4 days run:
kubectl get wf -o go-template -n argo --template '{{range .items}}{{.metadata.name}} {{.metadata.creationTimestamp}}{{"\n"}}{{end}}' | awk '$2 <= "'$(gdate -d '30 days ago' -Ins --utc | sed 's/+0000/Z/')'" { print $1 }' | gxargs --no-run-if-empty kubectl delete wf -n argo

Cluster Updates​

Argo updates should be tested on a separate cluster before applying them to production. Jetstream has some custom logic to connect to clusters and issue workflows that might be incompatible with future versions of Argo.

  • Setup a separate cluster, install most recent Argo version
    • Optionally, push a custom docker image that should be tested
      • docker build -t jetstream-test .
      • docker tag jetstream-test gcr.io/moz-fx-data-experiments/jetstream-test:latest
      • gcloud auth configure-docker
      • docker push gcr.io/moz-fx-data-experiments/jetstream-test:latest
      • Update the workflow configuration file to point to the docker image to be tested
  • To update Argo run:
    • Connect to cluster: gcloud container clusters get-credentials jetstream --region=us-central1-a
    • To install new release kubectl apply -n argo -f https://github.com/argoproj/argo-workflows/releases/download/v3.4.5/install.yaml
      • Change version number to most recent release
  • The GKE cluster itself is updated automatically by GCP

Tooling and Metric Versioning​

Jetstream uses the same tooling and metric versions for an experiment across its entire analysis duration. This prevents inconsistent results, for example, when changes are made to how mozanalysis computes results or new default metrics are added in metric-hub mid-experiment.

Keeping track of tooling versions​

When a new version of jetstream is released, for example after some library updates, a new Docker container gets pushed to the Artifact Registry. The container installs a specific version of each library and can be uniquely identified by a SHA256 hash. A timestamp indicating when the container was uploaded is also available.

Every time Jetstream runs and writes computed results to BigQuery, it tags the result tables with a last updated timestamp. However, the enrollments table won't update on new Jetstream runs, giving us an anchor from which to identify a consistent Jetstream image hash.

The last updated timestamp of the enrollments table is used to determine which Docker container was the most recently published one at that time. This container is then be used for all subsequent analyses.

The Artifact Registry API is used to access image information and to determine the container to use for the analysis based on the last updated timestamp of the experiments enrollment table.

Container hashes are passed to the Argo workflow config, which references docker containers used for execution.

Jetstream can be run using a specific image version using the --image_version parameter. The image can also be changed using the --image parameter.

Keeping track of metric-hub versions​

Outcome and default configs can potentially change mid-experiment, leaving some experiments in an inconsistent state. Since these configs get pulled in dynamically and aren't installed as part of the Docker image the prior approach doesn't work here.

Instead, ConfigCollection.as_of(<date>) is used to checkout an earlier version of the repo as of the provided date. This date will again be based on the last updated timestamp of the enrollments table. Calling as_of() will load the configs, defaults and outcomes that will subsequently be used for analysis. as_of() iterates through the commit history until it finds the first commit from before the last updated timestamp. If this commit is in a broken state (i.e., configs cannot be successfully loaded), as_of works forward from this point to find the closest working commit.

When making changes to experiment-specific configs, jetstream will automatically rerun the affected experiments which will result in the enrollments table getting updated and the most recent configs in metric-hub being used.

More information on how to use the most recent tooling and metric versions can be found here.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/outcomes/index.html b/deep-dives/jetstream/outcomes/index.html index 55f8f8032..82d9914ba 100644 --- a/deep-dives/jetstream/outcomes/index.html +++ b/deep-dives/jetstream/outcomes/index.html @@ -4,7 +4,7 @@ Outcomes | Experimenter Docs - + @@ -17,7 +17,7 @@ and then copy-paste the metrics into an Outcome for later use.

What happens if an Outcome changes?​

Changing an Outcome does not re-run any experiments. A commit hash associated with the version of each Outcome is captured in the experiment metadata published to GCS, so it's possible to understand which version of an Outcome was associated with an experiment analysis.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/overview/index.html b/deep-dives/jetstream/overview/index.html index f16caec18..fd73be685 100644 --- a/deep-dives/jetstream/overview/index.html +++ b/deep-dives/jetstream/overview/index.html @@ -4,7 +4,7 @@ Overview | Experimenter Docs - + @@ -69,7 +69,7 @@ are available in BigQuery. Technical documentation is available in the Mozilla data docs.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/statistics/index.html b/deep-dives/jetstream/statistics/index.html index 3434a5681..ca73953b9 100644 --- a/deep-dives/jetstream/statistics/index.html +++ b/deep-dives/jetstream/statistics/index.html @@ -4,7 +4,7 @@ Statistics | Experimenter Docs - + @@ -41,7 +41,7 @@ and returns a StatisticResultCollection.

A StatisticResultCollection is a wrapper around a StatisticResult. The meaning of the values on a StatisticResult is elaborated in the statistics table schema documentation.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/testing/index.html b/deep-dives/jetstream/testing/index.html index 57d9e4079..47bad96a2 100644 --- a/deep-dives/jetstream/testing/index.html +++ b/deep-dives/jetstream/testing/index.html @@ -4,7 +4,7 @@ Testing Jetstream Configs | Experimenter Docs - + @@ -12,7 +12,7 @@

Testing Jetstream Configs

Validation via Continuous Integration (CI)​

Configurations for Jetstream experiments get added by opening a pull-request in the metric-hub repository. Continuous Integration (CI) automatically runs checks to validate that the syntax and SQL definitions of the new or updated configuration files is correct. These checks only run for pull-requests that are opened from a branch that was pushed to the metric-hub repository. For pull-requests based on forks only some checks will run.

Pull-requests that pass the CI validation can be automatically merged without requiring an external review.

Local validation​

To locally iterate and validate Jetstream configurations, the Jetstream tooling needs to be installed: 

pip install mozilla-jetstream

Once installed, local config files can be valided using the jetstream validate_config command:

jetstream validate_config /local/path/to/config/file.toml

In case changes have been made to outcomes, defaults or metric definitions it is possible to specify a local metric-hub directory to be used for validation:

jetstream validate_config /local/path/to/config/file.toml --config_repos=/path/to/metric-hub

Jetstream previews​

When iterating on configurations, it is sometimes useful to get a preview of what computed data on the final dashboard would look like. Previews are computed on a data sample in order to reduce cost and speed up the analysis. Preview data should not be used to draw any conclusions on the outcome of an experiment. It should only be used to validate configurations.

To generate previews, install the Jetstream CLI tooling locally:

pip install mozilla-jetstream

Authenticate to GCP:

gcloud auth login --update-adc

And set the project to mozdata

gcloud config set project mozdata

This is the default project where preview data will be temporarily be stored in. It is also possible to set this project to a personal sandbox project, or any other project with write permissions.

Preview data can be generated using the jetstream preview command:

jetstream preview --help                  
Usage: jetstream preview [OPTIONS]

  Create a preview for a specific experiment based on a subset of data.

Options:
  --project_id, --project-id TEXT
                                  Project to write to (default: mozdata)
  --dataset_id, --dataset-id TEXT
                                  Dataset to write to (default: tmp)
  --start_date, --start-date YYYY-MM-DD
                                  Date for which project should be started to
                                  get analyzed. Default: current date - 3 days
  --end_date, --end-date YYYY-MM-DD
                                  Date for which project should be stop to get
                                  analyzed. Default: current date
  --num-days, --num-days INTEGER  Number of days for which the project be
                                  analyzed. Default: 3
  --experiment_slug, --experiment-slug TEXT
                                  Experimenter or Normandy slug of the
                                  experiment to (re)run analysis for
  --config_file, --config-file FILENAME
  --config_repos, --config-repos TEXT
                                  URLs to public repos with configs
  --private_config_repos, --private-config-repos TEXT
                                  URLs to private repos with configs
  --analysis_periods, --analysis-periods ANALYSISPERIOD
                                  Analysis periods to run analysis for.
  --sql-output-dir, --sql_output_dir OUTDIR
                                  Write generated SQL to given directory
  --platform TEXT                 Platform/app to run analysis for. If not
                                  specified, use Experimenter API to determine
                                  plaftorm  [required]
  --generate-population, --generate_population
                                  Generate a random population sample based on
                                  the provided population size. Useful if
                                  enrollment hasn't happened yet
  --population-sample-size, --population_sample_size INTEGER
                                  Generated population sample size. Only used
                                  when `--generate-population` is specified.
                                  Use floats to specify population sizes in
                                  percent, e.g 0.01 == 1% of clients
  --enrollment_period, --enrollment-period INTEGER
                                  Numer of days used as enrollment period when
                                  generating population.
  --help                          Show this message and exit.

By default the preview runs Hetstream on a 3 day analysis window on a population sample of 1%. These default parameters can be overwritten when invoking jetstream preview.

The resulting data artifacts get written into the mozdata.tmp dataset by default, which is configured to delete data after 7 days. Anyone at Mozilla has permissions to write to this dataset.

To generate a preview on a 10% sample for an existing experiment on Firefox iOS run:

jetstream preview \
  --experiment_slug=ios-search-bar-placement-impact-assessment \
  --start_date=2022-08-01 \
  --platform=firefox_ios \
  --population-sample-size=10

Jetstream Preview fetches experiment information from the Experimenter API. In some cases a preview might need to be generated for experiments that haven't been launched yet or that haven't seen any enrollments. For these experiments a random population sample can be generated when specifying --generate-population that will be considered as the clients that enrolled:

jetstream preview \
  --config_file=/path/to/local/config.toml \
  --start_date=2023-01-01 \
  --platform=firefox_deskop \
  --generate-population \
  --population-sample-size=1

Once preview data has been computed, a link to a Looker dashboard will be printed where data for each metric and statistic is visualized:

A preview is available at: https://mozilla.cloud.looker.com/dashboards/experimentation::jetstream_preview?Project='mozdata'&Dataset='tmp'&Slug='ios_search_bar_placement_impact_assessment'
Jetstream Preview Looker Dashboard

The dashboard shows the computed statistics for each metric in a graph, raw results as they show up in the BigQuery table as well as logs that were written during the analysis. Some additional metadata about the experiment is shown at the top. Also a cost estimate is provided. The estimate is based on the sampled data, as well as a subset of analyses periods and days. The final cost will be much higher.

The preview data gets written into the tmp dataset in the mozdata project by default. Data written to this dataset gets automatically removed after 7 days.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/deep-dives/jetstream/troubleshooting/index.html b/deep-dives/jetstream/troubleshooting/index.html index 143b01adb..daf81f5fa 100644 --- a/deep-dives/jetstream/troubleshooting/index.html +++ b/deep-dives/jetstream/troubleshooting/index.html @@ -4,14 +4,14 @@ Troubleshooting Jetstream | Experimenter Docs - +

Troubleshooting Jetstream

How can I see what Jetstream is doing?​

For checking on daily Jetstream runs, the jetstream DAG can be viewed via the Airflow Web UI. This show whether the run is still in progress or has completed.

For checking on reruns after a config change, the metric-hub CI will show all active runs and indicate whether a rerun has been successful or if there have been any errors.

For viewing the Argo workflows Jetstream is running, connect to the Argo dashboard by running:

gcloud container clusters get-credentials jetstream --zone us-central1-a --project moz-fx-data-experiments && kubectl port-forward --namespace argo $(kubectl get pod --namespace argo --selector='app=argo-server' --output jsonpath='{.items[0].metadata.name}') 8080:2746

The dashboard can than be accessed via 127.0.0.1:8080 through the web browser and provides a detailed overview of past workflows, the statuses of each step in a workflow and container logs.

How do I know if something went wrong?​

Jetstream logs errors to the console and, optionally, to the monitoring.logs BigQuery table. Logging to BigQuery is enabled by default when running Jetstream via Airflow, as it allows for better alerting and monitoring of errors. It is by default disabled for runs triggered via the metric-hub CI.

Errors can be viewed on the Jetstream error dashboard in Looker.

Additionally, alerts can be set up in Looker to check for errors daily and sent an email if failures have been detected. To subscribe to these alerts, go to the Jetstream error dashboard, click on the Alerts (bell) icon on the Critical Errors Last Run tiles and follow the "Error Count" alert.

Something went wrong, what do I do?​

  1. Check the Jetstream error dashboard for more details on the error that occurred.
  2. If the experiment uses a custom configuration, make sure the configuration is valid. Sometimes, SQL written for specifying metrics in the configuration file can contain logical errors that result in failures when computing statistics.
    • It is possible that SQL that was once valid starts to fail, because the query has become too complex. This can happen if new fields get added to a table that is being queried. In these cases, try to rewrite and simplify the SQL query or consider using source tables instead of derived views.

If you are unsure of what might have gone wrong or what to, you can open an issue in Jira or ask for help in the #ask-experimenter Slack channel.

Some results appear to be missing​

It can take up to 2 days for results of the overall time period to be available after an experiment ends. For example, if an experiment ends on 2021-04-05, then results for the overall time period will be computed in the next daily analysis run on 2021-04-06. As running the analysis can take a few hours to complete results will be available on 2021-04-07.

If results other than for the overall period are missing or more than 2 days have passed and no overall results are available, check the Jetstream error dashboard if there are any analysis errors for your experiment.

If there have been no errors, or the errors cannot be resolved, open an issue in Jira or ask for help in the #ask-experimenter Slack channel.

How do I debug operational or more complex errors?​

Debugging operational or more complex errors is usually done by Jetstream engineers.

  1. Check the Jetstream error dashboard for more details on the error that occurred.
  2. To get more detailed logs or view each individual workflow step, connect to the Argo dashboard and navigate to the workflow where errors might have occurred.
  3. Once errors have been resolved, to rerun affected experiments for the date when they failed make sure to install the Jetstream CLI locally and execute: jetstream run-argo --date=2021-04-26 --experiment_slug=bug-1695015-pref-new-tab-modernized-ux-region-1-release-86-88. For example: jetstream run-argo --date=2021-04-26 --experiment_slug=bug-1695015-pref-new-tab-modernized-ux-region-1-release-86-88

The logs can indicate a couple of different problems:

This could happen, for example, if available memory or CPUs have been exceeded. To get more information about the pods that failed, navigate to the jetstream Kubernetes cluster in the GCP web console. The web UI allows to view the memory and CPU usage of specific pods or the entire cluster as well as pod logs. This information can help to decide whether the cluster needs to be resized. Resizing the cluster or allocating more resources is worth considering if these errors happen frequently. For occasional failures, simply rerunning the affected experiment is sufficient.

An external config or outcome definition is causing failures​

  1. Ensure that the config is valid and that SQL does not contain any logical errors.
  2. If the SQL has become too complex, try to simplify queries or use source tables instead of derived views.
  3. Fix the configuration. Once the new config gets merge, the experiment will be rerun automatically.

There has been an error because of a timeout when using an external API.​

Timeouts occasionally happen when running queries in BigQuery, fetching experiments from the Experimenter API or fetching config files from GitHub. Jetstream implements a retry mechanism for most of these cases but it is possible that all of these retries fail. Rerunning affected experiments should in most cases resolve these issues. However, if this failures keep happening then this could indicate API changes.

There is a bug in the jetstream code base​

  1. Add a test case to jetstream to reproduce the error.
  2. Fix the bug and open a PR against the repository.
  3. Once the fix has been approved, merged and deployed, the affected experiment can be rerun.

Airflow returned an error or is sending notification emails​

  1. Check the Airflow logs
  2. Errors in Airflow can happen if there has been a problem with the Airflow cluster itself, e.g. the jetstream tasks could not be started. In this case, clearing the affected task to trigger a rerun should fix the issue. If problems persist, then reach out to data ops by opening a Bugzilla ticket.
  3. Airflow failures can also occur if the analysis workflow could not be started. For example, if connecting to the Argo cluster failed. Check if the jetstream cluster is in a healthy state and if the analysis run can be started using a locally installed Jetstream CLI.
  4. The Jetstream DAG has some upstream dependencies that need to successfully complete in order for jetstream to run. If one of these upstream dependencies fails, then Airflow will keep sending email alerts with up_for_retry in the subject. Failures in the upstream dependencies need to be resolved before jetstream can run. Check for the owner of the upstream task that failed and open a Bugzilla ticket with the owner tagged.
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/designing-experiments/index.html b/designing-experiments/index.html index 8a75c8dc1..e008e82a3 100644 --- a/designing-experiments/index.html +++ b/designing-experiments/index.html @@ -4,14 +4,14 @@ Designing Your Experiment | Experimenter Docs - +

Designing Your Experiment

WARNING: below is a sample image only of this part of the flow - see this LIVE and CURRENT workflow for the most accurate information, including maintained links and flow Designing flow

Good experiment design starts with a period of ideation. Think about your product strategy and brainstorm some ideas that align with your goals. Look for opportunities that are low cost and easy to implement, such as a feature that already exists in your product that you believe could be easily modified to be even more impactful.

As you work through you and refine your idea backlog, it is important to consider whether you need an experiment, or if there are other more suitable tools such as user testing, user research or market research.

Once you have concluded that an experiment is the correct approach for you continue through the designing workflow.

To design an experiment​

  1. Create an experiment brief

  2. File a data org (DO) Jira ticket (click Create in the header of the page)

  3. Show up to an experiment design office hours to discuss and validate your ideas. There are a few office hours available- depending on your need and where you are in the experiment process.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/desktop-enroll-locally/index.html b/desktop-enroll-locally/index.html index 0320dc514..9386d4086 100644 --- a/desktop-enroll-locally/index.html +++ b/desktop-enroll-locally/index.html @@ -4,13 +4,13 @@ Desktop Local Experiment Iteration | Experimenter Docs - +

Debugging an experiment from experimenter locally

Trying to iterate on an experiment in Preview on experimenter.services.mozilla.com can be painful, because even after you change something and post to preview, you have a wait a while for the updated recipe to be propagated to the CDN.

Enable Nimbus debugging​

  • In about:config, set:
    • nimbus.debug to true

Enable the Browser Toolbox​

  • In about:config, set:
    • devtools.chrome.enabled to true
    • devtools.debugger.remote-enabled to true

Prepare a few things:​

  • Load about:studies and unenroll this profile from anything that might interfere
  • On the Experimenter page for your experiment, select the contents of the Recipe JSON field from the Details tab, and copy it into your Copy/Paste buffer

Do the Enrollment​

  • Open Tools > Browser Tools > Browser Toolbox
  • Switch to the browser console
  • In the input area, do the following:
    • const branchSlug = 'treatment-a'; // or whatever branch you want
    • let recipe = paste_recipe_json_here ;
    • const { ExperimentManager: em } = ChromeUtils.importESModule("resource://nimbus/lib/ExperimentManager.sys.mjs");
    • let branch = recipe.branches.find(b => b.slug == branchSlug);
    • em.forceEnroll(recipe, branch);

Reload about:studies, and you should see the experiment​

Copyright © 2024 Mozilla Corporation
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Desktop Feature API testing

In order to make testing easier we created some helpers that can be accessed by including

const { ExperimentFakes } = ChromeUtils.import(
  "resource://testing-common/NimbusTestUtils.sys.mjs",
);

Testing your feature integrating with Nimbus​

You need to provide a feature configuration and await enrollment

let doExperimentCleanup = await ExperimentFakes.enrollWithFeatureConfig({
  featureId: "<YOUR FEATURE>",
  // You can add values for each variable you added to the manifest
  value: {
    enabled: true,
  },
});

// Now you can assume the feature is enabled so you can
// test and that it's doing the right thing

// Assert.ok(It works!)

// Finishing up
await doExperimentCleanup();

Testing with a live Nimbus recipe​

If you already published an experiment through Nimbus then you will want to test enrollment in the browser.

  1. Flip the following pref to true in the browser you want to enroll (in about:config)

nimbus.debug

  1. You need to copy paste the following URL and fill in the required details.

For experiments that are already live:

about:studies?optin_slug=<YOUR_EXPERIMENT_SLUG>&optin_branch=<BRANCH_SLUG_TO_ENROLL>

For experiments that are in "preview" mode:

about:studies?optin_slug=<YOUR_EXPERIMENT_SLUG>&optin_branch=<BRANCH_SLUG_TO_ENROLL>&optin_collection=nimbus-preview

Testing with Desktop Rollouts​

For writing tests you usually want to have the following modules imported:

const { ExperimentAPI, NimbusFeatures } = ChromeUtils.import(
  "resource://nimbus/ExperimentAPI.sys.mjs",
);
const { ExperimentFakes } = ChromeUtils.import(
  "resource://testing-common/NimbusTestUtils.sys.mjs",
);

Next this is how you would set up your feature to test integration with Desktop Rollouts:

// Ensure everything has finished initializing
await ExperimentAPI.ready();
// The actual setup
const doCleanup = await ExperimentFakes.enrollWithFeatureConfig(
  {
    // Reference your feature id already defined in the FeatureManifest.yaml
    featureId: "<YOUR FEATURE ID>",
    value: {
      enabled: true,
    },
  },
  { isRollout: true }
);

// Now your feature integration is ready for testing

// NimbusFeatures.<YOUR FEATURE>.getVariable("enabled")
// NimbusFeatures.<YOUR FEATURE>.getAllVariables()

await doCleanup(); // to remove the rollout

Testing with gTest​

Otherwise for a gTest or other requirements you can set a pref nimbus.syncdatastore.<feature name>.<variable name> with the appropriate type you defined in the FeatureManifest.

⚠️ This variable naming convention is an implementation detail and might change in the future.

#include "mozilla/browser/NimbusFeatures.h"

# Set a preference as described above based on what you want the test to do

// GetInt will return the appropriate value you set
NimbusFeatures::GetInt("aboutwelcome"_ns, "skipFocus"_ns, false);

// On update will call your callback function when the pref value changes
NimbusFeatures::OnUpdate("aboutwelcome"_ns, "skipFocus"_ns,
  [](const char*, void*){}, void*);

Examples​

Examples of platform consumers:

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/desktop-feature-api/index.html b/desktop-feature-api/index.html index b0fb25516..2d4e3efdd 100644 --- a/desktop-feature-api/index.html +++ b/desktop-feature-api/index.html @@ -4,14 +4,14 @@ Desktop Feature API (JS and C++) | Experimenter Docs - +

Desktop Feature API (JS and C++)

This guide will help you use the Nimbus Feature API in Desktop Firefox to run experiments, set values remotely, and manage user preferences. If you are familiar with Normandy and are trying to migrate a feature, you may want to check out the Migration Guide for Pref Experiments.

About the Feature API​

Can I use this?​

For the JS implementation you can import ExperimentAPI.jsm in the parent process or a privileged child process. We do support First run experiments on Windows, holdbacks, and rollouts.

For the C++ implementation you can import #include "mozilla/browser/NimbusFeatures.h" and we support early startup experiments and holdbacks.

If you have a usecase that isn't supported, please reach out in #ask-experimenter on Slack.

What is a feature?​

In the Nimbus ecosystem, a feature is an area of code instrumented for experiments and remote configuration. It can be as small as a single function or as complex as a whole about: page. Some examples:

  • aboutwelcome, The about:welcome page in Desktop
  • newtab, The about:newtab page in Desktop

In your code, you will use the Nimbus SDK to access variables associated with those features. e.g.

const { screens, skipFocus } = NimbusFeatures.aboutwelcome.getAllVariables();

Configuration sources​

Note

This section is relevant only for the JS API.

The Nimbus Feature API will return the correct configuration for a feature given a few different inputs in this order:

  1. Experiment value: First, we check if a Nimbus experiment is activated that changes the feature.
  2. Remotely-configured value: If no experiment is set, we check if there is a remotely-defined value. This is a mechanism that allows us to roll-out changes quickly between releases.
  3. Local default: Finally, we will return the current value of preferences in the manifest, if they are defined in firefox.js.

Registering a new feature​

To register a new feature, you will need to choose an identifier and add it to the manifest in FeatureManifest.yaml: After adding the feature a build step is required to update the appropriate header file.

# In FeatureManifest.yaml
# Our feature name
aboutwelcome:
  description: The about:welcome page
  # Include this if you need synchronous access / very early access at startup
  # or if you are registering this to use for platform experiments.
  isEarlyStartup: true
  variables:
    # Additional (optional) values that we can control
    # The name of these variables is up to you
    enabled:
      type: boolean
      fallbackPref: browser.aboutwelcome.enabled
    skipFocus:
      type: boolean
// In firefox.js
pref("browser.aboutwelcome.enabled", true);

Importing the Feature API​

Import the NimbusFeatures module:

XPCOMUtils.defineLazyModuleGetters(this, {
  NimbusFeatures: "resource://nimbus/ExperimentAPI.jsm",
});

API Reference Guide​

getVariable()​

getVariable(variableName: string): FeatureValue

Returns the value of a single feature variable.

// Warning: **This function will throw in Nightly and CI build** if you do not define `variableName` in the Nimbus manifest.

const foo = NimbusFeatures.myFeature.getVariable("foo");

// notAVariable is not defined in the manifest, so this will throw in CI
const baz = NimbusFeatures.myFeature.getVariable("notAVariable");

getAllVariables()​

getAllVariables({ defaultValues }): FeatureValue (JS Only)

Returns the value of all variables for a feature. Note that variables will be merged between sources.

If options.defaultValues is defined, it will be preferred before default branch fallback values but after experiment, remote, and user-set preference values.

const { foo, bar } = NimbusFeatures.myFeature.getAllVariables({
  defaultValues: { foo: true, bar: false },
});

recordExposureEvent()​

Use this to send an exposure event. By default this will send one exposure event per function call, but you can add an options object of {once: true} to only send it once per session.

Note that you should add an exposureDescription to the manifest describing when/how this event is sent.

NimbusFeatures.myFeature.recordExposureEvent();

// Only sends once per session, even if this function is called multiple times
NimbusFeatures.myFeature.recordExposureEvent({ once: true });

ready()​

ready(): Promise (JS Only)

Wait for the remote experiment and defaults stores to be synced before checking values.

await NimbusFeatures.myFeature.ready();
const { foo } = NimbusFeatures.myFeature.getAllVariables();

onUpdate()​

Listen for changes, include to remote defaults or pref values.

NimbusFeatures.myFeature.onUpdate((event, reason) => {
  /**
   * `reason` is a string that can be used to identify the source
   * of the update event.
   * This list of reasons:
   * 1. `feature-experiment-loaded` or `feature-rollout-loaded` this
   * is triggered when the Nimbus feature has finished loading
   * (when .ready() resolves). It is not relevant for isEarlyStartup=true features
   * 2. `experiment-updated` or `rollout-updated` client recipe for this
   * feature was changed (activated or deactivated)
   * 3. `pref-updated` the value of the fallback pref for the feature
   * variable was changed
   */
  const newValue = NimbusFeatures.myFeature.getAllVariables();
  updateUI(newValue);
});

off()​

Stop listening for changes.

NimbusFeatures.myFeature.onUpdate(aListener);

// Later
NimbusFeatures.myFeature.offUpdate(aListener);

Experiment Metadata​

If you need to know whether an experiment is active or get access to the experiment or branch identifier (for example, to report in utm_params), you can use ExperimentAPI.getExperimentMetaData:

XPCOMUtils.defineLazyModuleGetters(this, {
  ExperimentAPI: "resource://nimbus/ExperimentAPI.jsm",
});

const data = ExperimentAPI.getExperimentMetaData({ featureId: "myFeature" });

// If there is no experiment, data will be null.
const slug = data?.slug;
const branchSlug = data?.branch?.slug;

if (experimentSlug && branchSlug) {
  sendSomeTelemetry(
    `The experiment identifier is ${slug} and the branch identifier is ${branchSlug}`,
  );
}
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/desktop-migration-guide/index.html b/desktop-migration-guide/index.html index e124c928f..ff2bf761f 100644 --- a/desktop-migration-guide/index.html +++ b/desktop-migration-guide/index.html @@ -4,14 +4,14 @@ Desktop Migration Guide (JS) | Experimenter Docs - +

Desktop Migration Guide (JS)

To migrate front-end normandy prefs to Nimbus controlled experiments or rollouts​

This guide will help you migrate your Desktop front-end code to run experiments with Nimbus, while still being able to use preferences for default and user-override values

Prerequisites:

  • Your experimental variables are already instrumented with Firefox preferences
  • You don't use the user branch of each pref for anything other than actual user-defined values or testing (see docs on order of precedence
  • Your code can import a jsm

An illustrative example (about:myself)​

For the purposes of this guide, we will be migrating an imaginary about page (about:myself), which uses the following preferences defined in firefox.js:

pref("browser.aboutmyself.enabled", true);
pref("browser.aboutmyself.bgcolor", "#FE8DAE");

Step 1: Add a new feature to the manifest​

First, you will need to register a new feature in FeatureManifest.yaml. In this case, we're creating one called aboutmyself. After adding the feature a build step is required to update the appropriate header file.

Read more to find out if you want to send an exposure event. This is optional but a decision must be recorded in the manifest.

Each preference is registered as a variable:

aboutmyself:
  description: A page that shows personal browsing stats
  # Exposure is optional, in which case `hasExposure` would be false
  # and `exposureDescription` would not be defined
  hasExposure: true
  exposureDescription: The exposure is the earliest moment that the user could be affected by the experimental treatment
  variables:
    enabled:
      type: boolean
      fallbackPref: browser.aboutmyself.enabled
    bgColor:
      type: string
      fallbackPref: browser.aboutmyself.bgcolor

Step 2: Update your feature code​

First, you will need to import ExperimentAPI.jsm:

XPCOMUtils.defineLazyModuleGetters(this, {
  NimbusFeatures: "resource://nimbus/ExperimentAPI.jsm",
});

Then anywhere in your code that uses Services.prefs to get experiment-defined values, use NimbusFeatures instead:

element.style.backgroundColor = Services.prefs.getBoolPref("browser.aboutmyself.bgColor");

becomes

element.style.backgroundColor = NimbusFeatures.aboutmyself.getVariable("bgColor");

See the API reference docs for more details, including listening to changes.

Step 3: Run tests​

If you've configured fallback preferences your tests should pass as written, but we recommend also reading Testing Guide to add experiment-specific tests.

FAQ​

What happens if I run a Normandy experiment and a Nimbus experiment simultaneously?​

If possible, we do not recommend you run Normandy and Nimbus experiments on the same user population simultaneously. If a user were to enroll in an experiment from both sources, the value returned would follow the rules defined in Configuration Sources docs. (Nimbus values override default pref branch values; user pref branch values override Nimbus).

If you have a long-running Normandy experiment during which you must launch a Nimbus experiment, you can add a custom targeting rule to exclude users from a specific experiment ID.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/desktop-pref-experiments/index.html b/desktop-pref-experiments/index.html index bba99ded4..6afadb926 100644 --- a/desktop-pref-experiments/index.html +++ b/desktop-pref-experiments/index.html @@ -4,7 +4,7 @@ Running Pref-setting Experiments on Desktop | Experimenter Docs - + @@ -41,7 +41,7 @@ pref-setting variables.

Restrictions with Fallback Prefs​

Variables may not specify both a fallbackPref and a setPref.

Fallback prefs and set prefs are mutually exclusive. That is, If any variable in any feature specifies a pref as a fallback pref, no variable may set that variable as a set pref and vice versa.

These restrictions are enforced at build time.

Copyright © 2024 Mozilla Corporation
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Debugging Targeting expressions

How to enable ASRouter devtools​

  • In about:config, set browser.newtabpage.activity-stream.asrouter.devtoolsEnabled to true
  • Visit about:newtab#devtools-targeting to see the devtools (you need to copy-paste this manually to navigate).

Overview of ASRouter devtools​

Devtools image

Targeting​

Available targeting attributes are documented in firefox-source-docs.

Inside the textarea targeting expressions can be written and evaluated using the Evaluate button.

Targeting expressionStatusResultObservation
Correctly formatted JEXL expression
❌
✅		false
true		Depends on the truth value of the expression
Correctly formatted JEXL expression
referencing unknown variables		❌Empty resultUsing unknown variables is an error, result is neither true nor false
JEXL expression with syntax error❌<ERR_MSG>Full error message is shown

Builtin functions and examples​

The full list of available functions can be seen in FilterExpressions.jsm.

A JEXL function is called using the | operator followed by the function name, example: [1,2,3]|length == 3.

  • Any preference can be read using |prefValue
  • An array of objects can be filtered by key value
topFrecentSites // An array of objects with `frequency` and `host` names

topFrecentSites[.frecency > 9000] // returns an array containing only those objects with frequency over 9000
  • [1,2,3] intersect [3,4,5] => [3]
  • [{a:1, type:"foo"}, {b:2, type:"bar"}]|mapToProperty("type") => ["foo", "bar"]
  • Date casting
// Is the profile older than 7 days
(currentDate | (date - profileAgeCreated)) / 86400000 > 7;
  • 1 in [1,2,3] => true
  • "JEXL expression"|regExpMatch("\w+") => ["JEXL"]
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/ending/index.html b/ending/index.html index ebcc4a0fc..d21b570e8 100644 --- a/ending/index.html +++ b/ending/index.html @@ -4,13 +4,13 @@ Ending Your Experiment | Experimenter Docs - +

Ending Your Experiment

To end enrollment​

Once the experiment has reached the end of the enrollment period, the experiment owner can elect to end enrollment which will prevent new clients from enrolling. To do this, first press the End Enrollment button located on the experiment's management page in the Experimenter console. This will record the request. A reviewer with L3 access will then need to approve the request. If your product team does not have a dedicated reviewer, you can request the ending by posting in #ask-experimenter.

Some notes:

  • Enrollments can be ended at any time after launch, not just after the proposed period
  • If enrollment is ended before sufficient client volume has been achieved, there may not be enough users in the experiment to determine statistical significance.
  • By default, clients will continue to enrolled until enrollment is manually ended. If the experiment needs a different enrollment window than when it was ended, a custom Jestream config will need to be created. For assistance creating this, please file a Data Org JIRA ticket or attend the relevant office hours for your experiment. An example of the relevant TOML is below. This will set the enrollment period to be the first 14 days of the experiment. Note that this will impact the observation window (data collection window between enrollment and the end of the experiment, from which the experiment results are calculated).
[experiment]
enrollment_period = 14

To end the experiment​

Once the experiment has run for sufficient time, it can be ended. This is the process by which clients are unenrolled from the experiment and revert to seeing the default experience. Ending the experiment will also trigger the final analysis to be run. This is a nightly batch process, so expect results to be available the day after the experiment was ended.

The process to end the experiment is similar to that of ending enrollment: press the End Experiment button to request ending. Then get a reviewer (either from your team or #ask-experimenter) to approve the change.

Some notes:

  • Automated analyses are based on the actual observation period (period between enrollment and experiment ending), not the planned period. So experiments that remain open past their scheduled end date will have more days worth of data included in their results.
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/enrollment/experiment-enrollment/index.html b/enrollment/experiment-enrollment/index.html index 8d17f734f..b560a0221 100644 --- a/enrollment/experiment-enrollment/index.html +++ b/enrollment/experiment-enrollment/index.html @@ -4,13 +4,13 @@ Experiment enrollment | Experimenter Docs - +

Experiment enrollment

When is a user enrolled?​

Question​

When a user receives a recipe, are they enrolled immediately or at the next session?

Answer​

For first run experiments, the recipe is bundled with the app.

For existing users, the flow is:

1) Firefox starts

2) Existing enrollments are applied fast

3) Browser continues to start

4) Nimbus syncs and computes new enrollments

5) New enrollment prefs are set

So if your feature only reads prefs right at startup, then the pref wouldn't be applied until the following session.

tip

You can use isEarlyStartup to persist the prefs to disk so that they are available as soon as possible at startup.

Unenrollment​

Question​

If a client is unenrolled from an experiment (say they opt out of that experiment specifically) will they be able to enroll again if they meet the criteria?

Answer​

No. Once a user is disqualified by opting out (or through targeting, etc.) the client keeps a record of that and won't enroll back into the same experiment.

Copyright © 2024 Mozilla Corporation
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Experiment Owners

For an overview of experiment lifecycle, See Experiment Workflow Overview

After you've prepared your experiment brief, entering the info into the Nimbus/experimenter console should not take a long time. The role of experiment owner is most frequently played by a Product Manager, though others may act in this capacity. Please follow the Getting Started: Product Manager guidance before using Nimbus/experimenter for the first time. If it is your first time - feel free to ask for a "shadow" in #ask-experimenter. A "shadow" is an experienced experiment buddy to answer questions on zoom while you fill out your first experimenter form.

Experimenter is intended to be flexible and simple to use by allowing users to:

  • Save pages, leave the console, and come back to it later without having lost any of their information.
    • Go back and forth between all pages through the sidebar, without having to have completed the page completely.
  • Edit or modify all information while your experiment is in Draft state.
  • Put your experiment into Preview mode for QA and self-testing
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/experiment-sizing/index.html b/experiment-sizing/index.html index efa9842da..3470a74fa 100644 --- a/experiment-sizing/index.html +++ b/experiment-sizing/index.html @@ -4,13 +4,13 @@ Sizing Experiments Using Mozanalysis | Experimenter Docs - +

Sizing Experiments Using Mozanalysis

This page gives an overview of how Mozanalysis can be used to do experiment sizing. Mozanalysis is a library used to standardize experiment analysis at Mozilla. Mozanalysis contains the statistical tools used by Jetstream to produce results from experiments, as well as tools to pull historical data from BigQuery to calculate necessary sample sizes to carry out experiments. The results of the tool are the sample size and percent of the total target population required per branch of an experiment with a balanced design to achieve a given power.

Experiment sizing paradigm​

Experiment sizing in Mozanalysis retrieves historical data from BigQuery in a way that mimics how experiment enrollment and analysis is conducted by Jetstream: an enrollment period is defined, during which clients that satisfy certain target conditions are recorded; following enrollment, metrics are calculated for those clients over a defined analysis period. Following collection of metrics, sample size calculation is performed that corresponds to the statistical tests to be used in analyzing the eventual experiment.

Mozanalysis sizing workflow

Enrollment and analysis​

Similar to how experiments use an enrollment period to select clients for a study before collecting metrics for a period of time, Mozanalysis experiment sizing records all clients that satisfy the target conditions during an enrollment period, set by the user based on the analysis start date and the number of dates to enroll. Metrics are then recorded for a user-defined number of days for the analysis.

Analysis with an enrollment period

An alternative to using an enrollment period to select clients is to use continuous enrollment, where any client that satisfies the targeting criteria during the entire analysis period is enrolled; metrics are collected from each client's enrollment date to the end of the analysis period.

Analysis with continuous enrollment

Time series of metrics​

In some cases, experiments will require client-level time series data. Mozanalysis can split the analysis period into time series periods (either daily, weekly, or monthly).

Time series metrics

The time series data returned will contain a row for each client for each time series period, so the number of rows in the time series results will be (number of periods) times larger than the results for a single analysis window. Mozanalysis offers a few options to retrieve this data:

  1. Download the entire set directly: Mozanalysis will warn the user about the approximate size of the data set they will download.
  2. Download the data in chunks: Analysis periods in the time series are keyed by start dates, and the data for each period can be downloaded separately using this key.
  3. Calculate aggregated metrics in BQ before downloading: A BQ aggregate function can be used to calculate within-analysis-period statistics before downloading; this will drastically reduce the size of the data, to (number of periods) rows, and is recommended whenever possible.

Targets and metrics​

Selecting clients for analysis is accomplished by using the Segment objects in Mozanalysis; users can either reuse segments that currently exist in Mozanalysis or define one at runtime. Segments consist of a data source, either a table or view in BigQuery, and a SELECT expression to filter that data source; this SELECT expression must include a SQL aggregate function.

Similarly, Mozanalysis experiment sizing reuses Metric objects from Jetstream, and users can reuse metrics that are currently implemented in Mozanalysis or in Jetstream configs, or users can define their own at runtime.

Metrics and target segments are passed to Mozanalysis experiment sizing as lists of Segment or Metric objects; users may include multiple of each in the analysis. When multiple Segment objects are used, Mozanalysis identifies clients that satisfy the conditions of all targets in the list. If users would like to run analyses where Segments should be joined with OR rather than AND, multiple experiment sizing tasks should be completed, for each condition in the OR statement, and aggregate the returned results from each separate study.

Implemented sample size calculators​

Mozanalysis contains functions that take the results from pulling historical data and the list of metrics contained in the results and produce sample size estimates for each metric, given the desired power, significance level, and relative effect size expected from the change, a percent change in a statistic of the metric. The tests in Mozanalysis will return the required sample size per branch of the experiment; these sample sizes assume the experiment has two branches and that each branch will have an equal number of clients enrolled in the experiment. The following tests have sample size calculators implemented:

  • Z or T test for difference in means for independent samples
  • Z test for difference in proportions for independent samples
  • Test for difference of Poisson rates
  • Empirical effect size Wilcoxen-Mann-Whitney U test, based on an effect size calculated as the 90th percentile of week-to-week changes in the metric

Example Colab notebooks​

  1. Example with explanations for setting up sizing for a simple experiment (link)
  2. Replicating the sizing for the MR holdback (link)
  3. Sizing a mobile experiment (link)
  4. Replicating sizing for Waldo, which uses continuous enrollment (link)
  5. Pulling time series historical data and sizing with empirical sample size calculation (link)
Copyright © 2024 Mozilla Corporation
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Exposure

Question​

Is the "exposure event" the default way to use all the automation to exclude the first session? Or is manual exclusion of the first session in analysis the only way?

Answer​

Manual exclusion in analysis is the way. Exposures tell us which users saw the treatment, but we just use that as an indicator, we don’t automatically filter the data after the exposure event, so it has to be manual.

Copyright © 2024 Mozilla Corporation
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FML Errors

When creating your experiment, you may come across Feature Manifest errors on the Branches page:

These errors are generated by the Feature Manifest Language (FML). Learn more about these errors here.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/faq/branches/how-many-branches/index.html b/faq/branches/how-many-branches/index.html index 6297dfcdd..9c41130a9 100644 --- a/faq/branches/how-many-branches/index.html +++ b/faq/branches/how-many-branches/index.html @@ -4,14 +4,14 @@ How many branches can I have? | Experimenter Docs - +

How many branches can I have?

While you might want to try out lots of variations for your experiment - each variation reduces your chance of detecting changes. We highly recommend running as few variations as possible. Trying to cram too many changes into one experiment can lead to learning nothing about all branches.

This is discussed in office hours for mobile or desktop when you review your experiment with data science.

We aim to see statistically significant changes, also know as changes that aren’t likely to have been caused by chance.
In order to find a statistically significant change we need a large enough population size that we can say “we’d expect to see this same change if we repeated this experiment 95 out of 100 times”.

Stat significant changes are more detectable if:​

  1. The change caused a big impact in what you are measuring. It takes fewer users per branch to detect if a 5% change was by chance, then if a 1% chance was by chance.
  2. If everyone encounters the scenario your change impacts. If you are looking to change something in PDF and only 5% of people use the PDF feature - you will need a large audience size for EACH branch.
  3. We run on enough users so we can detect smaller changes .5-2%. Going to more users is decided based on a few factors: 1. Risk - if there is a possible negative risk to user experience, stability, or revenue, 2. What else is running? Would you be taking up all the experimentation for an area).

Before adding more branches:​

  1. Consider if this might be good to break into multiple experiments. That way you can learn the most critical aspects first with 2-4 branches before moving on to experiments with more specific learnings.
  2. Are there other ways to learn this? Experiments provide data. If you are looking for qualitative feedback on what users like or if they understand the flow - consider user testing first, then run an experiment on the winners from user testing.
Copyright © 2024 Mozilla Corporation
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Custom configs

Question​

If I create a custom config for an experiment, will I still results still be generated for the primary and secondary outcomes, if I select them? Or will the config completely overwrite those outcomes?

Answer​

You will still get the outcomes even with a custom config.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/faq/desktop/desktop-prefs/index.html b/faq/desktop/desktop-prefs/index.html index 3336e6897..0a2c5d4de 100644 --- a/faq/desktop/desktop-prefs/index.html +++ b/faq/desktop/desktop-prefs/index.html @@ -4,13 +4,13 @@ Desktop prefs | Experimenter Docs - +

Desktop prefs

tip

See the deep dive on desktop prefs for more information

Question​

We were looking to run an experiment in v115 that required setting a pref. We have registered the pref and landed it on beta, but uplifting to release so late may be problematic. Is there any way to make this experiment work in 115 without that update to FeatureManifest.yaml landing?

Answer​

No, the only way to set prefs currently requires them to be registered in the feature manifest.

Copyright © 2024 Mozilla Corporation
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Exposure

tip

See the deep dive on growable collections and Jetstream enrollment vs. exposure for more information

Question​

How do I decide whether I want hasExposure to be true/false on desktop? What's the value of these events or the cases where I might/might not want to use them?

Answer​

Exposure events are most valuable when a good proportion of enrolled users won’t see the treatment. For example, if seeing the treatment relies on a user action like opening a PDF or using picture in picture.

Sometimes an experiment might have a large impact on the set of users who saw it, but relatively few users did the action to see the treatment. So the “overall” impact of the experiment might be small, but with exposure events we can see that there was indeed a large impact on users who saw the treatment.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/faq/early-startup/how-early/index.html b/faq/early-startup/how-early/index.html index 55cfabfc5..50f707386 100644 --- a/faq/early-startup/how-early/index.html +++ b/faq/early-startup/how-early/index.html @@ -4,7 +4,7 @@ How early is early? | Experimenter Docs - + @@ -14,7 +14,7 @@ will read from prefs if it hasn't yet loaded its data store. Then you can use the C++ API.

You don't have to do anything special through, as long as isEarlyStartup is set it should Just Work. Assuming everything is main thread, you shouldnt have any issues.

First startup initializes Normandy and then nimbus, which will then try to grab experiments and rollouts for all features (code). This happens before we paint the first window. First startup blocks painting.

Early on Mobile​

TBD

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/faq/early-startup/what-do-it-do/index.html b/faq/early-startup/what-do-it-do/index.html index d9c2eed0a..927a9bf6f 100644 --- a/faq/early-startup/what-do-it-do/index.html +++ b/faq/early-startup/what-do-it-do/index.html @@ -4,13 +4,13 @@ What does early startup do? | Experimenter Docs - +

What does early startup do?

tip

There is info on how to use Early Startup here in the documentation

Include "isEarlyStartup" in the FeatureManifest.yaml if you need sychronous access / very early access at startup or if you are registering this to use for platform experiments.

What does this do?

  • It caches the feature values to disk using temporary prefs so that they’re available as soon as user prefs are initialized and loaded, so they’re available earlier in browser initialization and also helps make them available to gecko more easily.
  • IsEarlyStartup means we persist the experiment data in prefs so you can a) use them from c++ or b) use the JS experiment API before we've loaded experiments from disk.

So why don't I always use Early Startup to be sure my feature is controllable by an experiment early enough?

  • You have to reconcile storing state in multiple places, it's only needed for a specific set of features in specific circumstances, so for the rest of them it's much safer to not touch the pref store at all and only worry about a single source of state.
  • It also makes tests more annoying because if you don't clean them up properly it's easy to break adjacent tests
Copyright © 2024 Mozilla Corporation
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Ending experiments

What happens at experiment end? See the Experimentation Workflow for general information about ending experiments.

Feature state changes​

Question​

If there is a problem and I need to abort the experiment and rollout, will cancelling the experiment and rollout revert the feature flags to their pre-experiment and pre-rollout states (i.e. feature is disabled) for all users enrolled so far?

Answer​

Ending means Nimbus stops applying the change, so users will go back to whatever state they would have without the experiment or rollout applied. If you have a complex feature - you can verify by asking QA to test that scenario before release as well.

Copyright © 2024 Mozilla Corporation
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Feature restart

Question​

Is feature restart supported?

Answer​

Yes! Feature restart is supported by Nimbus. This is how it works when a feature requires a restart:

  • Session 1: Client enrolls, applies feature configuration, but the feature itself won't implement the changes until the next restart
  • Session 2: Client is enrolled, feature configuration is applied, feature now shows changes
Important

Please share that this experiment needs a restart in your data science Jira ticket and that there is custom work needed at analysis to exlude the pre-restart data.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/faq/first-run/index.html b/faq/first-run/index.html index a5fb57dc3..325d1394f 100644 --- a/faq/first-run/index.html +++ b/faq/first-run/index.html @@ -4,13 +4,13 @@ Mobile First Run | Experimenter Docs - +

Mobile First Run

Question​

What is First Run Release Date?

Answer​

The first run release date field is applicable for mobile first run experiments. If you are running a first run experiment, selecting the "First Run Experiment" checkbox will allow you to select a release date. When entering the date, you should select the expected release date for the Firefox version that you are targeting (this version number is what you are selecting in the "Min Version" field at the top of the Audience page).

If you do not know the release date, never fear! The help text on the "First Run Release Date" links to https://whattrainisitnow.com/, where you can look up the dates for whatever version of Firefox that you are targeting.

Why should I care about this date?

First run experiments are bundled with the app itself, so it's important that we know when the specific app version will be released to the users. This will help us notify you when it is time to end enrollment/end the experiment.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/faq/general-faq/index.html b/faq/general-faq/index.html index 10b0b0009..e5e4f63c0 100644 --- a/faq/general-faq/index.html +++ b/faq/general-faq/index.html @@ -4,13 +4,13 @@ General FAQ | Experimenter Docs - +

General FAQ

Question​

What's the difference between "Experimenter" and "Nimbus"?

Answer​

Nimbus is the code name of the experimentation project, so Nimbus refers to the whole experimentation system (handles enrolling/unenrolling users, sending branch configurations, etc). Experimenter is the customer interface to both Nimbus and Jetstream that you access at http://experimenter.services.mozilla.com/. In Experimenter, users can configure experiments, manage them (launch, monitor, end), and can see the results.

Question​

What is Jetstream?

Answer​

Jetstream is the whole analysis system (interfaces with data, runs analysis, formats results).

Question​

What is Normandy?

Answer​

Normandy (https://experimenter.services.mozilla.com/legacy/) is the legacy experimentation platform for Firefox. It is being deprecated in favor of Nimbus.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/faq/mobile-faq/index.html b/faq/mobile-faq/index.html index 918159b79..fe84f9628 100644 --- a/faq/mobile-faq/index.html +++ b/faq/mobile-faq/index.html @@ -4,13 +4,13 @@ Mobile FAQ | Experimenter Docs - +

Mobile FAQ

Question​

As a mobile engineer, how do I add a dot release version?

Answer​

  • Add a new Version to the constants.py file in Experimenter
  • Run make generate_types to generate the new version in schema.graphql and globalTypes.ts
  • Example PR

Question​

For Mobile first-run experiments, can I run multiple experiments in the same version as long as they are on different surfaces?

Answer​

Yes, we can run multiple mobile first-run experiments on the same version so long as they are using different surfaces (different Nimbus features, e.g. onboarding-feature or search or messaging). We could even technically run multiple on the same surface, but we don’t usually have enough users to get significance if we split like that.

If multiple first run experiments are configuring the same feature, the same user will only get a maximum of one of those experiments. If they configure the different features, then the same user could get the both experiments.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/feature-definition/index.html b/feature-definition/index.html index b458e2183..1072d2b85 100644 --- a/feature-definition/index.html +++ b/feature-definition/index.html @@ -4,13 +4,13 @@ Feature Definition | Experimenter Docs - +

Feature Definition

In the experimentation ecosystem, experiment surfaces are described as features. A feature is an area of code instrumented with telemetry and accessible for remote configuration. It can be as small as a single function or as complex as a whole page. Some examples:

aboutwelcome: The about:welcome page in Desktop
homescreen: The homescreen page in Fenix
tabTrayFeature: The tab tray in Firefox iOS

Features are defined in a Feature Manifest file for the application, and the client code uses the Nimbus SDK to access variables associated with those features.

After landing a new feature in mozilla-central and before doing an Experiment or Rollout using it, it is recommended to go through QA to provide an extra layer of stability and possibly be informed of certain limitations that could exist with the feature. See To test your feature for information on how to involve QA.

To define your feature in the feature manifest file​

First, look at what is already defined in the manifest file:

To test your feature​

Starting with 2023, the Ecosystem QA team has begun to test all the available Desktop Nimbus Features and their configurations. This is done in an attempt to ease testing on future experiments using a feature config (old or new) and to provide a baseline health report for it.

For engineers looking to test a new config:

  • See this document for steps on how to file a QA request. When logging the ticket, please use the Feature-Configuration label in JIRA to mark it as such. Example
    • If you have documentation regarding the feature’s configuration capabilities, please link it to the QA ticket as this helps with Test Plan and Test Case creation.
  • The most commonly QA asked questions for feature configs are:
    • What specific functionality is enabled in the browser and how exactly could we see it in action?
    • Are there any exposed user preferences in about:preferences when using the feature?
    • If the feature has an exposure event, when does it trigger?
    • Does the feature have specific telemetry events and which telemetry data pipeline are we using? Legacy or Glean?
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/feature-metrics/index.html b/feature-metrics/index.html index 4a77daa4d..645d5c195 100644 --- a/feature-metrics/index.html +++ b/feature-metrics/index.html @@ -4,13 +4,13 @@ Feature metrics | Experimenter Docs - +

Feature metrics

Question​

What do I do if feature metrics aren't there?

tip

Watch this video to know how metrics work with experiments. The video covers the steps below. These details are provided here for ease of reference and links.

After completing your experiment brief and defining your learning goals - attend an office hours to discuss how you could measure the changes. Based on that discussion - go through the steps below until the metrics needed for this experiment are covered. You do not need to complete all the steps - just enough to answer your questions.

Step 1 - Check here to learn if the existing Guardrail/Core metrics or Outcomes will answer your questions.

Step 2 - Did an experiment run previously had these metrics you need?

  • Look at previous experiments in your feature area - do those metrics meet your needs? Re-using metrics is a much quicker than writing custom metrics from scratch.
  • If yes, create a DO Jira ticket including:
    • the link to your current experiment
    • the link to the previous experiment(s) results. Specify the metrics from the previous experiment you are interested in and how they answer your question.
  • BEST PRACTICE: If you want these metrics for future experiments on this feature - ask to have an Outcome added instead of a custom config file. Adding an Outcome means these metrics will be available to select from the Outcome drop down in Metrics for future experiments.

Step 3 - Provide information to create a custom metric for this experiment.

  • Create a DO Jira ticket including:
    • the link to your current experiment
    • the questions you need to answer about the feature (that aren't covered by metrics from step 1 or step 2)
    • the specific telemetry probes in your feature that will be used to answer the question - including the possible values those probes collect and when they are sent.
  • A data scientist will work with feature product and engineering to create a custom jetstream config file to add these metrics. Provided the telemetry probes exist and are testing - the custom configuration file can be written after experiment launch (before end) and applied.
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/fml-spec/index.html b/fml-spec/index.html index ba7144e4c..f53a65f1c 100644 --- a/fml-spec/index.html +++ b/fml-spec/index.html @@ -4,7 +4,7 @@ The Feature Manifest Language | Experimenter Docs - + @@ -19,7 +19,7 @@ TABLE_NAME '/' KEY

The lookup uses the bundle.localizedString(forKey: key, value: nil, table: tableName) method.

If the text is not found, then the raw string is used instead.

For Image, they should correspond to an image named in an asset bundle, and use the UIImage(named: name, in: bundle, compatibleWith: nil) constructor.

For Text strings, if the text key does not resolve to a localized string, the key itself is used as a string.

Object types​

Some features require more organization. In the case, JSON Objects can be coerced into generated data classes.

Object types have fields in the same way features have variables. Objects can be used in multiple places, and in more than one feature.

features:
  dialog-appearance:
    description: A feature to vary the appearance of all toasts and modal dialogs
    variables:
      positive-button:
        type: ButtonAppearance
        default:
          background-color: blue
          text-color: white
      neutral-button:
        type: ButtonAppearance
        default: {}
      negative-button:
        type: ButtonAppearance
        default:
          text-color: white
          background-color: red
objects:
  ButtonAppearance:
    description: A button used in dialogs throughout the app
    fields:
      text-color:
        description: The color of the text
        type: String
        default: black
      background-color:
        description: The background color
        type: String
        default: gray

The JSON to recreate the defaults for the feature above would be:

{
  "positive-button": {
    "text-color": "blue",
    "background-color": "white"
  },
  "neutral-button": {
    "text-color": "black",
    "background-color": "gray"
  },
  "negative-button": {
    "text-color": "red",
    "background-color": "white"
  }
}

Structural types​

Generic types aren't supported, but in the following section, T can be any other type supported by the FML, including structural types.

  • Option<T> or T?
  • List<T> - lists of type T. Lists are encoded with JSON arrays. Lists are not merged, so are less useful than you might think.
  • Map<K, V> — maps with key type K to V.

Maps are transported as JSON objects, which restrict the types of the keys to types that can be coerced from Strings. Additionally, JSON values that cannot be coerced to the value type of the map are discarded.

features:
  homepage:
    variables:
      sections-enabled:
        description: A map of whether or not to display the sections.
        type: Map<SectionId, Boolean>
        default:
          top-sites: true
          jump-back-in: false
          pocket: false
          recently-saved: false
          recent-searches: false
      section-ordering:
        description: The order that the sections appear in on the homescreen.
        type: List<SectionId>
        value:
          - jump-back-in
          - pocket
          - recently-saved
          - recent-searches
    defaults:
      - channel: nightly
        value:
          {
            top-sites: true,
            jump-back-in: true,
            recently-saved: true,
            recent-searches: true,
            pocket: true,
          }
enums:
  SectionId:
    description: An enum representing the sections enabled by the homepage.
    variants:
      top-sites:
        Frecency based URLs
      jump-back-in:
        Tabs which the user was interrupted while reading.
      pocket:
        URLs from the Pocket homepage
      recently-saved:
        URLs which were recently bookmarked or saved to Pocket
      recent-searches:
        Search queries and their opened results.

In this example, a Map<SectionId, Boolean> is used. SectionId is an enum.

Maps with enum keys must have a default value for every variant of the enum.

Since maps are backed by JSON objects, the merge/patching allows entries to come from the manifest, experiments and rollouts simultaneously.

String aliasing​

As the size and complexity of the feature grows, different parts of the configuration are tied together with String keys.

string-alias is a type annotation for FML feature variables. It's effect is to define a String type with a limited set of valid strings.

This is like enums, in that it defines a set of valid values, but unlike enums in that the set is not known at build time.

features:
  onboarding:
    description: A feature to vary the appearance of all toasts and modal dialogs
    variables:
      queries:
        description: A map of named JEXL queries
        type: Map<QueryName, String>
        string-alias: QueryName
        default:
          ALWAYS: 'true'
          CHRISTMAS_DAY: '-12-25' in date_string
      cards:
        type: Map<CardKey, CardData>
        string-alias: CardKey
        default: {}

objects:
  CardData:
    description: An onboarding card, which can be optionally displayed or hidden.
    fields:
      trigger-if:
        description: Show this message if the list of queries are all true.
        type: List<QueryName>
        default:
          - ALWAYS
      except-if:
        description: Hide this message if any of this list of queries are true.
        type: List<QueryName>
        default: []
      …

In the above example, QueryName is given as the set of strings that are keys for the feature's queries map.

When used in the cards trigger-ifandexcept-iflists, the FML will validate that each item is found in the map. In this manner, experiment owners can add queries to the map and safely use them when defining newcards`, without needing a rebuild or re-release of the code.

For more, please see the string-alias documentation.

Merging other FML files into this one​

This include property is a list of files which will be merged with this one. The files may be relative to this one, absolute or URLs.

include:
    - nimbus/search.yaml
    - @mozilla/nimbus-shared/fml/messaging.yaml

Notice the @ in the second entry: by default this will be interpreted as a GitHub repository, with the file path on the main branch. More about using @ paths here.

In this example, two files are merged into this one: the features, enums and objects from each are added to the corresponding objects in this one.

More

Linking files from other components​

The import list is a list of objects referencing other FML files from other components. The code generated from those files are generated at build time of those components, with their own channels and configurations.

import:
    - path:    ../Accounts/nimbus.fml.yaml
      channel: production
    - path:    @mozilla-mobile/ios-components/components/feature/search/nimbus.fml.yaml
      channel: release

The list contains blocks with the following mandatory properties:

  • path: this string value is a relative path or URL to the imported file.
  • channel: this string value is the name of a channel. The channel must be in the channel list of the included file.

An imported file:

  • must have an about block.
  • may have an include block.

Optionally, a features block is provided, which is a Map<FeatureId, List<DefaultBlock>>. This provides a way of providing app specific configuration to already built components.

A list of DefaultBlocks is the same way feature defaulting, and channel specific defaulting works when specifying a feature.

import:
    - path:    ../Accounts/nimbus.fml.yaml
      channel: production
      features:
        accounts:
            - value:
                button-color: blue

The value used by the imported code will come from the importing code.

More

Additional feature specific configuration​

In order to drive a better experience for experimenter (the Nimbus web server), a number of extra optional fields are needed. See Providing feature metadata for more.

Feature co-enrollment​

A feature which allows co-enrollment allows a client to be enrolled in any number of experiments/rollouts for that feature. See Co-enrolling Features for more information.

Feature variables configured by preferences​

Some feature variables may be optionally driven by preferences (UserDefaults or SharedPrefences). There are some restrictions and nuances here, so see the documentation for more information.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/fml/coenrolling-features/index.html b/fml/coenrolling-features/index.html index 96829d44f..9adce7e29 100644 --- a/fml/coenrolling-features/index.html +++ b/fml/coenrolling-features/index.html @@ -4,13 +4,13 @@ Co-enrolling Features | Experimenter Docs - +

Co-enrolling Features

A feature which allows co-enrollment allows a client to be enrolled in any number of experiments/rollouts for that feature.

Features supporting co-enrollment

  • Messaging (Fenix, Firefox iOS, Focus for Android, Focus for iOS)

How to define a co-enrolling feature​

A feature can be marked as allowing co-enrollment with a boolean flag in its feature definition in a Feature Manifest. For example, in your messaging.fml.yaml file:

features:
  messaging:
    description: |
      The in-app messaging system.
    allow-coenrollment: true
    variables:
      messages:
        description: A growable collection of messages
        type: Map<String, MessageData>
        default: {}

Once your messaging feature is defined as a co-enrolling feature, a client can be enrolled in any number of messaging experiments/rollouts.

Recording exposure​

In order to record exposure for a co-enrolled feature, the string {experiment} needs to exist in the feature config somewhere. In the case of messaging, this would be in the message object:

objects:
  MessageData:
    ...
    fields:
      experiment:
        type: Option<String>
        description: The slug of the experiment that this message came from.
        default: null

This string is then replaced with the experiment slug at enrollment time.

The feature code that the developer is writing needs to get the experiment slug from the feature config and record the exposure with recordExperimentExposure(experimentSlug). In Kotlin, this would look like:

val slug = message.data.experiment ?: return message

messagingFeature.recordExperimentExposure(slug)

Other things to note about co-enrollment​

  • Experiment feature values still take precedence over rollout feature values
  • Enrollment/unenrollment is calculated independently for each experiment/rollout regardless of whether they target the same feature or not
  • Enrollment/unenrollment telemetry is still sent for each experiment/rollout at the time each enrolls or unenrolls
Copyright © 2024 Mozilla Corporation
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Providing feature metadata

The feature manifest defines "features" which are used to:

  • generate type safe code to access configuration specified remotely by Nimbus
  • provide default values for the configuration when not specified remotely by Nimbus
  • provide experiment owners and others insight into the feature via experimenter, the Nimbus web site.

This last item is enabled by providing metadata about each feature in the feature manifest.

A complete example​

    description: |
      The in-app messaging system.

    documentation:
      - name: User documentation
        url: https://experimenter.info/messaging/mobile-messaging
      - name: QA documentation
        url: https://docs.google.com/document/d/555-555/edit?usp=sharing

    meta-bug: https://bugzilla.mozilla.org/show_bug.cgi?id=5555555

    contacts:
      - jhugman@mozilla.com
      - amejiamarmol@mozilla.com
      - twhite@mozilla.com
      - brennie@mozilla.com

    events:
      - https://dictionary.telemetry.mozilla.org/apps/fenix/metrics/messaging_message_shown
      - https://dictionary.telemetry.mozilla.org/apps/fenix/metrics/messaging_message_clicked
      - https://dictionary.telemetry.mozilla.org/apps/fenix/metrics/messaging_message_dismissed
      - https://dictionary.telemetry.mozilla.org/apps/fenix/metrics/messaging_message_expired
      - https://dictionary.telemetry.mozilla.org/apps/fenix/metrics/messaging_malformed

    configurator: https://mozilla.github.io/limelight/

description​

This is a short description that appears alongside the name in experimenter.

It also is used to generate in-line class documentation in generated code.

documentation​

This is a list of named links to documentation for the feature elsewhere.

Documentation might be for experiment owners to read, engineers to read, or QA.

Documentation for each variable and field is asked for in the feature manifest; for simpler features, this may be sufficient as experiment owner documentation, however, for more complex features a more cohesive document for a less technical audience may be more appropriate.

QA documentation may already exist for the feature, or it might be created from an existing template.

Minimally, QA documentation should include:

  • how to manipulate the app/feature/configuration so that the app shows the feature,
  • how to manipulate the app/feature/configuration so it emits the exposure event for an experiment,
  • how to manipulate the app/feature/configuration so each event listed in the events section is emitted.

If this list is empty, then nimbus-fml validate will warn that the feature is missing documentation.

contacts​

This is a list of email addresses of engineers who have worked on the feature, and feature owners who have responsibility for the feature.

These should have valid Jira accounts and will be attached to QA tickets filed for any experiments involving this feature.

This is to ensure fast turn around of experiments and resolving question that arise in these QA tickets.

If this list is empty, then nimbus-fml validate will warn that the feature is missing contacts.

meta-bug​

This is a URL where bugs should be filed against the feature. This may be a metabug for the feature, or an Epic, or a Jira CreateIssue.jspx link. The primary function of this URL is a place where QA can file bugs found with this feature, on an ongoing basis.

If this is missing, then nimbus-fml validate will warn that the feature is missing a meta-bug.

events​

This is an optional list of events in the Glean dictionary that this feature emits.

configurator​

This is an optional link to a feature specific web app for generating valid configurations. This is likely only useful for very large or complex feature configurations (e.g. Glean, messagaing, onboarding).

Copyright © 2024 Mozilla Corporation
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Commands

Generating code for an mobile application​

The simplest invocation is in this form:

% nimbus-fml generate --language <LANGUAGE> --channel <CHANNEL> <INPUT> <OUTPUT>
  • LANGUAGE can be kotlin or swift.
  • CHANNEL is one of the channels specified in the FML file.
  • INPUT may be a local file or directory.
  • OUTPUT should be a local file or directory.

If INPUT is a directory, it is recursively searches for any files with names ending in .fml.yaml.

This command should be called at build time of components and the build time of the application.

Adding repo-files​

Adding repo files can be done with one or more --repo-file argument.

% nimbus-fml generate --language <LANGUAGE> --repo-file ./app-structure.json <INPUT> <OUTPUT>

Getting different versions of the same manifest​

For remote manifests, the default branch to fetch from is main. This can be changed with the --ref option.

Using a ref is equivalent to taking the remote repo of the manifest path and creating a repo-file with that repo mapped to the given ref.

This command uses feature manifest from the releases_v118 branch of the https://github.com/mozilla-mobile/firefox-android repository:

% nimbus-fml generate --language <LANGUAGE> --ref releases_v118 @mozilla-mobile/firefox-android/fenix/app/nimbus.fml.yaml <OUTPUT>

Caching remote files​

% nimbus-fml generate --language <LANGUAGE> --channel CHANNEL --cache-dir ./build/cache-dir <INPUT> <OUTPUT>

For include and import directives in the FML, a remote file may be referenced. The cache directory is a local cache of these remote files.

For each of the following, --ref, --cache-dir and --repo-file flags are supported.

Generating a manifest file for experimenter​

The reason to use a custom manifest format a language specific JSON serialization library is to do with keeping the code in sync with the experimenter web server.

For legacy reasons, this is a different format of yaml file, which should be checked in to version control and is periodically pulled in by experimenter.

% nimbus-fml generate-experimenter <INPUT> <OUTPUT>

This should only be called at the application level, with the one main fml file as an input.

--cache-dir, --ref, and --repo-file arguments are also supported.

Validating a manifest file​

% nimbus-fml validate <INPUT>

This gives a view of the validation process for all channels.

% nimbus-fml validate @mozilla-mobile/firefox-android/fenix/app/nimbus.fml.yaml

gives

ℹ️ Loaded modules:
- https://raw.githubusercontent.com/mozilla-mobile/firefox-android/main/fenix/app/onboarding.fml.yaml
- https://raw.githubusercontent.com/mozilla-mobile/firefox-android/main/fenix/app/nimbus.fml.yaml
- https://raw.githubusercontent.com/mozilla-mobile/firefox-android/main/fenix/app/pbm.fml.yaml
- https://raw.githubusercontent.com/mozilla-mobile/firefox-android/main/android-components/components/browser/engine-gecko/geckoview.fml.yaml
- https://raw.githubusercontent.com/mozilla-mobile/firefox-android/main/android-components/components/service/nimbus/messaging.fml.yaml

Validating manifest for different channels:
✅ release.............valid
✅ beta................valid
✅ nightly.............valid
✅ developer...........valid

Re-write a complicated distributed manifest into a single file​

The FML spec allows you to import and include dependencies' manifests. This is convenient for engineers to place the feature manifest close to where the feature code lives.

However, for some circumstances, it may not be good for packagers or distributers of the software: e.g. if the file is to be distributed with the software.

To help this, the single-file command is used to merge all imports and includes into the top level file, with a single channel.

% nimbus-fml single-file --channel release @mozilla-mobile/firefox-android/fenix/app/nimbus.fml.yaml single-file.fml.yaml

Generating a machine readable overview of a feature​

% nimbus-fml info [--channel CHANNEL] [--json] <MANIFEST> [--feature FEATURE]

This prints a simplified YAML or JSON representation for each feature. e.g.

{
  "file": "https://raw.githubusercontent.com/mozilla-mobile/firefox-android/main/fenix/app/nimbus.fml.yaml",
  "features": {
    "homescreen": {
      "description": "The homescreen that the user goes to when they press home or new tab.",
      "types": [
        "Boolean",
        "HomeScreenSection",
        "Map<HomeScreenSection, Boolean>"
      ],
      "hashes": {
        "schema": "7a15570b",
        "defaults": "423ab0bd"
      }
    }
  }
}

Hashes​

The hashes object shows truncated SHA256 hashes for:

  • the schema (the types, variable names, the enum variants available)
  • the defaults (the default values of the feature variables)
tip
  • Changes in the schema hash might indicate a change in the feature code.
  • Changes in the defaults hash would indicate a different configuration being used for the same code.
  • Changes in the schema hash will almost always be accompanied by a change in the defaults hash, with the exception of changing variable types from a String type to a string-alias type (or vice-versa).

Types​

The types list is a sorted list of the types involved used to define the feature. This list may be used as a proxy for a measure of complexity of the feature configuration space.

Feature metadata​

In addition, the feature metadata is also displayed.

If a --feature argument is supplied, then restrict the output to just this feature.

If a --channel argument is supplied, then use this channel. This will affect the defaults hashes.

Using info with JQ

You can use --json JQ to output some interesting data:

Get the features for this app:

% nimbus-fml info <INPUT> --json | jq '.features|keys'

Get the channels for this app:

% nimbus-fml channels <INPUT> --json

Get the hashes for just the homescreen feature:

% nimbus-fml info <INPUT> --json | jq '.features.homescreen.hashes'

--cache-dir, --ref and --repo-file arguments are also supported.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/fml/fml-front-end-format/index.html b/fml/fml-front-end-format/index.html index 48b89a21f..dc2bec851 100644 --- a/fml/fml-front-end-format/index.html +++ b/fml/fml-front-end-format/index.html @@ -4,13 +4,13 @@ Feature Manifest Language Front-end Format As YAML | Experimenter Docs - +

Feature Manifest Language Front-end Format As YAML

  • Status: accepted
  • Deciders: @travis79, @jhugman, @teshaq, @k88hudson
  • Date: 2021-10-26

Context and problem statement​

In order to allow product teams to define experimentable application features, there needs to be a manifest file which defines these in a way that is understandable to the Nimbus ecosystem. The feature manifest should define data types used in the application code to configure features. It should also be able to define a complete default configuration for each application feature.

The purpose of this document is to define the decision on the format of the feature manifest but not be a full specification of the schema or language of it.

Decision drivers​

  • It should not be onerous to write and maintain for a junior engineer or intern
  • A single feature should be readable/guessable by a product owner or designer
  • Once written, it should be easy to navigate and read, even if there are many features
  • It should map easily to Kotlin, Swift (and Rust)
  • No algebraic types
  • No inheritance / polymorphism
  • Defaults should be specifiable at the property level
  • Defaults should be specifiable at the feature level
  • Alignment with Desktop Nimbus which is already using a feature manifest, in the hopes that the schema can be shared between mobile and desktop eventually

Decision outcome​

The front-end format for the Nimbus manifests will follow the YAML format.

Pros:

  • More concise, and less likely to have errors due to formatting such as missing a bracket or comma
  • Support for comments within the document
  • A schema can be defined using JSONSchema for the purposes of validation
  • YAML is a superset of JSON, so any existing JSON should be able to be parsed by the YAML parser
  • JSON can be embedded within YAML
  • Support for multiple documents within one file, so each feature could be a separate "document", simplifying and flattening the schema
  • Ease of migration for Desktop from JavaScript objects to YAML objects

Cons:

  • Yet another data representation language to deal with
  • Readability over other formats is marginal

Examples​

To illustrate the YAML format, here is an example of the "homescreen" feature, first in JSON, then the same representation in YAML.

JSON Example​

This example is only intended to illustrate the structure of the data, and not serve as an example of the suggested format. See the YAML example below for the suggested format.

{
  "enums": [
    {
      "name": "SectionId",
      "doc": "The sections of the homescreen",
      "variants": [
        {
          "name": "top-sites",
          "doc": "The original frecency sorted sites"
        },
        {
          "name": "jump-back-in",
          "doc": "Jump back in section"
        },
        {
          "name": "recently-saved",
          "doc": "Tabs that have been bookmarked recently"
        }
      ]
    }
  ],
  "objects": [],
  "hints": {},
  "features": [
    {
      "name": "homescreen",
      "doc": "Represents the homescreen feature",
      "props": [
        {
          "name": "sections-enabled",
          "doc": "A map of booleans",
          "type": {
            "EnumMap": [
              {
                "Enum": "HomeScreenSection"
              },
              "Boolean"
            ]
          },
          "default": {
            "jump-back-in": false,
            "recently-saved": false,
            "top-sites": true
          }
        }
      ],
      "default": null
    }
  ]
}

YAML Example​

This example is meant to illustrate the concise format and readability of YAML.

---
# Define your enumerations here
enums:
  SectionId:
    description: The sections of the homescreen
    variants:
      top-sites:
        description: The original frecency sorted sites
      jump-back-in:
        description: Jump back in section
      recently-saved:
        description: Tabs that have been bookmarked recently
# Define your features here
features:
  homescreen:
    description: Represents the homescreen feature
    variables:
      sections-enabled:
        description: A map of booleans
        variable_type: Map<Enum(SectionId), Boolean>
        default: |
          [
            "top-sites": "true"
            "jump-back-in": "false"
            "recently-saved": "false"
          ]
    default: ~

Other considered options​

Option 1 - JSON​

JSON is still technically supported since YAML is a superset of JSON.

Option 2 - JavaScript/TypeScript​

JavaScript and TypeScript parser crates are harder to find and appear less supported in Rust, and using a full-blown javascript engine seems like overkill.

Additional considerations​

  • It should be easy to copy a winning branch of an experiment or rollout back into the feature manifest
  • Reference to bundled text and icons should allow for multiple choice in the manifest
  • An app may consist of multiple projects
  • The format/schema/grammar of the feature manifest language should be documented in the nimbus-shared repo
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/fml/fml-imports-and-includes/index.html b/fml/fml-imports-and-includes/index.html index 236995772..017cf4715 100644 --- a/fml/fml-imports-and-includes/index.html +++ b/fml/fml-imports-and-includes/index.html @@ -4,13 +4,13 @@ Componentizing the Nimbus Feature Manifest | Experimenter Docs - +

Componentizing the Nimbus Feature Manifest

In the first iteration of the nimbus-fml, the tooling was only able to work on one file at a time.

It accepted a nimbus.fml.yaml file, and outputed a Kotlin or Swift file for inclusion in the product, or an .experimenter.yaml file for ingestion into Experimenter.

The pattern we have seen is that the application specifies code that:

  • grows each time a new feature is added. This makes it hard to navigate.
  • is only able to generate code app code, which is not usable in any of the app's components.

This second restriction is most problematic: the app's components (e.g. Fenix is made up of Android Components, Application Services, GeckoView and Gecko) are unable to instrument their code for experimentation, even though they by themselves might make up the majority of the codebase of the app.

This document discusses three new blocks in the FML specification, which are to implement including and importing FML files which easily solve these problems.

It allows for experimentation in the re-usable and library codebases that makes up most of our applications.

It enables cross-platform re-use of data definitions and schemas which will in turn, make it easier to reason about experimental features by experiment owners.

Goals​

  • The nimbus.fml.yaml file should be composable such the monolithic file can be broken up in to smaller pieces, potentially to live closer to the code it's configuring.
  • The .experimenter.yaml ingested by Experimenter should give a complete picture of all the features available in the application.
  • The components (and their fml.yaml files) can live in a different repository to the application.
  • The components' feature code generation happens at component compile time
  • Generated feature code should be visible and configurable from the application
    • e.g. Fenix and Focus might use the same feature from Android Components, but require different configuration.
  • versioning and branches:
    • experiments are launched at a population of users using different versions of the app.
    • each specific version of the app is potentially built with a specific version of their components
    • for this proposal, easily varying the versions of components is not explored.
  • local development
    • for some feature development, working in multiple repos at the same time is necessary.
    • for this proposal, easily varying the paths or URLs of components is not explored, although this will likely involve a config file.

Non-goals​

  • namespacing
    • currently names of feature, object, and enum classes are unique; collisions are disallowed
    • for this proposal, no attempt is made to allow classes of the same name to be used.
  • connectors to different languages
    • some features may be written in a different language or context to the one that the application is written in: e.g. C++, Rust or JS, or in an iOS app-extension.
    • for this proposal, this is only lightly discussed.

Concepts​

This proposal changes the way the nimbus-fml file is invoked, and adds three new structures to the Feature Manifest Language spec.

Invoking the nimbus-fml command​

The following nimbus-fml commands can be run in fml files in either applications or components.

Each component or app has its own build.gradle.

The following command is assembled and run by the Nimbus Gradle Plugin.

% nimbus-fml generate --language kotlin --channel release component.fml.yaml components/build/generated/debug/nimbus

This will generated exactly one Kotlin file.

The following command is assembled and run at application build time only.

% nimbus-fml generate-experimenter application.fml.yaml .experimenter.yaml

The .experimenter.yaml file is checked into source control, and ingested by Experimenter.

The about block​

The about block is an optional block in any given .fml.yaml file.

If present:

  • the file may be used to generate a Swift or Kotlin file.
  • the file may be imported by another .fml.yaml file.

If not present:

  • the file may be included in another .fml.yaml file.

about associates a particular .fml.yaml file what Kotlin/Swift class and package/module this file will generate:

about:
    android:
        class:   .nimbus.MyNimbus
        package: mozilla.components.search

The package property must correspond to the app's namespace, where the R and BuildConfig files are generated.

The class property is the qualified name of the class that will be generated. If the class has . as a prefix, the fully qualified name is created by appending the package to the class. The filename is taken to be the final segment of the fully qualified name.

For Android developers, this should be the familiar way android:name is specified in the AndroidManifest.xml.

As with existing .fml.yaml files, any file with an about block, must contain a channels list. e.g.

channels:
    - release
    - beta
    - developer
    - testing

The include list​

This is a list of files which will be merged with this one. The files may be relative to this one, absolute or URLs.

include:
    - nimbus/search.yaml
    - @mozilla/nimbus-shared/fml/messaging.yaml

Each element in the list is a path or URL to another file to be included into this one.

Included files:

  • must not contain an about block,
  • must not contain a channels list, or must match the channels list of the including file.

Including a file means that contents of the features, objects and enums blocks will be appended from the included file to the including file. Any collisions will cause an error, i.e. if two files declare a type of the same name, this should cause an error.

Included files may include other files. These files may be remote or on a local filesystem.

Including the same file twice should be a no-op.

Included files may import files. These files may be remote or on a local filesystem.

The import list​

import:
    - path:    ../Accounts/nimbus.fml.yaml
      channel: production
    - path:    @mozilla-mobile/ios-components/components/feature/search/nimbus.fml.yaml
      channel: release

The list contains blocks with the following mandatory properties:

  • path: this string value is a relative path or URL to the imported file.
  • channel: this string value is the name of a channel. The channel must be in the channel list of the included file.

An imported file:

  • must have an about block.
  • may have an include block.

Optionally, a features block is provided, which is a Map<FeatureId, List<DefaultBlock>>. This provides a way of configuring components.

A list of DefaultBlocks is the same way feature defaulting, and channel specific defaulting works when specifying a feature.

import:
    - path:    ../Accounts/nimbus.fml.yaml
      channel: production
      features:
        accounts:
            - value:
                button-color: blue

The value used by the imported code will come from the importing code.

Implementation notes​

Illustrative sketch​

Consider two projects that are already linked: the app, and the components. One of the components is a messaging feature which we'd like to be able to use in the app.

The app needs to initialize the messaging component with specific configuration, not available to the component when it was compiled.

Two FML files​

In ../components/messaging.fml.yaml

about:
    class:   .nimbus.ComponentNimbus
    package: org.example.components
channels:
    - testing
    - staging
    - production

features:
    messaging:
        # definition elided, for clarity
        # We include one variable for illustration
        variable:
            triggers:
                type: Map<String, String>
        defaults:
            - channel: production
              value:
                triggers:
                    ALWAYS: "true"
                    NEVER:  "false"

The top level file, app.fml.yaml.

about:
    class:   .nimbus.AppNimbus
    package: org.example.app
channels:
    - developer
    - nightly
    - beta
    - release
import:
    - path:    ../components/messaging.fml.yaml
      channel: production
      features:
        messaging:
            - channel: release
              value:
                I_AM_DEFAULT_BROWSER:     is_default_browser == true
                I_AM_NOT_DEFAULT_BROWSER: is_default_browser != true

The corresponding Kotlin files​

Running nimbus-fml on messaging.fml.yaml in the components directory, with the channel as production generates:

package org.example.components.nimbus

import org.example.components.R

class ComponentNimbus {
    var api: NimbusFeaturesInterface?

    …
}

class Messaging
    private constructor(…) {
    constructor(
        _variables: Variables = NullVariables.instance,
        triggers: Map<String, String> = mapOf(
            "ALWAYS" to "true",
            "NEVER" to "false"
        )
    )
}

When running the nimbus-fml command with the release channel, the component file is imported.

The generated file looks like:

package org.example.app.nimbus

import org.example.component.ComponentNimbus
import org.example.app.R

class AppNimbus {
    var api: NimbusFeaturesInterface? = null
        set(value) {
            ComponentNimbus.api = value
        }

    companion object {
        init() {
            ComponentNimbus.features.messaging.withConfiguration { _variables ->
                Messaging(
                    _variables,
                    triggers = mapOf(
                        "ALWAYS" to "true",
                        "NEVER" to "false",
                        "I_AM_DEFAULT_BROWSER" to "is_default_browser == true",
                        "I_AM_NOT_DEFAULT_BROWSER" to "is_default_browser != true"
                    )
                )
            }
        }
    }
}

Several places to highlight in this code:

  • The class names and package names are gained from the about blocks of the imported files.
  • The api property connects the Nimbus SDK to the generated code. Setting this lets the AppNimbus get configuration from the server. The setter now sets the api all imported (and generated above) classes— in this case ComponentNimbus.
  • The companion object init block calls withConfiguration as soon as AppNimbus is used. withConfiguration is a new method on FeatureHolder which provides an alternative create closure. It has to be public and is named as to appear after value() in the list auto-completed identifiers offered by IDEs. The new configuration comes from merging the component default with the app default.

So when ComponentNimbus.features.messaging.value() is called, even from within the component itself, it returns configuration from the Nimbus SDK, and defaults from the app.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/fml/fml-paths/index.html b/fml/fml-paths/index.html index 065ebb5f8..468ca1176 100644 --- a/fml/fml-paths/index.html +++ b/fml/fml-paths/index.html @@ -4,13 +4,13 @@ Using paths in FML | Experimenter Docs - +

Paths and URLs

In several places paths to link one .fml.yaml file to another.

For convenience, we should use the conventions around URLs used in Carthage and npm package managers:

  • a relative path must use / as a separator
  • a relative path may use ./ and ../ in their prefixes
  • an absolute URL may be used, but this must start with https://
  • a Github repository may be specified with a prefix of @.
    • e.g. @mozilla/nimbus-shared expands to https://raw.githubusercontent.com/mozilla/nimbus-shared/main/

Hint: The rules of the URL construction would be a good place to consider local development and branches.

When invoked in conjunction with the repo-file argument, these @ paths may be overriden:

Remapping @ prefixes with repo-files​

nimbus-fml generate --repo-file v102.0.json input.fml.yaml output-dir

A v102.0.json may look like:

{
  "mozilla-mobile/android-components": "releases/102.0",
  "mozilla/application-services": "v93.1.0"
}

A path to @mozilla-mobile/android-components/components/messaging/nimbus.fml.yaml would resolve to https://raw.githubusercontent.com/mozilla-mobile/android-components/releases/102.0/components/messaging/nimbus.fml.yaml.

Defining local paths are useful for working with multiple repos at a time. A local-dependencies.json may look like this:

{
  "mozilla-mobile/android-components": "../android-components",
  "mozilla/application-services": "../application-services"
}

Defining local paths is also useful for imposing structure on a complicated filesystem. e.g. an app-structure.json file may look like

{
  "my/messaging": "./Messaging/nimbus-manifests",
  "my/onboarding": "./Onboarding/nimbus-manifests"
}

Multiple repo files​

The nimbus-fml command line can accept multiple repo-files at a time, resolving @ links as it goes.

nimbus-fml generate --repo-file ./app-structure.json --repo-file @mozilla-mobile/dependency-versions/v102.0.json --language swift ./input.fml.yaml output-dir

The fetch command​

The FML command line interface includes a fetch command to help build intuition around how these paths work.

nimbus-fml fetch @mozilla/application-services/Cargo.toml

It takes a single INPUT file, and any number of --repo-file options and outputs the file (if found) to stdout.

In this case, the file will be from https://raw.githubusercontent.com/mozilla/application-services/main/Cargo.toml

Using the repo file from above, the command

nimbus-fml fetch --repo-file v102.0.json @mozilla/application-services/Cargo.toml

would fetch from https://raw.githubusercontent.com/mozilla/application-services/v93.1.0/Cargo.toml

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/fml/string-alias/index.html b/fml/string-alias/index.html index df2ee59f5..2c51bf607 100644 --- a/fml/string-alias/index.html +++ b/fml/string-alias/index.html @@ -4,13 +4,13 @@ Using string alias | Experimenter Docs - +

Using string alias

string-alias is a type alias annotations for feature variables in the feature manifest language. It defines a named set of strings which can be used and validated elsewhere in the feature manifest.

It is named as a special case of typealiasing found in many languages.

typealias QueryName = String
val queries = mapOf<QueryName, String>()

In this kotlin example above, we are able to use QueryName wherever we're able to use String, and vice versa: there is nothing else linking QueryName with queries.

string-alias defines a named set of valid strings​

In FML, the string-alias belongs to the variable definition.

    queries:
        string-alias: QueryName
        type: Map<QueryName, String>
        default:
            ALWAYS: 'true'

Here, QueryName is defined as the set of Strings that are keys in the queries map.

In the example above, we're defining a map of named queries. The default has one entry in.

Where QueryName is used again, its value is checked against this membership test, by the FML:

    available-if:
        type: QueryName
        default: ALWAYS

Note that had the queries map been empty, we could not have provided a default value for available-if.

Now that the QueryName string-alias has been defined, it can be used in conjunction with any structural type definition for example:

    available-if:
        type: Option<QueryName>
        default: null

This means that available-if can be either a valid QueryName or null.

    available-if:
        type: List<QueryName>
        default: []

This means that available-if can be a list of valid QueryName strings.

The named set is used to validate strings by experimenter​

Over time, the number of queries can grow in the FML:

    queries:
        string-alias: QueryName
        type: Map<QueryName, String>
        default:
            ALWAYS: 'true'
            USER_RECENTLY_INSTALLED: days_since_install < 7
            USER_EN_SPEAKER: 'en' in locale
            USER_DE_SPEAKER: 'de' in locale

Defining QueryName allows experimenter to validate a feature configuration before it reaches the application:

{
    "available-if": [
        "USER_RECENTLY_INSTALLED",
        "USER_ES_SPEAKER"
    ]
}

In the above example, experimenter shows the user an error:

Invalid value "USER_ES_SPEAKER" for type QueryName; did you mean one of "ALWAYS", "USER_DE_SPEAKER", "USER_EN_SPEAKER" or "USER_RECENTLY_INSTALLED"?

The named set can be added to by FML authors or experiment owners​

This can be fixed by adding a query to the queries map in the FML file or the user can add it directly in the feature configuration:

{
    "queries": {
        "USER_ES_SPEAKER": "'es' in locale"
    },
    "available-if": [
        "USER_RECENTLY_INSTALLED",
        "USER_ES_SPEAKER"
    ]
}

Defining the named set of valid strings​

We've seen how a string-alias can be used, and how QueryName was defined as a key in a map.

The valid set of strings can be defined by any existing structural types. Some contrived examples follow:

    surfaces:
        type: List<SurfaceName>
        string-alias: SurfaceName
        default: []

Any use of SurfaceName must be contained in the list of surfaces. This may be used as an alternative to an enum, used in a library, but whose variants are defined in an app, thereby breaking the compile-time dependency from library to app.

    experiment-slug:
        type: ExperimentSlug
        string-alias: ExperimentSlug
        default: '{experiment}'

Any use of ExperimentSlug must be the default value. In conjunction with an Option<> at the usage site, this lets us specify either an exact value or null.

For completeness, the string alias named set can be defined as:

  • MyStringAlias: a single value
  • Map<MyStringAlias, _>: keys in a map
  • Map<_, MyStringAlias>: values in a map
  • List<MyStringAlias>: items in a list
  • Option<MyStringAliase>: an option
  • Map<_, List<StringAlias>>: combinations of these structural types.
Restriction

Only one string-alias can be defined per feature variable. The following– using one variable to define two named sets of strings– is not possible at this time.

    available-events:
        string-alias: EventCategory, EventName
        type: Map<EventCategory, List<EventName>>

String aliases can be used in nested objects​

features:
    my-onboarding-feature:
        variables:
            queries:
                type: Map<QueryName>
                string-alias: QueryName
                default: {}
            cards:
                type: Map<CardKey, CardData>
                string-alias: CardKey
objects:
    CardData:
        fields:
            exclude-if:
                type: List<QueryName>
                default: []
            include-if:
                type: List<QueryName>
                default: []
Restriction

String-alias can only be defined in a feature variable. The object can only be used, directly or indirectly, by a feature which defines the string-aliases it uses.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/fml/unimplemented/index.html b/fml/unimplemented/index.html index 4ba53c803..65d51dbbf 100644 --- a/fml/unimplemented/index.html +++ b/fml/unimplemented/index.html @@ -4,14 +4,14 @@ A history of considered changes for Feature Manifest Language | Experimenter Docs - +

A history of considered changes for Feature Manifest Language

Future specifications​

This document houses a number of proposals not ready for comment. They are not linked to from the spec.

Feature defaults and runtime targeting​

⚠️ Unimplemented. This is intended to use the same JEXL targeting used elsewhere in Nimbus SDK.

Occasionally, we would like to vary a configuration based upon the device. In this example, the homepage feature has sections for both Pocket and Topsites.

features:
  homepage:
    variables:
      pocket-enabled:
        description: If true, show stories from Pocket. This is only available in certain territories.
        type: Bool
        default: false
      topsites-enabled:
        description: If true, show tiles from the users most recent and frequently visited pages.
        type: Bool
        default: false
    defaults:
      - channel: nightly
        value: { topsites-enabled: true }
      - channel: nightly
        targeting: locale in ['en-US', 'de-DE', 'en-GB']
        value: { pocket-enabled: true }

In this example, pocket-enabled defaults to true only on the nightly channel and when the device locale is in US, DE or GB.

Types coerced from String​

We have already seen enums being declared in the types section of the manifest.

Other types that may be derived from String:

  • Color
  • URL

Variables and fields with no defaults​

Required fields​

Some object fields do not have a sensible default value, but without them, the object itself doesn't make sense.

For example a MessageItem doesn't make sense unless it has a message and a deeplink. Where any required fields are missing from the JSON, the MessageItem is incomplete, and cannot be used.

Incomplete objects cannot be included in maps, lists or optional types.

In this case, a message surface is added to the new-tab, but isn't always needed to be displayed.

features:
  new-tab:
    variables:
      message:
        description: An optional message
        type: MessageItem?
        default: null
types:
  MessageItem:
    description: …
    fields:
      label:
        type: String
        required: true
      deeplink:
        type: String
        required: true

In these cases, the app code must deal with the cases with objects that are said to be failable, because there are no defaults.

let newTabConfig = FxNimbus.features.newTab.value()

if let message = newTabConfig.message {
    // We have to check that the message exists, because the message may not exist.
    displayMessage(message.label, message.deeplink)
}

// continue configuring the new tab.

Failable features​

Feature variables may also be marked as required. In the cases where these variables are missing, then the whole feature no longer makes sense.

Features with required variables must be marked as failable.

features:
  emergency-startup-message:
    failable: true
    description: A message displayed to the user at start up.
      variables:
        message-content:
          description: The long form message to be displayed to the user
          type: String
          required: true
        color:
          description: The background color of the screen
          type: String
          default: red

Failable features are implemented as Optional. If the feature is incomplete or failed, then it is returned as nil or null.

guard let message = FxNimbus.features.emergencyStartupMessage else {
    // phew, there is no emergency message.
    return
}

Features with required variables which should be marked failable but aren't will produce an error.

Imports and Includes​

Unclear/scope–creep/YAGNI​

  • The importing .fml.yaml file should not have access to types from the imported one.
  • The imported file may not have an import list.

Eventually it might be necessary to have imported files recursively importing other files. Implementing this proposal will give an idea of how much extra complexity is required.

Connecting to different languages or contexts​

The import block does two different roles:

  1. Giving experimenter knowledge of the imported features.
  2. Sending experimental configuration from the application singleton into the components.

For features written in a different language, the feature configuration needs to travel across an FFI.

import:
    path:      @mozilla/gecko-dev/fenix.fml.yaml
    channel:   production
    connector: gecko-view
    features:
        autofill:
            - value: {}

Imports for the same connector are gathered up so we can get a Map<String, JSONObject> containing configuration from the experiment (and the app-specific defaults) by calling:

FxNimbus.connectors.geckoView.value()

The app will need to send this to the right place, for example:

val nimbus = Nimbus.shared
nimbus.register(object : Nimbus.Observer {
    fun onExperimentsApplied() {
        val features: Map<String, JSONObject> = FxNimbus.connectors.geckoView.value()
        geckoView.setNimbusFeatures(features)
    }
})
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/fml/using-prefs/index.html b/fml/using-prefs/index.html index 1e9719358..d22bd2534 100644 --- a/fml/using-prefs/index.html +++ b/fml/using-prefs/index.html @@ -4,13 +4,13 @@ Using prefs to override feature variables | Experimenter Docs - +

Using prefs to override feature variables

This page details adding pref-key to a feature definition. This cause the FML generated code to check the user preferences (UserDefaults or SharedPrefences) before checking the Nimbus configuration store or the default.

Setting up​

The app's preferences object needs to be added to the NimbusBuilder call:

NimbusBuilder()
    .with(userDefaults: UserDefaults.standard) // or alternative
    .build(…)

Without this line, the default value used is UserDefaults.standard.

These should be readable by Nimbus, but writeable by the rest of the app. For best effect, these should be the same preferences that drive the Settings screens.

Once the preference object is available to nimbus, you can add pref-keys to feature variables.

Adding a pref-key to a feature variable definitions​

The pref-key can be specified by top level feature variables:

features:
    sample-feature:
        variables:
            is-enabled:
                type: Boolean
                default: false
                pref-key: sample-feature.isEnabled

The generated API is used in the same way as without the pref-key:

let feature = FxNimbus.shared.features.sampleFeature.value()
if feature.isEnabled {
    // Do something because the feature has been enabled.
}

However, now, the call to feature.isEnabled is overridden by the preference held at sample-feature.isEnabled.

This is available for types that are supported by preferences: Boolean, Int, String and Text.

Generated code sketch​

The generated code looks approximately like:

let isEnabled: Boolean {
    return prefs.getBoolean("sample-feature.isEnabled") ??
        json.getBoolean("is-enabled") ??
        defaults.isEnabled
}

Without the pref-key:

let isEnabled: Boolean {
    return json.getBoolean("is-enabled") ??
        defaults.isEnabled
}

Limitations​

This is available for feature variables (not nested Objects fields), and only for scalar types.

It is not available for structural types (i.e. Option<T>, List<T> or Map<String, T>).

Feedback welcome

If there is demand for it, then support for these types and places will be considered.

Similarly, we're looking for feedback on how to make this API or generated code better.

Additional effects of experiment events​

If the user sets any of the named preferences for the feature, then the feature is said to be user-modified.

This is exposed in the isModified() method of the feature.

User-modified features will not emit exposure events.

experimental

Question: Should user-modification be allowed for the general population? i.e. can we add these toggles to a public facing settings screen?

Answer: Currently, it is recommended that user-modification should be exposed only in Secret Settings screens.

This is not suitable as building an opt out of all experiments involving the feature.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/for-leadership/index.html b/for-leadership/index.html index dc6269997..dec84cc75 100644 --- a/for-leadership/index.html +++ b/for-leadership/index.html @@ -4,13 +4,13 @@ Leadership | Experimenter Docs - +

Leadership

How can leadership interact with experiments?​

  • Nimbus Console is the best place for exploring what is running, completed, and in draft. Currently there is basic filtering - which will be expanded this year to enable easier experiment discovery.

  • Each experiment should have an Experiment Brief following this template, as a link from Nimbus Console - which goes into the background, learning goals, and experiment design.

  • After an experiment is completed, there is a Results link in the right hand vertical gray bar. image

  • For completed experiments, there should be information added to Nimbus Console in the "Takeaways" section for a high level tl;dr interpretations and actions taken for each experiment. If that is blank - ask the Product Manager for the takeaways and learnings and to fill that section. image

Where do I find Experimentation Program metrics?​

The Experiment Tracking spreadsheet is the current source of truth on experimentation program metrics.

  • This is updated monthly before Product Days
  • The summary view has the SPI metrics that are being reported out regularly.
  • Tabs for "by feature" and "by area" information in graph format for experiments based on feature area and area (desktop, mobile, platform).

Until we improve discovery in the Console - the 2022 data sheet tab is a possible hack to discover experiments. There are comments at the top cell of each column explaining what is being captured.

  • Please do not edit it.
  • Example: you can find experiments that had a "CDOU Stat Sig" and "Leading Indicator Stat Sig" change. to easily find experiments that saw a stat sig change.
  • Ask Shell, Jim Thomas, Devyani, or Daniel Berry for help interpreting anything in that spreadsheet.
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/for-product/index.html b/for-product/index.html index bba666752..29fbb8b14 100644 --- a/for-product/index.html +++ b/for-product/index.html @@ -4,14 +4,14 @@ Product Managers | Experimenter Docs - +

Product Managers

What is the role of Product in experimentation?​

Product Managers typically have the role of experiment owner in the experimentation workflow. Responsibilities include:

  • Watching training for new experiment owners here - the slide deck in the video is here
  • Starting and completing the experiment brief - which aligns everyone supporting the experiment to the learning goals.
  • Align on the design of the experiment by attending a desktop or mobile office hour and filing a data org Jira ticket.
  • Creating the Data Org Jira ticket (linked in experiment brief)
  • Implementing the experiment in Nimbus console AKA Experimenter
  • Creating the QA Jira ticket (linked in experiment brief)
  • Launching the experiment, ending enrollment, ending experiment (the experimentation workflow)
  • Working with the experiment team (in slack at #ask-experimenter) to identify a set of reviewers trained and authorized to review experiments in your feature area. The experiment team will train them on how to review.

Where do I start?​

  • Watch the 5 minute video on experiment workflow for an overview of the experiment lifecycle to understand the stages / ordering. Use the Workflow Miro board as your source for current links and guidance.
IMPORTANT

You must watch the training video before writing your first experiment.

  • The training for new experiment owners is here
  • The slide deck in the video is here.
  • Ask any questions in ask-experimenter (you will have questions the first few times). Please ask for a shadow in #ask-experimenter your first time(s) through and someone will hop on zoom to write with you. We are happy to help people through the learning curve.
  • Start by opening the Experiment Brief link. If it is your first Experiment Brief - there is an optional 5 minute "how to video".
  • Fill out your experiment brief. As the Product Manager you are responsible for clearly articulating the product goals and questions that need to be answered. Capture as much as you can. It will improve and evolve as you have conversations with different subject matter experts. The experiment brief is the authoritative source to capture the shared understanding.
    • Referencing one source (the experiment brief) for the verbose experiment background enables the subject matter experts (engineering, QA, experiment tooling, data) to efficiently support the effort and respond to changing requirements.
    • Product Managers will keep editing the experiment brief throughout the lifecycle of the experiment. Capture the decisions as they are made back in your experiment brief. Decisions left in in slack channels, verbal discussions, various notes, jira tickets, etc - increase the likelihood of errors and wasted effort..
    • There is a Checklist section at the end of your Experiment Brief to help you through the steps of your experiment.
  • Link the experiment brief to your experiment when you create your experiment in the Nimbus experiment tool.

Key Resources for Product Managers​

Rather than duplicate links that may later break as the process improves - links to the resources and training videos are in the Workflow Miro Board.

  • There are several weekly touchpoints Office Hours for in person assistance from subject matter experts in different areas.
  • If you are confused on any aspect - that's expected the first few experiments until you've gotten the rhythm. Ask in the #ask-experimenter Slack channel - it is an open community of support from several disciplines.

When shipping product changes: a guide on when to use what option​

Experiment (Experimentation Program)RolloutHoldback
DefinitionExperiment with 2 or more branches (treatment groups). Insights gathered at the end of experiment timeframe. Can be scaled to Rollout.Delivery where you ship 1 change, and that change goes to all the users in the determined population.When you run an experiment (see Experiment column) at the same time as Rollout.
Population and timeframeA fixed set of population for a fixed set of timeframeA fixed set of population at each shipping phase (population size is variable - can be scaled up & down)Can have different population sizes between Rollout and experiment. Time frame of rollout and experiment can differ.
Measure causal DAU impactYesNoYes
See statistical significant insights on guardrail metricsYesNoYes
When to useLooking for a causal relationship between a product change and key business metrics like DAU and 2-week retention. Also when measuring the impact of a potential new feature.Looking to primarily reduce technical risks such as scalability when shipping a product changeLooking to make a product change available immediately to a wide population while monitoring product change impact to business metrics
Delays the speed of making the product change available to a wider population.No insights on key business metrics like DAU, as tooling is not set up to make control and treatment group comparisons.If a product change has detrimental impact to guardrail metrics and DAU, we may or may not be too late in rolling back the changes.
AdvantagesCan measure impact first, then if positive ship the changeCan scale up or down as appropriateFaster than blocking on impact measurements
Experiment brief neededYesNoYes

Example of how to construct a balanced launch plan​

[What Velocity team practices today]

  • First, run a small experiment
    • Goal: Reduce business risk and decide whether to ship
    • Example: 5% of users*; observe DAU impact of 1% to 10%
  • Then, if results of small experiment is positive, ship via rollout + holdback
    • Goal: Measure impact at scale
    • Example: 50% of users, 25% get feature and the other 25% doesn’t; observe DAU impact of 3%-5%

*Note that: 5% can be whatever population you choose to target. Can be en-US, “infrequent users”, “users who haven’t imported bookmarks”, etc.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/getting-started/engineers/for-engineers/index.html b/getting-started/engineers/for-engineers/index.html index fd3e3e4fb..feb05f6ea 100644 --- a/getting-started/engineers/for-engineers/index.html +++ b/getting-started/engineers/for-engineers/index.html @@ -4,13 +4,13 @@ Getting started with implementation | Experimenter Docs - +

Getting started with implementation

Engineers are typically tasked with implementing an experiment on a new surface with an experiment that has been designed by their product manager in partnership with a data scientist.

Integrating with a new app​

To get started with implementation​

  • Review the experiment design document.
  • Determine if you need to implement any additional telemetry collection
  • Check if you need to implement a custom audience
  • Confirm the name of the feature and follow the guide to adding a new feature to the manifest
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/getting-started/engineers/getting-started-for-android-engineers/index.html b/getting-started/engineers/getting-started-for-android-engineers/index.html index 85dd3cdac..79aaf8653 100644 --- a/getting-started/engineers/getting-started-for-android-engineers/index.html +++ b/getting-started/engineers/getting-started-for-android-engineers/index.html @@ -4,14 +4,14 @@ Getting Started for Android Engineers | Experimenter Docs - +

Introduction

Nimbus is an experimentation platform from Mozilla.

This document shows you how to set up the Nimbus SDK with a new Android app. It assumes that your app is already using the Glean SDK and Android Components.

Building with Nimbus

Nimbus is distributed through bundled Rust code as part of Mozilla's Application Services "Megazord".

In app/build.gradle, in the dependencies block, include the implementation line for Nimbus:

dependencies {
    …
    implementation "org.mozilla.appservices:nimbus:${Versions.mozilla_appservices}"
    …
}

Building with the Nimbus FML gradle plugin​

The Feature Manifest Language provides type-safe access to configuration coming out of the Nimbus SDK, and is used to configure your application features, by -generating Kotlin from a Feature Manifest.

The tooling-nimbus-gradle plugin manages the download of the tooling, the generating of the Kotlin code, and is configured by gradle.

In your top-level build.gradle:

buildscript {
    dependencies {
        classpath "org.mozilla.appservices:tooling-nimbus-gradle:${Versions.mozilla_appservices}"
    }
}

and in app/build.gradle:

apply plugin: "org.mozilla.appservices.nimbus-gradle-plugin"

nimbus {
    // The path to the Nimbus feature manifest file
    manifestFile = "nimbus.fml.yaml"

    // Map from the variant name to the channel as experimenter and nimbus understand it.
    // If nimbus's channels were accurately set up well for this project, then this
    // shouldn't be needed.
    channels = [
            debug: "debug",
            nightly: "nightly",
            beta: "beta",
            release: "release",
    ]
    
    // This is generated by the FML and should be checked into git.
    // It will be fetched by Experimenter (the Nimbus experiment website)
    // and used to inform experiment configuration.
    //
    // *NOTE*: This value is optional, and is not necessary when Nimbus is being used
    // as part of a library.
    experimenterManifest = ".experimenter.yaml"
    
    // This is an optional value, and updates the plugin to use a copy of application
    // services. The path should be relative to the root project directory.
    // *NOTE*: This example will not work for all projects, but should work for Fenix, Focus, and Android Components
    applicationServicesDir = gradle.hasProperty('localProperties.autoPublish.application-services.dir') 
        ? gradle.getProperty('localProperties.autoPublish.application-services.dir') : null
}

In this case, it should generate a file named in the nimbus.fml.yaml file. In the case of Fenix, this is called FxNimbus.

The start-up sequence

Before using Nimbus in your Android app, you need to start it.

The Nimbus SDK is a configuration store, making configuration available to the any thread, and — to a first approximation— to be immutable within the same session of the app.

For this reason, we want to be starting the Nimbus SDK as close to the beginning of the start of the app as possible.

In Firefox for Android and Focus for Android, this is done at the beginning of the Application#onCreate() method.

class MyApplication: Application() {

    lateinit var nimbus: NimbusInterface

    override fun onCreate() {
        beginNimbusSetup()


        // do the rest of the set up here.

        finishNimbusSetup()
    }

    fun beginNimbusSetup() {
        Megazord.init()

        nimbus = createNimbus(this, NIMBUS_REMOTE_SETTINGS_ENDPOINT)
    }

    fun finishNimbusSetup() {
        nimbus.fetchExperiments()
    }

    fun createNimbus(context: Context, urlString: String): NimbusInterface {
        val isAppFirstRun = context.settings().isFirstRun
        val customTargetingAttibutes = JSONObject().apply {
            // Put any custom attributes you want to use to segment an audience on to
            // target your experiments.
            put("is_first_run", isAppFirstRun)
        }

        val appInfo = NimbusAppInfo(
            appName = "my-app-name",
            channel = BuildConfig.BUILD_TYPE,
            customTargetingAttributes = customTargetingAttributes
        )

        // Use the Nimbus builder to build a NimbusInterface object.
        return NimbusBuilder(context).apply {
            url = urlString
            errorReporter = { message, e ->
                Logger.error("Nimbus error: $message", e)
            }

        }.build(appInfo)
    }
}

Notes:

  1. Megazord.init() is called before createNimbus().
  2. createNimbus uses a NimbusBuilder to create the Nimbus object.
  3. We build a JSONObject of custom targeting attributes.
  4. The nimbus.fetchExperiments() method is called sometime at or after the app has started.

NimbusBuilder configuration​

Getting errors out of Nimbus​

By design, Nimbus is deliberately unobtrusive; if it fails then it should not crash, but continue as if not enrolled in any experiments.

The errorReporter callback is there to connect Nimbus to any error reporting framework in the rest of the app.

    return NimbusBuilder(context).apply {
        // …
        errorReporter = { message, e ->
            Logger.error("Nimbus error: $message", e)
        }
        // …
    }.build(appInfo)

Connecting the NimbusInterface to FML generated code​

The FML generated code has a runtime dependency on the NimbusInterface.

To connect it to the Nimbus object, we need to tell the NimbusBuilder. In this case, the generated class is FxNimbus.

    return NimbusBuilder(context).apply {
        // …
        // Connect FxNimbus to the Nimbus SDK.
        featureManifest = FxNimbus
        // …
    }.build(appInfo)

Handling First Run experiments​

Since fetchExperiments from the remote settings URL is slow, and we wish to be able have access to the Nimbus experimental configuration as early in start up as possible, Nimbus downloads and caches the experiment recipes on the nth run of the app and only applies them and makes them available to the app at the beginning of the next i.e. the (n + 1)th run of the app.

Astute readers will notice that when n = 0, i.e. the very first time the app is run, there are no experiment recipes downloaded. If Remote Settings experiment recipes JSON payload is available as a raw/ resource, it can be loaded in at first run:

    return NimbusBuilder(context).apply {
        // …
        isFirstRun = isAppFirstRun
        initialExperiments = R.raw.initial_experiments
        timeoutLoadingExperiment = TIME_OUT_LOADING_EXPERIMENT_FROM_DISK_MS // defaults to 200 (ms)
        // …
    }.build(appInfo)

The initial_experiments.json file can be downloaded, either as part of the build, or in an automated/timed job. e.g. this is the Github Action workflow used by Fenix.

Using the experiments preview collection​

The preview collection is a staging area for new experiments to be tested on the device. This should be toggleable via the UI, but should trigger a restart.

Adding the usePreviewCollection flag allows the builder to configure a NimbusInterface object connected to the experiment recipes in the preview collection.

        // Use the Nimbus builder to build a NimbusInterface object.
        return NimbusBuilder(context).apply {
            // …
            usePreviewCollection = context.settings().nimbusUsePreview
            // …
        }.build(appInfo)

A complete NimbusBuilder example​

    return NimbusBuilder(context).apply {
        url = urlString
        errorReporter = { message, e ->
            Logger.error("Nimbus error: $message", e)
        }
        initialExperiments = R.raw.initial_experiments
        usePreviewCollection = context.settings().nimbusUsePreview
        isFirstRun = isAppFirstRun
        sharedPreferences = context.settings().preferences
        // Optional callbacks.
        onCreateCallback = { nimbus ->
            // called when nimbus is set up
        }
        onFetchCallback = {
            // called each time the app fetches experiments
        }
        onApplyCallback = {
            // called each time the applies the fetched experiments.
        }
    }.build(appInfo)

Instrumenting the app for testing​

The nimbus-cli allows QA and engineers to launch the app in different experimental configurations. It largely obviates the need for configuring Nimbus to use the preview collection, above.

To connect the NimbusInterface object to the command line, we need to feed the intent from the app's launch activity to the NimbusInterface.


import org.mozilla.experiments.nimbus.initializeTooling

open class HomeActivity : AppCompatActivity() {)
    override fun onCreate(savedInstanceState: Bundle?) {
        // Find the nimbus singleton
        val app = application as MyApplication
        val nimbus = app.nimbus
        // Pass it the launch intent
        nimbus.initializeTooling(applicationContext, intent)
        // …
    }
Copyright © 2024 Mozilla Corporation
- +generating Kotlin from a Feature Manifest.

The tooling-nimbus-gradle plugin manages the download of the tooling, the generating of the Kotlin code, and is configured by gradle.

In your top-level build.gradle:

buildscript {
    dependencies {
        classpath "org.mozilla.appservices:tooling-nimbus-gradle:${Versions.mozilla_appservices}"
    }
}

and in app/build.gradle:

apply plugin: "org.mozilla.appservices.nimbus-gradle-plugin"

nimbus {
    // The path to the Nimbus feature manifest file
    manifestFile = "nimbus.fml.yaml"

    // Map from the variant name to the channel as experimenter and nimbus understand it.
    // If nimbus's channels were accurately set up well for this project, then this
    // shouldn't be needed.
    channels = [
            debug: "debug",
            nightly: "nightly",
            beta: "beta",
            release: "release",
    ]
    
    // This is generated by the FML and should be checked into git.
    // It will be fetched by Experimenter (the Nimbus experiment website)
    // and used to inform experiment configuration.
    //
    // *NOTE*: This value is optional, and is not necessary when Nimbus is being used
    // as part of a library.
    experimenterManifest = ".experimenter.yaml"
    
    // This is an optional value, and updates the plugin to use a copy of application
    // services. The path should be relative to the root project directory.
    // *NOTE*: This example will not work for all projects, but should work for Fenix, Focus, and Android Components
    applicationServicesDir = gradle.hasProperty('localProperties.autoPublish.application-services.dir') 
        ? gradle.getProperty('localProperties.autoPublish.application-services.dir') : null
}

In this case, it should generate a file named in the nimbus.fml.yaml file. In the case of Fenix, this is called FxNimbus.

The start-up sequence

Before using Nimbus in your Android app, you need to start it.

The Nimbus SDK is a configuration store, making configuration available to the any thread, and — to a first approximation— to be immutable within the same session of the app.

For this reason, we want to be starting the Nimbus SDK as close to the beginning of the start of the app as possible.

In Firefox for Android and Focus for Android, this is done at the beginning of the Application#onCreate() method.

class MyApplication: Application() {

    lateinit var nimbus: NimbusInterface

    override fun onCreate() {
        beginNimbusSetup()


        // do the rest of the set up here.

        finishNimbusSetup()
    }

    fun beginNimbusSetup() {
        Megazord.init()

        nimbus = createNimbus(this, NIMBUS_REMOTE_SETTINGS_ENDPOINT)
    }

    fun finishNimbusSetup() {
        nimbus.fetchExperiments()
    }

    fun createNimbus(context: Context, urlString: String): NimbusInterface {
        val isAppFirstRun = context.settings().isFirstRun
        val customTargetingAttibutes = JSONObject().apply {
            // Put any custom attributes you want to use to segment an audience on to
            // target your experiments.
            put("is_first_run", isAppFirstRun)
        }

        val appInfo = NimbusAppInfo(
            appName = "my-app-name",
            channel = BuildConfig.BUILD_TYPE,
            customTargetingAttributes = customTargetingAttributes
        )

        // Use the Nimbus builder to build a NimbusInterface object.
        return NimbusBuilder(context).apply {
            url = urlString
            errorReporter = { message, e ->
                Logger.error("Nimbus error: $message", e)
            }

        }.build(appInfo)
    }
}

Notes:

  1. Megazord.init() is called before createNimbus().
  2. createNimbus uses a NimbusBuilder to create the Nimbus object.
  3. We build a JSONObject of custom targeting attributes.
  4. The nimbus.fetchExperiments() method is called sometime at or after the app has started.

NimbusBuilder configuration​

Getting errors out of Nimbus​

By design, Nimbus is deliberately unobtrusive; if it fails then it should not crash, but continue as if not enrolled in any experiments.

The errorReporter callback is there to connect Nimbus to any error reporting framework in the rest of the app.

    return NimbusBuilder(context).apply {
        // …
        errorReporter = { message, e ->
            Logger.error("Nimbus error: $message", e)
        }
        // …
    }.build(appInfo)

Connecting the NimbusInterface to FML generated code​

The FML generated code has a runtime dependency on the NimbusInterface.

To connect it to the Nimbus object, we need to tell the NimbusBuilder. In this case, the generated class is FxNimbus.

    return NimbusBuilder(context).apply {
        // …
        // Connect FxNimbus to the Nimbus SDK.
        featureManifest = FxNimbus
        // …
    }.build(appInfo)

Handling First Run experiments​

Since fetchExperiments from the remote settings URL is slow, and we wish to be able have access to the Nimbus experimental configuration as early in start up as possible, Nimbus downloads and caches the experiment recipes on the nth run of the app and only applies them and makes them available to the app at the beginning of the next i.e. the (n + 1)th run of the app.

Astute readers will notice that when n = 0, i.e. the very first time the app is run, there are no experiment recipes downloaded. If Remote Settings experiment recipes JSON payload is available as a raw/ resource, it can be loaded in at first run:

    return NimbusBuilder(context).apply {
        // …
        isFirstRun = isAppFirstRun
        initialExperiments = R.raw.initial_experiments
        timeoutLoadingExperiment = TIME_OUT_LOADING_EXPERIMENT_FROM_DISK_MS // defaults to 200 (ms)
        // …
    }.build(appInfo)

The initial_experiments.json file can be downloaded, either as part of the build, or in an automated/timed job. e.g. this is the Github Action workflow used by Fenix.

To check if the firstrun experiment merged into beta to catch the next release​

First run experiments need to be in the beta build 8-11 days before release, so that they are in the release candidate. Final build happens 8 days before release on Monday - so best to get in and uplift approved by Friday at the latest. On Android the Release Candidate goes out to 5% of users a week before general release.

After the change is made in Nimbus/Experimenter to launch, enrollment end, or end the experiment - a github action kicks off the PR automatically to update 'initial_experiments.json'. Then a mobile engineer needs to r+ that PR and request uplift to Beta. If you replace 'version number' in the following file name, you can check this file to see if the experiment config is in the right state before release candidate build https://raw.githubusercontent.com/mozilla-mobile/firefox-android/releases_v'version number'/fenix/app/src/main/res/raw/initial_experiments.json.

Using the experiments preview collection​

The preview collection is a staging area for new experiments to be tested on the device. This should be toggleable via the UI, but should trigger a restart.

Adding the usePreviewCollection flag allows the builder to configure a NimbusInterface object connected to the experiment recipes in the preview collection.

        // Use the Nimbus builder to build a NimbusInterface object.
        return NimbusBuilder(context).apply {
            // …
            usePreviewCollection = context.settings().nimbusUsePreview
            // …
        }.build(appInfo)

A complete NimbusBuilder example​

    return NimbusBuilder(context).apply {
        url = urlString
        errorReporter = { message, e ->
            Logger.error("Nimbus error: $message", e)
        }
        initialExperiments = R.raw.initial_experiments
        usePreviewCollection = context.settings().nimbusUsePreview
        isFirstRun = isAppFirstRun
        sharedPreferences = context.settings().preferences
        // Optional callbacks.
        onCreateCallback = { nimbus ->
            // called when nimbus is set up
        }
        onFetchCallback = {
            // called each time the app fetches experiments
        }
        onApplyCallback = {
            // called each time the applies the fetched experiments.
        }
    }.build(appInfo)

Instrumenting the app for testing​

The nimbus-cli allows QA and engineers to launch the app in different experimental configurations. It largely obviates the need for configuring Nimbus to use the preview collection, above.

To connect the NimbusInterface object to the command line, we need to feed the intent from the app's launch activity to the NimbusInterface.


import org.mozilla.experiments.nimbus.initializeTooling

open class HomeActivity : AppCompatActivity() {)
    override fun onCreate(savedInstanceState: Bundle?) {
        // Find the nimbus singleton
        val app = application as MyApplication
        val nimbus = app.nimbus
        // Pass it the launch intent
        nimbus.initializeTooling(applicationContext, intent)
        // …
    }
Copyright © 2024 Mozilla Corporation
+ \ No newline at end of file diff --git a/getting-started/engineers/getting-started-for-ios-engineers/index.html b/getting-started/engineers/getting-started-for-ios-engineers/index.html index 4988cf038..fb6ff0ce6 100644 --- a/getting-started/engineers/getting-started-for-ios-engineers/index.html +++ b/getting-started/engineers/getting-started-for-ios-engineers/index.html @@ -4,14 +4,14 @@ Getting Started for iOS Engineers | Experimenter Docs - +

Getting Started for iOS Engineers

Introduction​

Nimbus is an experimentation platform from Mozilla.

This document shows you how to set up the Nimbus SDK with a new iOS app. It assumes that your app is already using the Glean SDK and Rust Swift Components.

Building with Nimbus​

Generating Swift with the Nimbus FML​

The Feature Manifest Language provides type-safe access to configuration coming out of the Nimbus SDK, and is used to configure your application features, by generating Swift from a Feature Manifest.

Once you have this running, the FML files will the main way you interact with Nimbus.

Setting up​

Run the following command in a terminal, from the top level directory of your project:

NIMBUS_FML_FILE=./nimbus.fml.yaml
curl --proto '=https' --tlsv1.2 -sSf \
    https://raw.githubusercontent.com/mozilla/application-services/main/components/nimbus/ios/scripts/bootstrap.sh | bash -s -- $NIMBUS_FML_FILE

You should also add it to your project's bootstrap.sh or installer script.

This will download the shell script that will run on each build. On first run, it will download a sample nimbus.fml.sh to the given location, and configuration files to ./bin.

Edit the configuration file nimbus-fml-configuration.sh to match your setup. For example, any time you add a new CONFIGURATION (e.g. Debug, Release), you should add a new mapping to a build CHANNEL in this file.

If you need to move the nimbus.fml.yaml file, then you should edit this in the call in the bootstrap.sh file and in the nimbus-fml-configuration.sh file.

Adding Nimbus FML to the build​

A build phase needs to be added to the app, calling in to nimbus-fml.sh. This is configured with a project specific nimbus-fml-configuration.sh.

This will generate a Swift file in a $MODULE/Generated/AppConfig.swift, where AppConfig is the name of the manifest as specified in the FML files.

The start-up sequence​

The nimbus object, an instance of the NimbusInterface manages the connection between your app and the Nimbus SDK.

It can be constructed with a NimbusBuilder. Here is a minimal example:

import Foundation
import MozillaAppServices

public static var nimbus: NimbusInterface = {
    // App settings, to allow experiments to target the app name and the
    // channel. The values given here should match what `Experimenter`
    // thinks it is.
    let appSettings = NimbusAppSettings(
        appName: "example-app",
        channel: "release"
    )

    guard let dbPath = defaultDatabasePath() else {
        log.error("Nimbus didn't get to create, because of a nil dbPath")
        return NimbusDisabled.shared
    }

    return NimbusBuilder(dbPath: dbPath)
        .with(url: remoteSettingsURL)
        .with(featureManifest: AppConfig.shared)
        .build(appInfo: appSettings)
}()

static func defaultDatabasePath() -> String? {
    let paths = FileManager.default.urls(for: .applicationSupportDirectory, in: .userDomainMask)
    if paths.count == 0 {
        return nil
    }
    return paths[0].appendingPathComponent("nimbus.db").path
}

The Nimbus object should be constructed and configured as soon as the app is launched, for example, at the beginning of application(_:willFinishLaunchingWithOptions).

func application(
    _ application: UIApplication,
    willFinishLaunchingWithOptions launchOptions: [UIApplication.LaunchOptionsKey : Any]? = nil
) -> Bool {
    let sdk = nimbus // or wherever you've put the nimbus singleton

    // Fetch experiments on a background thread.
    // These will not be ready until the next startup.
    sdk.fetchExperiments()
}

Getting errors out of Nimbus​

By design, Nimbus is deliberately unobtrusive; if it fails then it should not crash, but continue as if not enrolled in any experiments.

The errorReporter callback is there to connect Nimbus to any error reporting framework in the rest of the app.

    return NimbusBuilder(dbPath: dbPath)
        // …
        .with(errorReporter: { err ->
            log.error("Error in Nimbus SDK", err)
        }
        // …
        .build(appInfo: appInfo)

Connecting the NimbusInterface to FML generated code​

The FML generated code has a runtime dependency on the NimbusInterface.

To connect it to the Nimbus object, we need to tell the NimbusBuilder. In this case, the generated class is AppConfig.shared.

    return NimbusBuilder(dbPath: dbPath)
        // …
        .with(featureManifest: AppConfig.shared)
        // …
        .build(appInfo: appInfo)

Handling First Run experiments​

Since fetchExperiments from the remote settings URL is slow, and we wish to be able have access to the Nimbus experimental configuration as early in start up as possible, Nimbus downloads and caches the experiment recipes on the nth run of the app and only applies them and makes them available to the app at the beginning of the next i.e. the (n + 1)th run of the app.

Astute readers will notice that when n = 0, i.e. the very first time the app is run, there are no experiment recipes downloaded. If Remote Settings experiment recipes JSON payload is available as a raw/ resource, it can be loaded in at first run:

    return NimbusBuilder(dbPath: dbPath)
        // …
        .isFirstRun(isFirstRun)
        .with(initialExperiments: Bundle.main.url(forResource: "initial_experiments", withExtension: "json"))
        .with(timeoutLoadingExperiments: TIME_OUT_LOADING_EXPERIMENT_FROM_DISK_MS)  // defaults to 200 (ms)
        // …
        .build(appInfo: appInfo)

The initial_experiments.json file can be downloaded, either as part of the build, or in an automated/timed job. e.g. this is the Github Action workflow used by Firefox for iOS.

Using the experiments preview collection​

The preview collection is a staging area for new experiments to be tested on the device. This should be toggleable via the UI, but should trigger a restart.

Adding the usePreviewCollection flag allows the builder to configure a NimbusInterface object connected to the experiment recipes in the preview collection.

    return NimbusBuilder(dbPath: dbPath)
        // …
        .with(url: remoteSettingsURL)
        .using(previewCollection: usePreviewCollection)
        // …
        .build(appInfo: appInfo)

Instrumenting the app for testing​

The nimbus-cli allows QA and engineers to launch the app in different experimental configurations. It largely obviates the need for configuring Nimbus to use the preview collection, above.

To connect the NimbusInterface object to the command line, we need to feed the command line arguments through NimbusBuilder:

    return NimbusBuilder(dbPath: dbPath)
        // …
        .with(commandLineArguments: CommandLine.arguments)
        // …
        .build(appInfo: appInfo)

A complete NimbusBuilder example​

import Foundation
import MozillaAppServices

public static var nimbus: NimbusInterface = {
    let defaults = UserDefaults.standard
    let usePreviewCollection = defaults.bool(forKey: NimbusUsePreviewCollectionDefault)
    let isFirstRun = !defaults.bool(forKey: NimbusIsFirstRunDefault)
    if isFirstRun {
        defaults.set(false, forKey: NimbusIsFirstRunDefault)
    }

    // App settings, to allow experiments to target the app name and the
    // channel. The values given here should match what `Experimenter`
    // thinks it is.
    let appSettings = NimbusAppSettings(
        appName: "example-app",
        channel: "release",
        customTargetingAttributes: [
            "is_first_run": isFirstRun,
        ]
    )

    let errorReporter: NimbusErrorReporter = { err in
        log.error("Error in Nimbus SDK", err)
    }

    guard let dbPath = defaultDatabasePath() else {
        log.error("Nimbus didn't get to create, because of a nil dbPath")
        return NimbusDisabled.shared
    }

    let bundles = [
        Bundle.main,
        Bundle.main.fallbackTranslationBundle()
    ].compactMap { $0 }

    return NimbusBuilder(dbPath: dbPath)
        .with(url: remoteSettingsURL)
        .using(previewCollection: usePreviewCollection)
        .with(errorReporter: errorReporter)
        .with(initialExperiments: Bundle.main.url(forResource: "initial_experiments", withExtension: "json"))
        .isFirstRun(isFirstRun)
        .with(bundles: bundles)
        .with(userDefaults: UserDefaults.standard)
        .with(featureManifest: AppConfig.shared)
        .build(appInfo: appSettings)
}()
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/getting-started/engineers/getting-started-mobile-required-ui/index.html b/getting-started/engineers/getting-started-mobile-required-ui/index.html index 35b5cbc13..de20a5515 100644 --- a/getting-started/engineers/getting-started-mobile-required-ui/index.html +++ b/getting-started/engineers/getting-started-mobile-required-ui/index.html @@ -4,13 +4,13 @@ Required UI for Mobile Integration | Experimenter Docs - +

User Interface requirements

Currently Nimbus provides no user-interface components of its own, though provides API to connect to existing settings screens.

Global opt-out/opt-in for experiments​

The settings page should include a Studies toggle, which allows users to opt-in or opt-out of experiments. The example from Firefox for iOS is shown:

Toggling the Studies flag should set the Nimbus value for globalUserParticipation:

nimbus.globalUserParticipation = flag

Resetting telemetry identifiers​

During experiment enrollment, telemetry is generated which can connect the user to the experiment enrollment.

When the user opts-in or opts-out of telemetry collection, the toggle should call:

nimbus.resetTelemetryIdentifiers()

This disqualifies existing enrollments. and breaks any connection with experiment enrollment and the enrollment telemetry.

QA tooling​

The following are nice-to-haves, obviated by the use of the nimbus-cli.

Preview Collection​

To allow testing of experiments before they are published, the client should allow configuring the Nimbus startup to use the preview-collection.

The above shows a non-user visible settings screen in Fenix. The toggle sets a Defaults/SharedPreferences flag which is read at startup to configure Nimbus startup.

The preview collection is loaded on the next restart, and available to the app on the restart after that.

Manual opt-in of experiments​

To allow the manual opt-in of a particular branch, the app must provide a screen to list all available experiments:

val experiments: List<AvailableExperiment> = nimbus.getAvailableExperiments()

To get the list of branches from an available experiment; you can get the branch slug that the client is enrolled in with getExperimentBranch:

val branches = experiment.branches
val branchSlugs: List<String> = branches.map { it.slug }
val enrolledBranchSlug = nimbus.getExperimentBranch(experiment.slug)

You can opt-in and out of a given experiment.

// opt in to a particular branch
val newBranchSlug = branchSlugs.get(0)
if (enrolledBranchSlug != newBranchSlug) {
    nimbus.optIn(experiment.slug, newBranchSlug)
} else {
    nimbus.optOut(experiment.slug)
}
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/glossary/index.html b/glossary/index.html index adc88405a..8c40f9d3c 100644 --- a/glossary/index.html +++ b/glossary/index.html @@ -4,13 +4,13 @@ Glossary | Experimenter Docs - +

Glossary

A​

B​

Behavioral targeting: here

Bucketing: here

C​

Cirrus: The codename of Nimbus for Web applications. PRD and ADR

Co-enrolling: here

Custom targeting: coming soon

E​

Experimenter:

    Nimbus is the whole experimentation system (handles enrolling/unenrolling users, sending branch configurations, etc). Nimbus was the code name of the experimentation project.
    Experimenter is the customer interface to both Nimbus and Jetstream - that you access at experimenter.services.mozilla.com/. In Experimenter users can configure experiments, manage them (launch, monitor, end), and can see the results.

F​

First-run experiment: here

Feature Manifest Language (FML): here

J​

Jetstream:

    Jetstream is the whole analysis system (interfaces with data, runs analysis, formats results). See more here

K​

Kinto: docs

R​

Remote Settings:

Rollout:

    A single-branch experiment, often the "winning branch" from an experiment, that can be used to "roll out" a feature to your Audience. See the Rollouts deep dive for more info.

S​

Smoke Test:

    A smoke test is a preliminary trial/experiment with minimal setup/limited parameters/minimal user population to ensure that the experiment is operational and that there are no obvious issues. The objective is to catch any major problems early on, allowing us to address them before conducting more in-depth/resource-intensive (stat.sig) experiments. This is common when there are new pieces (like many newly added telemetry probes or complex targeting or triggers) coming together. It is better to learn in a small (hundreds instead of hundreds of thousands people) experiment that your message is triggering at the wrong time to the wrong users, or that your telemetry isn't capturing something you need to learn.

More coming soon...

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/help/index.html b/help/index.html index 2bfc14622..c8956bd16 100644 --- a/help/index.html +++ b/help/index.html @@ -4,7 +4,7 @@ finding-help | Experimenter Docs - + @@ -13,7 +13,7 @@ ⭐️ Join a lightning advisory session

In this documentation...​

⭐️ Take a look at the Getting Started section
⭐️ Check out our FAQs to see if your question has already been asked
⭐️ Check the Glossary for terms you are unfamiliar with

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/index.html b/index.html index 26bcba70d..c52417f82 100644 --- a/index.html +++ b/index.html @@ -4,13 +4,13 @@ Welcome | Experimenter Docs - +

Welcome

Welcome to the Experimenter documentation hub, your central resource for A/B experiments and feature rollouts in Firefox Mobile and Desktop. You should find these documents helpful if you are:

  • A product manager and you have a hypothesis you want to test or a feature you'd like to safely release.
  • An engineer who needs to implement an experiment or feature rollout in your client
  • A data scientist helping to design and interpret experiment results
  • A Mozilla leader who would like insight into completed and ongoing experiments and rollouts.

What is covered here?​

These documents are specific to the Mozilla experimentation program known as Nimbus. Nimbus experimentation and support is currently available for our Firefox Desktop and Mobile browser applications.

Our platform code is all open-source however, we do not offer support or services to 3rd party consumers. Some documentation links may only be available to Mozilla employees and NDA contributors.

What other tools exist​

The following projects have bespoke tools for experimentation. These are not included as part of this documentation with the exception of a migration guide from Normandy to Nimbus for engineers.

  • Firefox Desktop (legacy experimentation): Normandy
  • Mozilla Websites (a/b experiments): Bedrock
  • Firefox Accounts (limited content server): Fxa docs

About these docs​

This website is built using Docusaurus. If you'd like to edit or add to them, check out the Contributing page.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/integration-tests/index.html b/integration-tests/index.html index b58c2fb69..0b45cc717 100644 --- a/integration-tests/index.html +++ b/integration-tests/index.html @@ -4,13 +4,13 @@ Integration Test Docs | Experimenter Docs - +

Integration Tests

About​

The integration test suite is an end-to-end test suite that uses Selenium and Firefox to verify Nimbus functions properly for users.

Nimbus Tests​

Getting Started​

You must have Docker installed.

  1. Create a python virtual environment
  2. Copy .env.integration-tests file to .env.
  3. Run make refresh SKIP_DUMMY=1 to initialize the DB and setup Nimbus.
  4. Run the make up_prod_detached command.
  5. Run the following command to run the integration tests: make integration_test_nimbus

To pass custom parameters to pytest use the environment variable PYTEST_ARGS.

Legacy tests​

To run the legacy tests follow steps 1-3 above. Then run this command: make integration_test_legacy. This will run the legacy experimenter test suite.

Running a single test​

If you would like to run just 1 test, pass this flag to PYTEST_ARGS with the test name you would like to run: -k. Example make integration_test_nimbus PYTEST_ARGS=-ktest_archive_experiment

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/ios-custom-targeting/index.html b/ios-custom-targeting/index.html index 1335a91ef..d4073dfd0 100644 --- a/ios-custom-targeting/index.html +++ b/ios-custom-targeting/index.html @@ -4,14 +4,14 @@ Adding new targeting attributes to iOS | Experimenter Docs - +

Adding new targeting attributes to iOS

This page demonstrates how to add new targeting attributes to iOS, enabling experiment creators more specific targeting. For more general documentation on targeting custom audiences, check out the custom audiences docs

Adding the attribute to the application​

The Nimbus SDK exposes a new customTargetingAttributes parameter in its initializer that is a [String:String] map. We can take advantage of this parameter to pass in new targeting attributes without modifying the Nimbus SDK at all.

danger

A current limitation is that both the key and the value of the targeting attribute are strings. Please reach out to the Nimbus SDK team for any targeting attributes that require integer comparison, or any other richer JEXL expressions that cannot be done with strings.

Note that since we need to add the targeting attribute on the client code, the attribute changes will have to ride the trains before they are available for targeting.

How to add a new attribute​

In AppDelegate+Experiments.swift the map customTargetingAttributes will be used to add custom targeting. Simply add a new key-value pair to the map and it will be available for targeting. For example:

Experiments.customTargetingAttributes =  ["isFirstRun": "\(isFirstRun)", "newTargetingAttributeName": "targetingAttributeValue"]

Adding the attribute on experimenter​

After the targeting attribute is ready on the app, you will need to modify experimenter to allow creating experiments that target the attribute you created. Follow the instructions on the custom audiences page to add the new targeting on experimenter.

danger

The targeting JEXL expression on experimenter must use the same name as the key given to the SDK. For example, for targeting users on their first run, the app defines a key-value pair, with key isFirstRun. The experimenter expression must use the same name (i.e isFirstRun)

Examples​

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/ios-preview-testing/index.html b/ios-preview-testing/index.html index d45ed3015..316191c3d 100644 --- a/ios-preview-testing/index.html +++ b/ios-preview-testing/index.html @@ -4,14 +4,14 @@ Testing the preview flow on iOS | Experimenter Docs - +

Testing the preview flow on iOS

Launching an experiment to Preview the stage server​

The first step to testing the preview flow is to launch an experiment to preview on the stage nimbus server (we should not using the production server for testing). Go to the stage experimenter and create your experiment there.

Follow the first few instructions on the preview docs to get an experiment to preview. (note that the experiment should be set to target iOS, look at the FAQ section at the bottom)

Pointing a local iOS build to the stage server​

Since we will be using the stage server, we want Firefox to look for experiments there. For iOS, you can do that on a local build, which you can get running by following the instructions on the firefox-ios repository

Once you have a local build using xcode, you should replace NIMBUS_URL with https://firefox.settings.services.allizom.org/v1/. You can set that on info.plist, which lies in the Client directory once you have the project setup. NIMBUS_URL will have an initial value of $(NIMBUS_URL), replace that with the setting server's url.

Accessing the secret menu​

At this point, you can run your local build of Firefox iOS, once it's run:

  1. Access the settings menu and click 5 times on the Firefox Daylight 0.0.1 under the about section:

  2. Scroll all the way to the bottom and click the Experiments menu.

  3. Click on Edit on the top right.

  4. Choose the Use Preview Collection (requires restart) option.

  5. Click Reset and restart the app.

  6. Repeat the steps 1-2, and you should now see the experiment you launched to preview!

You can also check out this visual documentation for a clear demonstration of how to switch to the preview collection.

FAQs​

  • What kind of experiment should I create?

If you would like your test to be more in-depth, we recommend you create an iOS experiment that can be directly tested on the UI. For example, you can create an onboarding-default-browser experiment by setting the feature config to onboarding-default-browser and setting the appropriate values, check out this experiment on experimenter for a complete example

  • I don't see my experiments on Firefox!

It takes a little time, but if you still can't see the experiments, try restarting the app and double check that you launched your experiment to preview and set the Application to Firefox iOS and didn't miss any configuration on experimenter. If you keep facing trouble reach out to use on #ask-experimenter on slack!

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/launching/index.html b/launching/index.html index 398fd9417..ba7113b2e 100644 --- a/launching/index.html +++ b/launching/index.html @@ -4,13 +4,13 @@ Launching Your Experiment | Experimenter Docs - +

Launching Your Experiment

After you're experiment has passed QA and you've mitigated the risks, you are ready to launch!

You're experiment should be in Preview mode - from the QA testing and/or your self-test.

At this point you check off the questions accepting responsibility and verifying that you've completed the training, then click "Request Launch"

Now you follow the directions in Experimenter: Copy the URL for your experiment. Include the URL and ask for review to launch either from a reviwer on your team or go to #ask-experimenter and ask for someone to help review and approve the launch. If none of the general reviewers approves the launch it in a couple of hours - ping again. It is usually quite quick.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/local-enrollment/index.html b/local-enrollment/index.html index 6c7381acd..1f992d7c6 100644 --- a/local-enrollment/index.html +++ b/local-enrollment/index.html @@ -4,14 +4,14 @@ Local configuration for natural enrollments | Experimenter Docs - +

Local configuration for natural enrollments

Desktop​

Preferences to set in Firefox

  • nimbus.debug: True
  • app.normandy.run_interval_seconds: 30
  • services.settings.server: http://localhost:8888/v1

Note: You can also use the remote-settings-devtools add-on to control some of these but the app.normandy.run_interval_seconds preference must still be set.

Instructions for Nimbus​

  1. Set the above preferences within Firefox
  2. Create a desktop experiment with the following settings
    • A desktop feature
    • No advanced targeting
    • All Locales
    • All Regions
    • Percent of clients: 100%
    • Expected Number of Clients: 1 (or any number above 0)
  3. Request Launch and Approve the experiment in Remote Settings
  4. Open the Browser Console to view the logs from “RSLoader” (RemoteSettingsExperimentLoader.Jsm)

There should be log outputs of the RSLoader reading from remote settings and showing the JEXL evaluations being attempted. Eventually the experiment you created should be loaded and evaluated. If you are enrolled into a study with the same feature, the new experiment will not be allowed to enroll. 5. Check about:telemetry for an event that looks like this

  • Enrollment
    normandy  enroll    nimbus_experiment <name-of-experiment>
  • Unenrollment
    normandy  unenroll  nimbus_experiment <name-of-experiment>

Mobile​

Coming soon!

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/localization-process/index.html b/localization-process/index.html index e97a65e1a..25081da1c 100644 --- a/localization-process/index.html +++ b/localization-process/index.html @@ -4,13 +4,13 @@ Localization | Experimenter Docs - +

Localization

info

Localization support for Nimbus experiments is constantly improving. Make sure to check this page again before setting up new experiments.

Using the process described in this page it’s possible to localize content for experiments outside of the standard product release cycle. There are two different workflows, depending on the experiment’s target:

  • Firefox Desktop 113 and later versions include support for multi-locale recipes.
  • For older versions of Firefox Desktop, and potentially other platforms, there is a manual process using single-locale recipes.

In most cases, the translation will be performed through Pontoon, the internal Translation Management System (TMS) in use at Mozilla.

Overview of localization workflow

The standard process expects content to be translated and reviewed by community, which ensures higher quality but requires longer turn-around. On a case by case basis, it will be possible to consider alternative sources (internal staff, external paid vendors, or machine translation).

PROVIDERQUALITYSPEEDSLA
Community (Pontoon)HighLow (1-2 weeks)N/A
Pretranslation (Pontoon)1Medium-LowHigh (minutes without review)100%
Paid vendorsMedium-HighMedium-High (48h or more)100%

The localization EPM assigned to the issue will be able to advise on the best choice.

Multi-locale recipe (Firefox 113 and later)​

Set up the experiment​

Each localizable string is set up in the experiment using a $l10n object. The object properties are:

  • ìd: a unique identifier for the string. It can only contain letters, numbers, and -.
  • text: the text of the string.
  • comment: a comment used to provide more context to localizers.

An identifier can be reused throughout the recipe, but it always has to be associated to the same text.

Here’s an example of string definition in a recipe fragment:

"title": {
  "raw": {
    "$l10n": {
      "id": "welcome-text",
      "text": "Welcome to Firefox",
      "comment": "welcome text"
    }
},

In order to move forward with the translation request:

  • The experiment needs to be set up as a draft in Experimenter, with all localizable strings defined in each branch as $l10n objects.
  • The list of locales needs to be selected in the Audience section.
  • The Localization checkbox in the Branches section can remain deselected at this stage.

Automation in the l10n repository will use Experimenter’s API to find the draft, extract the list of requested locales and the strings to translate (by searching for $l10n objects).

Request Translation​

Once the experiment is set up as a draft and the content has been finalized, to initiate a translation request create a new issue here. Use the Submit new issue button at the bottom of the page after filling all mandatory fields in the form; this will generate a standard GitHub issue, so it will still be possible to add comments later.

Issue title​

Include a descriptive name for the experiment, e.g. PiP alternative dismiss message.

Information about the experiment​

Provide as much information as possible about the experiment, including links to the experiment brief and mock-ups of the UI where applicable. This information will be used by the Localization EPM assigned to the issue to prepare the file for translation, and provide context to localizers.

Deadline​

Provide the desired deadline (optional). Unless there is pre-established availability from staff to localize the content, expect about 7 days to complete the process from request to delivery. The time required might change depending on the amount of text to translate.

Retrieve Translated Content​

Once translation has been completed, automation will add a comment in the original issue with the translation in JSON format. This data can be copied directly in the Localization field in Experimenter (Branches section). Note that GitHub provides a convenient copy button, when hovering over a code block.

The JSON will look like this:

{
  "en-US": {
    "message-text": "This is an example localized experiment",
    "ok-button-text": "OK",
    "welcome-text": "Welcome to Firefox"
  },
  "fr": {
    "message-text": "Ceci est un exemple d’expérience localisée",
    "ok-button-text": "D’accord",
    "welcome-text": "Bienvenue dans Firefox"
  }
}

Single-locale recipe​

This solution has a few limitations:

  • To target multiple locales (e.g. de and fr), it’s necessary to create separate experiments, each targeting a single locale.
  • The process to request translations and retrieve translated content is completely manual.

One of the benefits is that the request can be started before actually setting up the experiment in Experimenter.

Request Translation​

Once the content needed for the experiment is finalized, to initiate a translation request create a new issue here.

For a description of fields shared with the multi-locale recipe request (Issue title, Information about the experiment, Deadline), see the previous section.

List of locales​

Provide the list of locales that need to be translated. Keep in mind that:

  • Locales are not regions. So, for example, French (fr) should be requested, not France.
  • Not all locales are supported. For example, our infrastructure supports a generic French (fr), but not Canadian French (fr-CA). In case, reach out to the Localization Team to clarify any doubts.

Given the short turn-around for localization, we are currently limiting the requests to tier 1 locales (de, fr) — where we potentially also have internal copywriters as a backup — and locales that have internal staff available for support (it, ja).

Content to translate​

If the experiment is small, it’s possible to provide the content to translate directly in the issue. Alternatively, consider providing a link to a spreadsheet with one string per row, and 2 columns (one for the English text, one for comments or context) (an example is available here).

If you’re working with a developer already familiar with Fluent, they can also help providing directly Fluent content with comments. Note that we only support a subset of features in experiments:

  • Only plain messages with a value, no attributes or variants (e.g. plurals).
  • No terms or variables, so brand names like Firefox need to be hard-coded in the text.

Example of Fluent format:

intro-message = Switch to Firefox Home with themed background
# Button label that enables the theme
intro-button-enable = Use background

Retrieve Translated Content​

Once translation has been completed, the requester will be notified in the original issue, with a link to a JSON file that includes all translations. The experiment owner will need to copy each translation manually in the experiment recipes.

The JSON file uses the following structure:

{
  "complete": true,
  "complete_locales": [
    "de",
    "en-US",
    "fr",
    "it"
  ],
  "translations": {
    "en-US": {
      "device-migration-fxa-spotlight-header": "Using an older device?",
      "device-migration-fxa-spotlight-body": "Back up your data to make sure you don’t lose important info like bookmarks and passwords –– especially if you switch to a new device.",
    },
    "de": {
      "device-migration-fxa-spotlight-header": "Verwenden Sie ein älteres Gerät?",
      "device-migration-fxa-spotlight-body": "Sichern Sie Ihre Daten, um wichtige Informationen wie Lesezeichen und Passwörter nicht zu verlieren – insbesondere, wenn Sie zu einem neuen Gerät wechseln.",
    },
    "fr": {
      "device-migration-fxa-spotlight-header": "Vous utilisez un appareil plus ancien ?",
      "device-migration-fxa-spotlight-body": "Sauvegardez vos données pour vous assurer de ne pas perdre d’informations importantes comme des marque-pages ou des mots de passe, surtout si vous changez d’appareil.",
    },
    "it": {
      "device-migration-fxa-spotlight-header": "Stai utilizzando un dispositivo meno recente?",
      "device-migration-fxa-spotlight-body": "Salva i tuoi dati per non perdere informazioni importanti come segnalibri e password, soprattutto se hai in programma di passare a un nuovo dispositivo.",
    }
  }
}
  • complete: boolean field that indicates if the experiment has been translated in all requested locales.
  • complete_locales: list of all locales completely translated.
  • translations includes a key for each locale (e.g. it for Italian), which in turns includes all pairs of unique message identifiers and translations. en-US is also provided as a reference..
  1. Currently still in a testing phase.↩
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/desktop-messaging-journey/index.html b/messaging/desktop-messaging-journey/index.html index f87bca4a5..741624d73 100644 --- a/messaging/desktop-messaging-journey/index.html +++ b/messaging/desktop-messaging-journey/index.html @@ -4,14 +4,14 @@ Desktop Messaging Journey | Experimenter Docs - +

Desktop Messaging Journey

From inception to launch, a message goes through the below three steps for Firefox Desktop:

Message Design​

Once an idea has been developed, the next step is designing the experiment message. A good place to start for some inspiration, is to look through previous messaging system experiments (Examples in Source doc) as well as looking into the available capabilities through the Messaging Surfaces. The OMC team has vast experience with running these experiments and can provide feedback and answer any questions in the #omc Slack channel.

Running an Experiment​

First, determine if your experiment will require any on-train development work to support the launch as well as any translated strings that need to land prior. Once that’s been determined, we can get started with creating the experiment. For a more in-depth guide and step by step process, visit OMC: Experimenter onboarding document

Message in Firefox​

Once the experiment has successfully concluded and analysis shows promising results, we can land the new message in tree. Depending on the messaging surface, the following files is where the JSON for our different messages exists

browser/components/newtab/aboutwelcome/lib/AboutWelcomeDefaults.jsm
browser/components/newtab/lib/FeatureCalloutMessages.jsm
browser/components/newtab/lib/OnboardingMessageProvider.jsm

Visit Firefox Source docs: newtab for details on how to develop within our components.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/desktop-messaging-surfaces/index.html b/messaging/desktop-messaging-surfaces/index.html index 26953eb7e..dd8509869 100644 --- a/messaging/desktop-messaging-surfaces/index.html +++ b/messaging/desktop-messaging-surfaces/index.html @@ -4,14 +4,14 @@ Desktop Messaging Surfaces | Experimenter Docs - +

Desktop Messaging Surfaces

The following messaging surfaces are currently remotely available in Firefox Desktop, meaning that you create new messages without shipping code changes.

Doorhanger​

Example of doorhanger component Example of doorhanger component
Doorhangers, also known as Contextual Feature Recommendation Panels, are anchored to a UI element such as the application menu, the identity panel, the Firefox View tab, and so on. They can include a configurable icon beside the title. Doorhangers do not reappear once the primary action button is clicked, irrespective of any frequency caps on the message. Clicking the primary button adds the message id to a block list and its messaging system impression are cleared.

Feature Callouts​

Example of feature callout in Firefox View with image and top center arrowExample of feature callout in the PDF viewer with upper right arrow
Feature Callouts point to and describe features in content pages or the browser chrome. Multiple messages can be used to create feature tours. The callout element is embedded in the page content and does not block other interactions. Callouts may be configured with a primary action and an optional dismiss button. The arrow can be positioned in the middle of one of the callout’s sides or either of its top corners. This surface is currently only available for Firefox View and the PDF viewer.

Infobar​

Example of infobar component
Shown at the top of browser content area, these can be per tab (switching tabs hides it) or global (persistent across tabs).

Moments Pages​

This type of message is a URL that the browser will open at the start of the browsing session and will focus on that tab. These pages appears on start-up as a full content page.

PrivateBrowsing​

Example of private browsing message
Messages shown inside about:privatebrowsing content area when new private window is opened.

Snippets​

Example of snippets component
Short messages that appear on New Tab Page to highlight products, features and initiatives

Multistage Spotlight​

Example of spotlight component with single centered screenExample of spotlight component with centered screen, QR code, email link, and app store buttons
Example of spotlight component with centered screen, step indicator, and noodlesExample of spotlight component with centered screen, step indicator, and noodles in dark mode
Example of spotlight component with split screenExample of spotlight component with split screen and checkbox in dark mode
This surface can be configured as a window or tab level modal, and all other interactions are prevented. The modal's configuration is highly flexible and may include primary and secondary actions, a logo image, background, dismiss button, localized app store icons, QR code with associated link, checkboxes, decorative noodles, and more. A given message may be configured with multiple screens. Progress through these screens may be displayed as a step indicator or a progress bar. Each screen can use a centered, single panel layout or a split screen layout with an image and/or hero text on one side and all other modal content on the other.
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/display-logic/index.html b/messaging/display-logic/index.html index f5d4e0320..e2c25d904 100644 --- a/messaging/display-logic/index.html +++ b/messaging/display-logic/index.html @@ -4,13 +4,13 @@ Messaging Display Logic | Experimenter Docs - +

Messaging Display Logic

There are three types of display logic you should consider when using any of the messaging surfaces:

  • Triggers: In what context should the message be shown? (e.g. when the user opens a URL)
  • Targeting: For what kinds of users, or browser state, should the message be shown? (e.g. locale, country, prefs)
  • Frequency and Blocking: How many times should the message be shown, and in what conditions should it never be shown again?

Triggers​

A trigger is a particular "event" (or set of events) that must occur for the message to be displayed. You can see a complete list of triggers in Firefox source docs, but here are some of the most common ones:

openURL​

The message will display when the user navigates to a URL that matches the provided hosts or patterns. The most general configuration for this is "*", which means the message will effectively trigger the first time any window is opened.

preferenceObserver​

The message will display when a preference has changed. Note that this does not trigger if the preference was already set to the desired value at startup.

Targeting​

Sometimes you want to limit messages to a particular group of users, to users with a particular kind of behavior. This is expressed as a combination of "targeting attributes", for example:

All users in the US, for beta and nightly channels, version 98.01 or higher, who do not have Firefox set to their default browser.

You can see a complete list of the available attributes in Firefox Source docs, but here are some common examples:

  • locale (language, e.g. en-CA)
  • region (based on IP, e.g. US, FR)
  • preferenceValue (any Firefox preference value)

Frequency and Blocking​

Depending on the needs of your campaign, messages can be configured to be shown only once or to re-show multiple times, under certain conditions.

Impressions​

We refer to the number of times a message can be shown in terms of "number of impressions". The details of a what counts as a single impression differ depending on the UI of the specific surface you are using, so you should read our messaging surfaces documentation to understand exactly what counts.

Frequency​

The number of impressions a message is allowed to be shown is expressed in terms of "frequency caps". There are two types of frequency caps to think about:

  • Lifetime frequency: the number of times message can ever be shown;
  • Frequency-per-period: the number of times an a message can be shown in a given period. This can be any period of time, but the most common ones are daily or weekly.

Some examples:

Lifetime cap of 3, weekly cap of 1. The message will be shown a maximum of once a week, three times overall.

Lifetime cap of 1. The message will only ever be shown once.

Blocking​

If a user takes an action to block a message, it will never show again, even if it was configured to have multiple impressions.

In most surfaces, this is when a user clicks the "x" or one of the action buttons. For some surfaces, this behavior can be configured. An action that closes a message without blocking it (i.e. that allows it show again if frequency capping allows) is referred to as a dismiss.

Blocks carry through to all messages with the same message id.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/experiments-and-user-messaging/index.html b/messaging/experiments-and-user-messaging/index.html index f48dd1d48..00f3f5293 100644 --- a/messaging/experiments-and-user-messaging/index.html +++ b/messaging/experiments-and-user-messaging/index.html @@ -4,13 +4,13 @@ Experiments & User Messaging | Experimenter Docs - +

Experiments & User Messaging

Experiments enforce the rule that a user cannot enroll in multiple experiments of the same type (feature). Each messaging surface corresponds to a feature so we can only run a single experiment for any given messaging surface per cohort of users.

There is no practical limitation of Messaging System for how many messages of the same type can exist. If deployed directly through Remote Settings multiple messages targeting the same surface can co-exist.

Messaging System will select a single message to show based on targeting condition and trigger. If multiple messages match, the first one is selected. Through frequency and lifetime settings eventually all messages will be given an opportunity to be shown.

For messages deployed outside of experiments we can use Priority (see How a Message Gets Shown) to rank the order in which they are evaluated.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/frequency-cap/index.html b/messaging/frequency-cap/index.html index 1a42e393f..911c122e3 100644 --- a/messaging/frequency-cap/index.html +++ b/messaging/frequency-cap/index.html @@ -4,13 +4,13 @@ Messaging System Frequency cap | Experimenter Docs - +

Message Frequency

How often and now many times a user should see a message is configured through frequency.

The following characteristics can be configured:

Lifetime​

How many times a message gets shown to the user before it permanently blocked.

Frequency​

How much time to allow between consecutive impressions. After a message is shown once it can be configured to be temporarily blocked for a period of time in order to not show it too often.

This capability is highly configurable allowing to specify multiple constrains for example:

  • show it at most 3 times per week
  • but not more than 1 per day
[
  {
    cap: 1,
    period: 86400000 // Max 1 per day
  },
  {
    cap: 3,
    period: 86400000 * 7 // Max 3 per week
  }
]

How it looks in practice

frequency: {
  custom: [
    {
      cap: 1, // How many times
      period: 60480000 // Time in ms
    }
  ],
  lifetime: 10 // Show it at most 10 times
}
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/groups-and-campaigns/index.html b/messaging/groups-and-campaigns/index.html index c63763edc..89b959625 100644 --- a/messaging/groups-and-campaigns/index.html +++ b/messaging/groups-and-campaigns/index.html @@ -4,13 +4,13 @@ Groups & Campaigns | Experimenter Docs - +

Groups & Campaigns

Message Groups​

There is the possibility to make further fine grained configuration that can set impression limits between multiple messages.

Example: we have two messages using 2 different messaging surfaces but the messages have a similar theme or content. We can use the groups field in the message configuration to point two separate messages to the same frequency and lifetime configuration. When multiple messages point to the same group configuration any impression from one of the messages counts against the total allowed for the group. If the group frequency is set to 1 per day then whichever message is shown first will prevent all the other messages in the group from appearing for the next 24 hours. When a single message is part of multiple groups, it will not display if either of the groups would not display (i.e. is disabled by preference or frequency capped).

Campaigns​

Campaigns are similar to groups but are used to block messages. If two messages reference the same campaign, blocking any of the messages will prevent the others from ever showing up. This was implemented for snippets, and as of December 22 2022, that is the only place they have been used. For example, all snippets mentioning "FxA accounts" would be grouped together in the same campaign. Block one snippet and it blocks the entire campaign.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/how-a-message-gets-shown/index.html b/messaging/how-a-message-gets-shown/index.html index 529de6acd..deb339fa4 100644 --- a/messaging/how-a-message-gets-shown/index.html +++ b/messaging/how-a-message-gets-shown/index.html @@ -4,13 +4,13 @@ How a Message Gets Shown | Experimenter Docs - +

How a Message Gets Shown

All messages belong to a message provider (a source):

  • Local provider: shipped with Firefox
  • Remote settings: a remote settings bucket
  • Experiments: part of a treatment branch

All messages have an associated trigger (user action targeted by the message) and targeting condition. Additionally messages have a frequency cap and they can belong to a group that also has a frequency cap.

Deciding to show a message starts with the trigger action, when this happens we look at all the messages matching that trigger. We filter out blocked messages or messages that reached their frequency cap (or their groups frequency cap) and we evaluate targeting returning the first message that matched targeting.

This cycle can result in a message being shown (which will send an Exposure event + message specific telemetry events) or no message being shown if targeting didn’t match (but it can still result in a Reach event being sent).

Message Priority​

Several messages can be listening for the same trigger condition in which case we can set a higher priority for the message we prefer.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/limitations/index.html b/messaging/limitations/index.html index e23638978..50f142f46 100644 --- a/messaging/limitations/index.html +++ b/messaging/limitations/index.html @@ -4,14 +4,14 @@ Limitations | Experimenter Docs - +

Limitations

History state of past experiments​

It is currently not possible to enroll users into experiments based on previous experiments that they might have been a part of. If multiple messaging campaigns go out as experiments targeting different surfaces it is not possible to ensure that users will not see both campaigns. Mitigation is possible through the use of groups for frequency capping. If the experiments happen simultaneously, then using the same group can ensure we control the time it takes for a user to see both campaigns.

Similarly (using targeting) it is possible to prevent a user from seeing a message if impressions from a different message are present. This is only possible if the two messages overlap in the system at the same time.

info

Note that when using targeting or groups in this way the message impressions are removed once the message has been removed or the experiment has ended.

No action to block message​

For the doorhanger template we had a built-in (into the message surface) button to block a message. For all other surfaces there is no way for the user to block a message from ever being shown. Usually campaigns have had a maximum of 1-2 impressions and it was not considered to add a block action. The benefit of blocking would be that we could group similar messages as part of a “campaign” (for example Mozilla VPN) and use the block signal as an indicator not to show future messages with similar content.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/mobile-messaging/index.html b/messaging/mobile-messaging/index.html index daab6fe31..2a9b68703 100644 --- a/messaging/mobile-messaging/index.html +++ b/messaging/mobile-messaging/index.html @@ -4,7 +4,7 @@ Mobile Messaging | Experimenter Docs - + @@ -12,7 +12,7 @@

Introduction

The mobile messaging system is a feature of Firefox on iOS and Android, designed to send in-app messages directly to users without going through a release cycle.

It allows staff— most likely experiment owners, product owners, user research and marketing teams—

  • to send messages to the user audiences, with rules about when to show them.
  • to experiment with those messages (the appearance or copy of the message)
  • to allow the user to respond to the message (either dismissing, or opening a specified URL)

About this document

This document is a guide for staff who wish to send message users through the experimenter interface.

It is also a living document:

  • the messaging system is under active development, and learning from its MVP.
  • attributes useful in triggers, and deeplink actions will accrue in each of the embedding apps.
  • surfaces are being added to embedding apps.

You can view a demo of sending a survey on mobile here Access Passcode: 9Zx9Lg&M

Edit history​

  • Changed is_default_browser to is_default_browser_string.
  • Renamed Glean messages to match the implementation.
  • Added notification surface and associated trigger expressions.
  • v117 - added experiment property to message, removed message-under-experiment.

Scene setting​

Nimbus Mobile Messaging is built on top of Nimbus, Mozilla's experimentation platform. Nimbus allows you to send bits of configuration to application features from Experimenter, the web-application staff use to launch and manage experiments and rollouts.

Using Experimenter in the general case is documented elsewhere, so this document is specifically concerned with configuring the messaging feature, via the Branches screen.

my-first-message in the branch configuration screen

The messages that are sent are specified as JSON in the "Value" text area on this screen, so much of this document will consider this the user-interface for sending messages.

{
    "messages": {
        "my-first-message": {
            "surface": "homescreen",
            "trigger": [
                "USER_EN_SPEAKER",
                "USER_RECENTLY_UPDATED"
            ],

            "title": "Enhance your privacy",
            "text": "We've improved our tracking protection, tap here to switch it on",
            "button-label": "Make it so!",

            "action": "OPEN_SETTINGS_TRACKING_PROTECTION"
        }
    }
}

We will start with this simple example to introduce the concepts, then add more as we go.

Anatomy of a message​

Messages have a number of components:

  • the message key. In this example above, this is my-first-message. For most cases, these should be
    • unique so as not to collide with other messages.
    • human readable. By convention, these are kebab-case.
  • the trigger. This is the conditions that must be true for the message to be eligible to be displayed.
  • the content of a message. This is the title of the message, the text and the button-label.
  • the surface. This is app specific.
  • the action that is performed if and when the user taps on the button.

If any of these components are missing, then the message is considered malformed.

Triggers​

The app provides the messaging system with attributes. We use JEXL expressions to query these attributes, or trigger messages based on the user's behavior in the app.

To reduce errors and to allow re-use, the JEXL expressions are named. By convention, these names are in SCREAMING_SNAKE_CASE.

The trigger attribute of the message is an array of these named trigger expressions.

If all of these trigger expressions evaluate to true, then the message is said to be eligible.

For example:

{
    "messages": {
        "my-first-message": {
            …
            "trigger": [
                "USER_EN_SPEAKER",
                "USER_RECENTLY_UPDATED"
            ],
            …
        }
    }
}

In the above example, only users who have updated their apps in the last 7 days and who are English speakers.

Message content​

The message content is provided as strings, and is what the user sees.

Only the text property is mandatory. If this is missing, then the message is said to be malformed.

{
    "messages": {
        "my-first-message": {
            …

            "title": "Enhance your privacy",
            "text": "We've improved our tracking protection, tap here to switch it on",
            "button-label": "Make it so!",

            …
        }
    }
}

If title is missing, then the title is not displayed.

If button-label is missing, then the button is not displayed, but the whole message is tappable.

Surfaces​

The surface of the message is the message surface on which the message will be displayed.

These are app specific: the application provides the surface to draw the message on.

SurfaceDescriptionVersions
homescreenOn the new tab/homescreen
notificationA system notification. Permission is required for Android SDK > 13v111
surveyA screen that is shown at startup

Actions​

Actions are performed when the user taps on the call to action (i.e. the button).

The actions are implemented as URLs, which may be deeplinks in to the app, web links, or deeplinks in to other apps.

To reduce errors and to allow re-use, action URLs can be named. By convention, these names are in SCREAMING_SNAKE_CASE.

If the specified action property contains a :// it is treated as a bare URL, so one-off URLs may be specified.

String substiutions​

Before the final URL is opened, a variable subsitution is made, so identifiers within a pair of braces may be made part of the URL.

Any attribute that Nimbus Mobile Messaging knows about can be used. For example:

{
    "messages": {
            "my-message": {
            …
            "action": "https://mozilla.org/fenix/{locale}/whatsnew/{app_version}"
            …
        }
    }
}

Additionally, the special {uuid} can be used to generate a new UUID. If detected, this will be recorded as an extra called action-uuid on the message's interaction event.

{
    "messages": {
        …
        "action": "https://surveys.thirdparty.org/fenix-viewpoint/?client_id={uuid}"
        …
    }
}

This allows the client's anonymous survey results to match up to their Glean data while remaining anonymous to the third party.

Other message attributes​

There are additional message attributes which don't quite fit anywhere, yet. It is best to leave this unused. We're looking for feedback during the MVP phase.

  • style – this is a string key into the styles object. It will code for visual style, and/or priority.

Current values are DEFAULT, PERSISTENT, WARNING, URGENT, NOTIFICATION.

This currently code for:

  • the message priority. Messages will be shown in descending order of priority.
  • the max-display-count. Messages will be shown to the user for this number of sessions before the message expires.

Localization of messages​

Localization of strings and messages is not fully developed in Nimbus: the intention is to integrate with the existing tooling for localizing strings.

For message content, you might use identifiers of pre-translated strings (in Firefox for iOS, Firefox for Android). This might be useful for very common phrases like OK, or Cancel.

However this relies on the exact translations either being coincidentally already in use by the app, or the experimental copy known about before the app was released. For several reasons, we do not recommend this approach.

Instead, we can add multiple messages which do the same thing, but in different langauges.

The messages property is a JSON object, so can accept multiple messages, each with different locale triggers.

{
    "messages": {
        "my-l10n-message-en": {
            …
            "trigger": [
                …
                "USER_EN_SPEAKER"
            ],
            "button-label": "Go to settings"
        },
        "my-l10n-message-de": {
            …
            "trigger": [
                …
                "USER_DE_SPEAKER"
            ],
            "button-label": "Einstellungen öffnen"
        },
        "my-l10n-message-es": {
            …
            "trigger": [
                …
                "USER_ES_SPEAKER"
            ],
            "button-label": "Ir a ajustes"
        },
        "my-l10n-message-fr": {
            …
            "trigger": [
                …
                "USER_FR_SPEAKER"
            ],
            "button-label": "Ouvrir les paramètres"
        }
    }
}

For each of the messages,

  • the message key should have a common prefix. This becomes important when experimenting with messages.
  • the triggers should be the same list of named expressions, but appending the trigger-expression to select for language.

In this manner the locale can be only one value at a time, and only one version of the message is eligible for display at any one time.

Experimenting with messages​

So far, we have talked about using Experimenter to push out messages as single branch experiments.

When running experiments, we'll need to configure two or more branches. By convention, each branch will have the same message keys— in the example below: my-first-message.

We should also tell the system that the message is under experiment. This is done by annotating each message with the experiment it came from. For convenience, the system replaces the string {experiment} with the experiment slug at enrollment, so annotating the message with the experiment is done like so:

{
    "messages": {
        "my-first-message": {
            "experiment": "{experiment}"
            …
        }
    },
}

Experimenting with localized messages​

We saw when localizing messages that the branch provided different messages based on adding additional trigger expressions.

It is important that:

  • each of the different languages represented in one branch is represented in all branches.
  • for the messages in each branch, no more than one message will be eligible for display at any one time. In the example below, the user's locale can only be set to one language at a time, so only one message is triggered at any one time.
{
    "messages": {
        "my-l10n-message-en": {
            "experiment": "{experiment}",
            …
            "surface": "notification",
            "trigger": [
                …
                "USER_EN_SPEAKER"
            ]
            …
        },
        "my-l10n-message-de": {
            "experiment": "{experiment}",
            …
            "surface": "notification",
            "trigger": [
                …
                "USER_DE_SPEAKER"
            ],
            …
        },
        "my-l10n-message-es": {
            "experiment": "{experiment}",
            …
            "surface": "notification",
            "trigger": [
                …
                "USER_ES_SPEAKER"
            ],
            …
        },
        "my-l10n-message-fr": {
            "experiment": "{experiment}",
            …
            "surface": "notification",
            "trigger": [
                …
                "USER_FR_SPEAKER"
            ],
            …
        }
    }
}

Since these triggers are mutually exclusive, the user will only ever be exposed to one message under experiment.

Control messages​

For most messages in experiments, we'll need to specify a control message.

The control is specified in a similar way to others:

{
    "messages": {
        "my-first-message": {
            "trigger": [ … ],
            "surface": "notification",
            "experiment": "{experiment}",
            "is-control": true,
        }
    }
}

Important: The control message must be eligible whenever the treatment message would have been. This means that:

  • the trigger value of the control must match the trigger value of the treatment message.
  • the surface value of the control must match the surface value of the treatment message.
  • the style value of the control must match the style value of the treatment message.

For localized messages, we will need to provide a set of contol messages that matches the treatment messages, so that the control message is triggered for all and only the same circumstances as the treatment.

{
    "messages": {
        "my-l10n-message-en": {
            "experiment": "{experiment}",
            "is-control": true,
            "trigger": [
                …
                "USER_EN_SPEAKER"
            ]
        },
        "my-l10n-message-de": {
            "experiment": "{experiment}",
            "is-control": true,
            "trigger": [
                …
                "USER_DE_SPEAKER"
            ]
        },
        "my-l10n-message-es": {
            "experiment": "{experiment}",
            "is-control": true,
            "trigger": [
                …
                "USER_ES_SPEAKER"
            ],
            …
        },
        "my-l10n-message-fr": {
            "experiment": "{experiment}",
            "is-control": true,
            "trigger": [
                …
                "USER_FR_SPEAKER"
            ]
        }
    }
}

Control messages (i.e. messages with is-control set to true) that do not have an experiment set to {experiment} will be reported by the client as malformed, since we can't ascertain which experiment they came from.

Displaying the control message​

The control message is the placebo, so doesn't make any sense to display to the user, so when a control message is selected for display, what should a message surface actually display?

{
    "messages": {
        "my-first-message": {
            "trigger": [ … ],
            "experiment": "{experiment}",
            "is-control": true,
        }
    },
    "on-control": "show-next-message"
}

The on-control property controls what happens in this case:

  • show-next-message causes the next eligible message to be displayed.
    • other messages may come from the app itself or from rollouts that other staff are doing.
  • show-none causes the message surface not to display anything at all.

By default, on-control is set to show-next-message.

Advanced uses of the {experiment} subsitution​

The literal string {experiment} can appear anywhere in the feature configuration, including the message key allows for some deduplication. For example, a regularly repeating message could be set up with message keys derived from the experiment slug.

{
    "messages": {
        "{experiment}-en": {
            "triggers": [
                "USER_EN_SPEAKER"
            ],
            …
        },
        "{experiment}-fr": {
            "triggers": [
                "USER_FR_SPEAKER"
            ],
            …
        },
        "{experiment}-es": {
            "triggers": [
                "USER_ES_SPEAKER"
            ],
            …
        },
        "{experiment}-de": {
            "triggers": [
                "USER_DE_SPEAKER"
            ],
            …
        }
    }
}

Events emitted​

Nimbus Events​

Nimbus emits events via Glean for all experiments that the user is enrolled in.

Enrollment is a decision taken at start-up about whether the user is eligible for the experiment (i.e. fits the experiment targeting criteria), and is chosen to part of the experiment cohort, i.e. is chosen to be have one of the branches).

Exposure evnts happen when the user is enrolled in the experiment, and the subsequently exposed to the treatment (or the control).

In the case of messaging, exposure events are emitted when a message from one of the branches of an enrolled experiment is shown. For other messages that aren't from an experiment, no exposure events are emitted.

Message Events​

Each of the following events is emitted— via Glean— at certain points while the message exists on the user's device.

  • message_shown: the message is shown to the user.
  • message_clicked: the user has tapped on the button, or the message itself if a button doesn't exist.
    • an action-uuid is given as an extra.
  • malformed: the message sender has somehow made an error specifying a particular message. e.g.
  • message_dismissed: the message was shown to the user, and tapped the dismiss action. This is message surface dependent.
  • message_expired: The message has been shown to the user for a number of sessions, and not been interacted with. We therefore expire the message.

Each message has a message-key extra.

Extending the system​

Much of the system relies on Nimbus merging together JSON objects. We have seen this in the messages object which can contain messages from the default configuration, rollouts, and experiments.

We can also add to the actions, triggers and styles object in the same way. This is covered below, and do not need an engineer.

Adding attributes​

Attributes require an application engineer to add values to the JSON object that is passed to the messaging subsystem.

Once in this JSON, this becomes available to JEXL expressions and string subsitution.

Adding custom trigger expressions​

Trigger expressions can be added on a per-message basis, by adding to the triggers object.

{
    "triggers": {
        "DATE_IS_CHRISTMAS": "'-12-25' in date_string"
    },

    "messages": {
        "happy-christmas-en": {
            "trigger": [
                "DATE_IS_CHRISTMAS",
                "USER_EN_SPEAKER"
            ]
            …
            "title": "Happy Christmas",
            …
        },
        "happy-christmas-fr": {
            "trigger": [
                "DATE_IS_CHRISTMAS",
                "USER_FR_SPEAKER"
            ]
            …
            "title": "Joyeux Noel",
            …
        }
    }
}

The DATE_IS_CHRISTMAS is now available as a trigger expression in all the messages in the branch.

It can be used in all messages only when it is added back to the application's list of trigger expressions.

Once a branch has been rolled out, then DATE_IS_CHRISTMAS becomes available to all messages.

Finally, it can be rolled back in to the product by adding it to the nimbus.fml.yaml file, i.e. in Firefox for iOS and Firefox for Android.

Care should be taken to test this new trigger expression before deploying it.

Suitable tools to prototype these expressions:

Adding custom actions​

All actions are implemented as URLs. Ad-hoc URLs can be used for one-off messages, but must contain the scheme and separator: e.g. https://.

URLs that start with :// are taken as deeplinks into the app.

You can add URLs as named actions for use by multiple messages.

{
    "actions": {
        "INSTALL_VPN": "market://details?id=org.mozilla.firefox.vpn"
    }

    "messages": {
        "upsell-vpn-en": {
            …
            "button-label": "VPN maybe?",
            "action": "INSTALL_VPN"
            …
            "trigger": ["USER_EN_SPEAKER"]
        },
        "upsell-vpn-fr": {
            …
            "button-label": "VPN peut-être?",
            "action": "INSTALL_VPN"
            …
            "trigger": ["USER_FR_SPEAKER"]
        },
    }
}

Deeplinks are implemented by the application developers in the app.

There are two parts to making new actions for messages:

  • implementing them so that the app can respond to deeplinks.
  • making those links accessible to messages, by adding them to the nimbus.fml.yaml file.

For example:

If the implementation stage has been done, but the FML part hasn't, you can add the action as part of the branch configuration.

{
    "actions": {
        "FXA_SIGN_IN": "://fxa-signin?signin"
    },

    "messages": {
        "upsell-fxa": {
            …
            "action": "FXA_SIGN_IN"
        }
    }
}

Custom actions can be used in all messages only when they are added back to the application's list of actions.

Lifecycle of a message​

Like all Nimbus enabled features the messaging system configuration (messages, triggers, actions, styles) is likely to undergo a number of phases:

  • experimentation
  • rollout the successful messages to the rest of the audience
  • fold-back into the code: copy the successful JSON branches into the nimbus.fml.yaml files where they will become part of the next release.

Appendices​

List of trigger expressions​

These trigger expressions are based upon the default set of attrbutes available to Nimbus.

Expression nameJEXL expressionDiscussion
USER_RECENTLY_INSTALLEDdays_since_install < 7
USER_RECENTLY_UPDATEDdays_since_update < 7 && days_since_install != days_since_update
USER_TIER_ONE_COUNTRY`('US' in locale
USER_EN_SPEAKER'en' in locale
USER_DE_SPEAKER'de' in locale
USER_FR_SPEAKER'fr' in locale
DEVICE_ANDROIDos == 'Android'
DEVICE_IOSos == 'iOS'
ALWAYStrue
NEVERfalse

These trigger expressions are application specific:

Expression nameJEXL expressionDiscussion
I_AM_DEFAULT_BROWSERis_default_browser == true
I_AM_NOT_DEFAULT_BROWSERis_default_browser == false
USER_ESTABLISHED_INSTALLnumber_of_app_launches >= 4
FUNNEL_PAIDadjust_campaign != ''
FUNNEL_ORGANICadjust_campaign == ''
INACTIVE_1_DAY'app_launched'\|eventLastSeen('Hours') >= 24User has not launched the app for 24h or more
INACTIVE_2_DAYS'app_launched'\|eventLastSeen('Days', 0) >= 2User has not launched the app for 1 d or more
INACTIVE_3_DAYS'app_launched'\|eventLastSeen('Days', 0) >= 3User has not launched the app for 2 d or more
INACTIVE_4_DAYS'app_launched'\|eventLastSeen('Days', 0) >= 4User has not launched the app for 3 d or more
INACTIVE_5_DAYS'app_launched'\|eventLastSeen('Days', 0) >= 5User has not launched the app for 4 d or more
FXA_SIGNED_IN'sync_auth.sign_in'\|eventLastSeen('Years', 0) <= 4User has signed in to FxA in the last 4 years
FXA_NOT_SIGNED_IN'sync_auth.sign_in'\|eventLastSeen('Years', 0) > 4User has not signed in to FxA in the last 4 years
USER_INFREQUENT'app_launched'\|eventCountNonZero('Days', 28) >= 1 && 'app_launched'\|eventCountNonZero('Days', 28) < 7User definition
USER_CASUAL'app_launched'\|eventCountNonZero('Days', 28) >= 7 && 'app_launched'\|eventCountNonZero('Days', 28) < 14User definition
USER_REGULAR'app_launched'\|eventCountNonZero('Days', 28) >= 14 && 'app_launched'\|eventCountNonZero('Days', 28) < 21User definition
USER_CORE_ACTIVE'app_launched'\|eventCountNonZero('Days', 28) >= 21User definition
LAUNCHED_ONCE_THIS_WEEK'app_launched'\|eventSum('Days', 7) == 1

It is possible this table is out of date. The definitive source of truth for this in the code itself.

List of actions​

These all correspond to the existing deeplinks in each app, so are entirely app specific.

ActionDescriptionCorresponding Deeplink
ENABLE_PRIVATE_BROWSINGfirefox://enable_private_browsing
INSTALL_SEARCH_WIDGETfirefox://install_search_widget
MAKE_DEFAULT_BROWSERfirefox://make_default_browser
OPEN_SETTINGS_ACCESSIBILITYfirefox://settings_accessibility
OPEN_SETTINGS_ADDON_MANAGERfirefox://settings_addon_manager
OPEN_SETTINGS_DELETE_BROWSING_DATAfirefox://settings_delete_browsing_data
OPEN_SETTINGS_LOGINSfirefox://settings_logins
OPEN_SETTINGS_NOTIFICATIONSfirefox://settings_notifications
OPEN_SETTINGS_PRIVACYfirefox://settings_privacy
OPEN_SETTINGS_SEARCH_ENGINEfirefox://settings_search_engine
OPEN_SETTINGS_TRACKING_PROTECTIONfirefox://settings_tracking_protection
OPEN_SETTINGS_WALLPAPERSfirefox://settings_wallpapers
OPEN_SETTINGSfirefox://settings
TURN_ON_SYNCfirefox://turn_on_sync
VIEW_BOOKMARKSfirefox://urls_bookmarks
VIEW_COLLECTIONSfirefox://home_collections
VIEW_HISTORYfirefox://urls_history
VIEW_HOMESCREENfirefox://home

List of attributes​

By convention these are in snake_case.

These attributes are defined by the Nimbus SDK.

AttributeTypeDescription
app_namestring
app_idstring
channelstring
app_versionstring
app_buildstring
architecturestring
device_manufacturerstring
device_modelstring
localestring
osstring
os_versionstring
android_sdk_versionstring
debug_tagstring
installation_datestring
home_directorystring

These attributes are application specific.

Unfortunately, the JEXL evaluator used does not have support for negation, so boolean attributes use equality or inequality.

AttributeTypeDescriptionVersions
date_stringstringIn YYYY-MM-DD format
is_default_browserbooleanJEXL.rs does not implement boolean negation !
number_of_app_launchesintIndicates how many times the app has been launched.
adjust_campaignstring?The campaign id parameter as derived by Adjustv111
adjust_networkstring?The network parameter as derived by Adjustv111
adjust_ad_groupstring?The Ad Group parameter as derived by Adjustv111
adjust_creativestring?The Creative parameter as derived by Adjustv111
are_notifications_enabledbooleanJEXL.rs does not implement boolean negation !v111

It is possible this table is out of date. The definitive source of truth for this in the code itself.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/remote-localization/index.html b/messaging/remote-localization/index.html index aaf3175a4..06c12f490 100644 --- a/messaging/remote-localization/index.html +++ b/messaging/remote-localization/index.html @@ -4,13 +4,13 @@ Remote Localization | Experimenter Docs - +

Remote Localization

Remote localization is available for all message types but it does require that strings are landed early to give localizers enough time. This is done via the ms-language-packs repo.

As a fallback, we can use hardcoded strings in the message definition.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/messaging/telemetry/index.html b/messaging/telemetry/index.html index 48706fec5..fd0f57d5f 100644 --- a/messaging/telemetry/index.html +++ b/messaging/telemetry/index.html @@ -4,7 +4,7 @@ Messaging Telemetry | Experimenter Docs - + @@ -12,7 +12,7 @@

Messaging Telemetry

Event telemetry is sent for any interaction with the messaging surfaces and additionally for impressions (when the message is shown). The following types of user interaction is captured by telemetry:

  • Impression
  • Primary click
  • Secondary click (when available)
  • Dismiss

Telemetry pings will include the message id, experiment and branch slug (if enrolled) and the event type.

Telemetry from all messaging surfaces (except doorhangers when not enrolled in an experiment) includes client id in the event ping. Special care should be taken not to associate any browsing history with messaging events: showing a message when the user visits a particular website is not allowed because it would accidentally leak information about that visit.

Additional telemetry is available when the message is delivered as an experiment:

  • Exposure
  • Reach

These events are sent once per feature per browsing session.

Exposure (actually displayed to the user) is sent just before the message is shown (similar to Impressions) and only for the messages that are defined as features in the FeatureManifest.

A client will send one Reach event for every message available in the branches that the client is not enrolled in. Example: Experiment has 3 branches [control, treatment-1, treatment-2]. If the user is enrolled in the control branch then 2 Reach pings will be sent for the messages in the treatment-1,2 branches. The reach ping is sent when the user action (trigger) associated with the message and the targeting conditions are satisfied (in other words when the user would have seen the message if the user would have been enrolled in that branch). Exposure and Reach events for a feature get sent when the trigger associated with that message fires and if the message targeting matches.

Debugging telemetry pings

Make sure browser.ping-centre.log pref is enabled. Open the Browser Toolbox and interact with the message, any telemetry pings will be logged to the console.

Because of the implementation of moments pages, telemetry is only sent for when a message is matched (the page will be shown in the next browsing session). No other telemetry is available.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/mobile-behavioral-targeting/index.html b/mobile-behavioral-targeting/index.html index 7cdacfecc..24eb1cd09 100644 --- a/mobile-behavioral-targeting/index.html +++ b/mobile-behavioral-targeting/index.html @@ -4,13 +4,13 @@ Behavioral Targeting | Experimenter Docs - +

Behavioral Targeting

Behavioral targeting is a term used to describe a set of jexl transforms which can be used to target specific user behaviors. User behaviors might be the user opened the app, the user logged in, the user navigated to a specific view, or any user-triggered event that has Glean metrics associated with it.

In order to maintain user privacy, this entire system lives in the Nimbus client launched by our mobile applications. Additionally, events are recorded and stored as counts in time interval-based buckets, allowing for a predictably small amount of disk spaceto be used for this event store.

Event Bucketing​

Stored events are bucketed into time intervals. The time intervals are Minutes, Hours, Days, Weeks, Months, and Years. No additional setup is required for this bucketing process, it is handled entirely by the SDK. Buckets for each of the time intervals are created and stored whenever a new event is recorded.

Bucket Advancement & Retention​

When buckets are created, they have a starting date. This date is set to Jan 1 00:00:00 UTC of the current year. As time passes, the current time is incremented by the time difference in whole intervals and the buckets are advanced that many positions.

Bucket advancement occurs when an event is recorded, or when a query is performed. Buckets always advance based off whole increments of their time interval; Minutes will advance by full minutes, Hours by full hours, and so on. One exception to this rule is the Months time interval – it advances in increments of 28 days.

Based on the current datetime, the buckets may not advance at all, or may advance so much that all the buckets are cleared. As an example, if the current date for the Hours bucket is set to May 1 10:00:00 UTC, and an event is recorded at May 1 12:45:00 UTC, the buckets will be advanced 2 positions, the current date will be updated to May 1 12:00:00 UTC, and the event will be recorded in the bucket for the 12pm hour.

Retention​

Each time interval has a maximum number of buckets it retains. As the time intervals move forward, buckets are rotated off of the deque and new buckets are added. If a query is performed that would go beyond the bucket count, it instead is cut off at the bucket count.

The following is a list of the time intervals and their bucket counts:

Minutes 60
Hours   24
Days    56
Weeks   52
Months  12
Years   4

Querying for User Behavior​

User behaviors are recorded in the same way as Glean events, and there are a number of ways in which they can be queried.

The following is a list of jexl transforms that exist within the Nimbus targeting helper, and thus are usable on all projects that use the Nimbus Rust library.

TransformDescriptionArgsReturns
eventSumCalculates the sum of all bucket values within the rangeinterval, bucket_count, starting_bucketint
eventCountNonZeroCalculates the total number of buckets with a non-zero value within the rangeinterval, bucket_count, starting_bucketint
eventAverageCalculates the average of all event bucket values within the rangeinterval, bucket_count, starting_bucketfloat
eventAveragePerNonZeroIntervalCalculates the average of all buckets with a non-zero value within the rangeinterval, bucket_count, starting_bucketfloat
eventLastSeenReturns the number of whole time intervals between the starting bucket and the first bucket with a non-zero valueinterval, starting_bucketint

Designing Experiments & Behavior Triggers​

The following are the existing options for behavioral targeting as defined in Experimenter (found under Advanced Targeting in the audience editor). In order to use these targeting options, the application must be Firefox or Focus for Android or iOS.

NameDescriptionTargeting String
Core Active UsersA user who has used the application 21 out of the last 28 days.
'app-opened-event'|eventCountNonZero('Days', 28, 0) >= 21
Recently Logged In UsersA user who has logged into Sync within the last 12 weeks.
'sync-signin-event'|eventCountNonZero('Weeks', 12, 0) > 0

There are many ways these queries could be used to our advantage when writing behavior-oriented code. One example could be to show a certain message to users after they have launched the app n times, and after 12hrs has passed from when they first opened the application.

'app-opened-event'&#124;eventSum('Years', 4, 0) >= 3 &&      // The sum of app opened events within the last four years must be 3 or more
(
  'app-opened-event'&#124;eventSum('Hours', 12, 12) >= 1 ||  // The sum of app opened events within 12hrs, starting 12hrs ago
  'app-opened-event'&#124;eventSum('Days', 7, 1) >= 1 ||     // The sum of app opened events within 7 days, starting 1 day ago
  'app-opened-event'&#124;eventSum('Weeks', 52, 1) >= 1      // The sum of app opened events within 52 weeks, starting 1 week ago
)                                                            // Any one of these results must have been 1 or more

Instrumented Events​

The following are the events that are currently instrumented in Firefox for iOS and Android, respectively:

DescriptionEvent
Application openedapp_opened
User logged into Syncsync_auth.sign_in

Engineering​

In order to instrument a new behavior/event, an equivalent call to the Nimbus event recording method must be made alongside the call to record a Glean event.

note

While this process is currently required, long-term we hope to have a hook in Glean that will record certain events automatically.

On Firefox for Android, a call should be made to components.analytics.experiments.recordEvent immediately following the Glean event being recorded. The argument should be the event name.

app/src/main/java/org/mozilla/fenix/HomeActivity.kt
import org.mozilla.fenix.ext.components

// ...
open class HomeActivity : LocaleAwareAppCompatActivity(), NavHostActivity {
    // ...
    final override fun onCreate(savedInstanceState: Bundle?) {
        // ...
        if (settings().isTelemetryEnabled) {
            // ...
            safeIntent
                ?.let(::getIntentSource)
                ?.also {
                    Events.appOpened.record(Events.AppOpenedExtra(it))
                    // This will record an event in Nimbus' internal event store. Used for behavioral targeting
                    components.analytics.experiments.recordEvent("app_opened")
                }
        }
        // ...
    }
}

Example in PR

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/mobile-enrollment-state-machine/index.html b/mobile-enrollment-state-machine/index.html index 76622b375..9d0be5f4e 100644 --- a/mobile-enrollment-state-machine/index.html +++ b/mobile-enrollment-state-machine/index.html @@ -4,7 +4,7 @@ Enrollment State Machine | Experimenter Docs - + @@ -14,7 +14,7 @@ The Enrolled and Disqualified statuses end up here when a recipe ends.

Error​

The client threw an error during enrollment evaluation[4].

Notes​

  1. The explicit order in which recipe criteria are evaluated is as follows:
    1. Feature conflicts
    2. Targeting
    3. Bucketing
  2. This state can be reached via Firefox mobile's secret menu.
  3. Experiments and rollouts do not share feature conflicts. A client can be enrolled in up to one rollout and one experiment for a given feature, unless that feature has been enabled for coenrollment.
  4. This status will only contain enrollments for recipes in which the client has never been enrolled. If the client is enrolled but throws an error during enrollment re-evaluation it will be moved to Disqualified::Error

State Diagrams​

The following state diagrams describe the interactions between states for the enrollment state machine in the mobile Nimbus SDK.

Experiments​

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/mobile-feature-api/index.html b/mobile-feature-api/index.html index dc7bb084a..362be15ce 100644 --- a/mobile-feature-api/index.html +++ b/mobile-feature-api/index.html @@ -4,13 +4,13 @@ Feature API | Experimenter Docs - +

Feature Variables and Me

About this document​

This document is to illustrate the concepts of the Feature Variables feature of Nimbus, internally known as the Feature API.

The API used by software engineers is relatively small, so this document is not just for them. This document is also for designers, product managers and engineering managers who design, work on, and are responsible for features in the mobile apps we build. Successful experimentation requires multiple parts of the team to share an understanding of these concepts.

👋 Information

Neither Nimbus, nor all of the Feature Variables work are finished yet, but they are certainly still useful. This document will talk about features that aren't yet implemented, but will serve to illustrate the concepts. A sidebar like this will tell you when a feature isn't ready.

⛅️🔬🔭 Nomenclature

Much of the literature around the methodology of experiments Nimbus implements has its roots in medical testing. The feature variables API does not require understanding of double blind experiments or data-science, but this document will occasionally use words like "treatment" or "exposure".

Document status​

Note: The code in this document still works, but is not the supported way of interacting with the Nimbus Feature API.

This document is still useful for the concepts. The Feature Manifest Language specification would be the best place for engineers to go having read this document.

Introduction​

The "Feature" in the "Feature Variables API" refers to features of the application. It's pretty abstract, and how the application is divided up into features is up to the product teams. Over time, a feature may be involved in many experiments.

We can be more specific here:

⛅️🔬🔭 Concept

A feature is an identifiable part of the app in which a change might be detectable by a user.

However, there is one rule:

⛅️🔬🔭 Concept

For a given user each feature can only be involved in one experiment at a time.

If we see user change behavior after being exposed to an experimental treatment we need to be able to attribute it to that treatment, not another from a different experiment.

There is one exception to this rule, which we will discuss later

One easy way to start thinking about features, would be to identify user-visible surfaces of the app: the new-tab screen, the app-menu, the context-menu, the onboarding.

Imagine you're a designer, doing a re-design of the app's menu. It would be natural to call the app menu a "feature" of the app.

You've got some hypotheses around the icons, and whether they should be to right or left of the text. There is also some uncertainty around the copy for each menu item.

The uncertainties and hypotheses you have might translate into variations and variables you might configure the menu with. If these variables and variations are documented, they should travel as an adjunct to or part of the design itself. Later these will be turned into a more formal document that lives with the code, but it is at this stage when they should be thought about.

To narrow the scope for documentation purposes, we'll focus on a small number of variables. Within the app-menu feature, we'll consider the menu being made up of menu items, and we'll zoom in on the settings menu item.

As a team communication tool, it may help to consider a JSON object to enumerate the variables that are configurable for the settings icon, and their defaults.

{
    "settings-menu-item-title": "Settings",
    "settings-menu-item-icon": "ic_settings",
    "settings-menu-item-enabled": true,
    "settings-menu-item-action": "firefox://settings"
}

This JSON object looks like what experimenters will be putting into branch configuration screens in Experimenter, under Feature Configuration.

Where did these keys come from? This is not up to Nimbus, but up to the app, i.e. the app team. In this hypothetical case, you have some theories about the title and the icon, and now the app needs to get those values from nimbus.

⛅️🔬🔭 Naming Convention

Nimbus doesn't take a view on how you arrange the JSON, but by convention, like all other identifiers, it prefers kebab-case (i.e. lower-case-words-joined-with-dashes).

In the app code, the Variables object is a wrapper around this JSON object, and we have a number of getters to get values out. Notice that all getters return optional types, so it is up to the app developer to provide a default value.

let variables = nimbus.getVariables("app-menu")
let action: String = variables.getString("settings-menu-item-action") ?? "firefox://settings"
let title: String = variables.getText("settings-menu-item-title") ?? Strings.AppMenuSettingsTitle
let icon: UIImage = variables.getImage("settings-menu-item-icon") ?? UIImage(named: "icon-photon-gear")
let isEnabled: Bool = variables.getBool("settings-menu-item-enabled") ?? true

It is a similar story in Kotlin:

val variables = nimbus.getVariables("app-menu")
val action: String = variables.getString("settings-menu-item-action") ?: "firefox://settings"
val title: String = variables.getText("settings-menu-item-title") ?: context.getString(R.string.app_menu_settings_title)
val icon: Drawable = variables.getDrawable("settings-menu-item-icon") ?: context.getDrawable(R.drawable.ic_settings)
val isEnabled: Bool = variables.getBool("settings-menu-item-enabled") ?? true

A few things to talk about here:

Fundamental types: Strings, Int, Bool​

getString(key), getBool(key) and getInt(key) all return values as found in the JSON. If there is a disagreement about types, i.e. if the app is expecting a string, and the value in the JSON is an integer, the app gets nil or null.

Everything is optional​

If the app asks for a variable that is not specified in this particular experiment, then it gets back nil or null.

It is thus imperative that the app has a reasonable default. On the other hand, this allows us to have experiments which configure only small parts of a feature.

Text resources​

In the example above, the title uses getText(). This gets a string value with getString(). The value is then used as a key to look up the app resource string.

For example, on Android: getText("settings-menu-item-title") may get a string from the JSON "app_menu_settings_title", which is then resolves to R.string.app_menu_settings_title which is then used to look up the String in the Resources.

On iOS, getText uses a similar process via LocalizedString to look up the translated strings. You can specify the tableName as well as the key in the single value by joining it with a slash.

For example, getText("settings-menu-item-title") may get a string from the JSON "AppMenu/SettingsTitle" which uses bundle.localizedString("SettingsTitle", tableName: "AppMenu") to look up a localized string. If the app doesn't use tableName, then you can omit it: e.g. AppMenu_SettingsTitle would look for NSLocalizedString("AppMenu_SettingsTitle").

If getString() returned a string, and the resource lookup didn't succeed, getText() falls back to the string.

This means that you can use either pre-translated strings to try out experiments across locales, or target your experiment on a single language.

🎛 Configuration

Resource lookup via Bundle and Context uses the objects passed to nimbus at construction time at app-startup. In Firefox for iOS and Fenix this is Bundle.main and context.applicationContext respectively.

📷 Image resources​

In the example above, the icon uses getImage() and its Android analog getDrawable(). This gets a string value from the JSON with getString() and then uses that value to look up the pre-bundled resource.

For example on Android: getDrawable("settings-menu-item-icon") uses getString("settings-menu-item-icon") which might get the value "ic_settings" from JSON. This is then resolved to R.drawable.ic_settings, which is then resolved to context.resources.getDrawable(R.drawable.ic_settings).

On iOS: getImage("settings-menu-item-icon") uses getString("settings-menu-item-icon") which might get the value "icon_photon_gear", which is then used to get the named UIImage with UIImage(named:in:).

Making JSON more manageable​

We focused on the settings menu item in the above example, as a way of making a small enough example to reason about in this documentation, but it made for some very long variable names. The Variables object has itself a getVariables(key: String) method to make navigating the JSON more easily. This in turn allows the JSON to be organized in different ways.

Zooming out of our example above, which had just one menu item: we can re-arrange the JSON to accommodate multiple menu items, with a simpler nested structure:

{
    "settings": {
        "icon": "ic_settings",
        "title": "Settings",
        "action": "firefox://settings",
        "enabled": true
    },
    "bookmarks": {
        "icon": "ic_bookmarks",
        "title": "View Bookmarks",
        "action": "firefox://bookmark_list",
        "enabled": true
    },
    "history": {
        "icon": "ic_history",
        "title": "View History",
        "action": "firefox://history_list",
        "enabled": true
    }
}

This might be accessed in Kotlin with:

val menuVariables = nimbus.getVariables("app-menu")
var settingsItem = menuVariables.getVariables("settings").let { vars ->
    val action: String = vars?.getString("action") ?: "firefox://settings"
    val title: String = vars?.getText("title") ?: context.getString(R.string.app_menu_settings_title)
    val icon: Drawable = vars?.getDrawable("icon") ?: context.getDrawable(R.drawable.ic_settings)

    MenuItem(icon, title, action)
}

In Swift:

let menuVariables = nimbus.getVariables("app-menu")
let settingsItem = menuVariables.getVariables("settings") { vars ->
    let action: String = vars?.getString("action") ?? "firefox://settings"
    let title: String = vars?.getText("title") ?? Strings.AppMenuSettingsTitle
    let icon: UIImage = vars?.getImage("icon") ?? UIImage(named: "icon-photon-gear")
    let isEnabled: Bool = vars?.getBool("enabled") ?? true

    MZMenuItem(icon: icon, title: title, action: action)
}

👋 Information

variables.getVariables() can be arbitrarily deep. variables.getVariables() returns an optional Variables object.

Structural types​

Lists and dictionary types are supported for every type.

For example: getStringList(key) returns an list of Strings ([String]? or List<String>?). getIntMap(key) returns a [String: Int]? or Map<String, Int>?. Getting a Map of anything will always have a key type String.

This includes nested variables and enums.

For example, we may have configured a feature to accept some JSON that may look like this:

{
    "ordering": ["settings", "bookmarks", "history"],
    "items": {
        "settings": {
            "icon": "ic_settings",
            "title": "Settings",
            "action": "firefox://settings"
        },
        "bookmarks": {
            "icon": "ic_bookmarks",
            "title": "View Bookmarks",
            "action": "firefox://bookmark_list"
        },
        "history": {
            "icon": "ic_history",
            "title": "View History",
            "action": "firefox://history_list"
        }
    }
}

The application code to read that JSON now looks like this in Kotlin:

fun toMenuItem(vars: Variables): MenuItem? {
    val action: String = vars?.getString("action") ?: return null
    val title: String = vars?.getText("title") ?: return null
    val icon: Drawable = vars?.getDrawable("icon") ?: return null
    return MenuItem(icon, title, action)
}

val menuVariables = nimbus.getVariables("app-menu")
// Use the ordering from the experiment or the hardcoded version.
val ordering: List<String> = menuVariables.getStringList("ordering") ?: hardcodedOrdering
// Get a list of MenuItem items from the "items" object, using toMenuItem.
val experimentalItems: Map<String, MenuItem> = menuVariables.getVariablesMap("items", ::toMenuItem) ?: mapOf()

// use the ordering to lookup the menu items from the experiment or the hardcoded version.
val items: List<MenuItem> = ordering.mapNotNull { id -> experimentalItems[id] ?: hardcodedItems[id] }

// Use the items to make the menu

And in Swift:

func toMenuItem(vars: Variables): MZMenuItem? {
    guard let action = vars.getString("action"),
        let title = vars.getText("title"),
        let icon = vars.getDrawable("icon") else {
            return nil
        }
    return MZMenuItem(icon: icon, title: title, action: action)
}

let menuVariables = nimbus.getVariables("app-menu")
// Use the ordering from the experiment or the hardcoded version.
let ordering = menuVariables.getStringList("ordering") ?? hardcodedOrdering
// Get a list of MZMenuItem items from the "items" object, using toMenuItem.
let experimentalItems = menuVariables.getVariablesMap("items", transform: toMenuItem) ?? [:]()

// use the ordering to lookup the menu items from the experiment or the hardcoded version.
let items: [MZMenuItem] = ordering.compactMap { id in experimentalItems[id] ?? hardcodedItems[id] }

// Use the items to make the menu

Building the menu like this allows the experiment to add and remove menu items remotely, while still providing a default experience.

Enumerations of values​

The above example leans quite heavily on Strings. The code may be written in such a way that an enum would be more appropriate.

In this contrived example of a homescreen with different sections, we see some JSON with a list and a map. The items of the list correspond to the keys of the map.

{
    "section-ordering": ["topSites", "highlights", "collections"],
    "sections-rows": {
        "topSites": 1,
        "highlights": 1,
        "collections": 2,
        "recentlyViewed": 0
    }
}

We can represent these items in Kotlin as an enum.

enum class SectionId {
    recentlyViewed,
    topSites,
    highlights,
    collections
}

👋 Information

enum classes in Kotlin can be resolved only by their name, which cannot include hyphens.

Also in Swift:

enum SectionId: String {
    case recentlyViewed
    case topSites
    case highlights
    case collections
}

Then, when preparing our Home screen, we can get the list:

val variables = nimbus.getVariables("home-screen")
val ordering: List<SectionId>? = variables.getEnumList("section-ordering")
val sectionsRows: Map<SectionId, Int>? = variables.getIntMap("sections-rows")?.mapKeysAsEnums()

and in Swift.

let variables = nimbus.getVariables("home-screen")
let ordering: [SectionId]? = variables.getEnumList("section-ordering")
let sectionRows: [SectionId: Int]? = variables.getIntMap("section-rows").compactMapKeysAsEnums()

Recording exposure events​

⛅️🔬🔭 Enrollment versus Exposure

When a client is selected to take part in an experiment, they are assigned a branch. This is enrollment.

However, the user may not be exposed to the branch until sometime later. The exposure is the earliest moment that the user could be affected by the experimental treatment.

Nimbus records the enrollments and exposure events using Glean.

Enrollments are recorded at each app start-up, and exposure events each time an exposure happens.

For experiments in Firefox for iOS and Android, enrollment happens shortly after app-startup.

In our example above, the app menu is constructed when a tab is open. The user is only exposed to the values of the JSON when they tap on the open-menu icon.

By default, exposure is recorded when nimbus.getVariables(featureId: String) is called. Whichever experiment the feature is enrolled in— always exactly zero or one— has an exposure event recorded.

A second, optional argument is allowed for this method getVariables, to change this default behavior.

Here, the menu is constructed with variables from Nimbus, but the user doesn't see the menu until they open it.

val menu = createMenu(
    nimbus.getVariables("app-menu", sendExposureEvents = false)
)
val menuButton = Button(
    icon = R.drawable.ic_menu,
    onButtonPressed = {
        nimbus.recordExposureEvent("app-menu")
        show(menu)
    }
)

This is a caricature of the same code in Swift.

let menuSheet = createMenuSheet(
    nimbus.getVariables("app-menu", sendExposureEvents: false)
)
let menuButton = UIButton()
menuButton.addTarget(self, action: #selector(didOpenMenu), for: .touchUpInside)

func didOpenMenu() {
    let nimbus = Nimbus.shared
    nimbus.recordExposureEvent("app-menu")
    viewController.present(menuSheet, animated: true, completion: nil)
}

Nimbus will take care of finding out what experiment, if any, the user is enrolled in when using this feature.

Using configurable features to experiment with another​

The feature itself may be configurable, but we don't have to limit feature configuration to experiments about that feature.

We can imagine a world where we have multiple configurable features, say: an app-menu, onboarding and newtab. On each of these features we have a messaging surface, and we want to run an experiment to find which is the best surface to show the message about a behavior we wish to maximize: setting the browser to be the device default.

Q Can we configure an experiment to test each of the message on each of these messaging surfaces?

A This would be done with an experiment that has three branches, and each branch configures exactly one feature. The application code doesn't have to know about the linkage between the features in this experiment, just get its configuration from Nimbus.

If a user is enrolled in that experiment, no other experiment is allowed to use the features involved.

We might also imagine a world where we have multiple features as before. Two different product teams are experimenting with two new capabilities of the app: both require onboarding instructions, one has an entry point via a app menu item, and the other has an entry point in the new tab screen.

If it was one product team where communication is high, perhaps they might run one experiment, with two treatment branches: one branch with configuration for the onboarding and app-menu features, and one branch with configuration for the onboarding and new-tab features.

Both teams require the onboarding feature. This allows each team to run their own experiments, which do not interfere with one another.

⛅️🔬🔭 Concept

While for any given user a feature may be involved in only one experiment, one experiment should be able to configure multiple features.

Because both product teams' experiments require the onboarding experiment, no user will be involved in both experiments.

For such an experiment, an experiment would have two branches, each of which configure two features.

The exception to the rule...​

"If a user is enrolled in that experiment, no other experiment is allowed to use the features involved." - this document, above.

As always, there will be an exception to the rule. In the case of feature enrollment, there is a way to allow certain features to be co-enrolled. This document will give you more information about defining co-enrolling features and which features are currently instrumented to be co-enrollable.

🔧⚙️ Working with configurable features​

Throughout the process of designing and building these configurable features, the feature variables have needed to be documented. At first, when the feature is being envisioned and designed, the variables should travel with the designs themselves.

When the feature is being implemented, these variables will begin to acquire concrete names, types and organization, which will be used extract JSON from the Nimbus SDK and configure the application features themselves. This documentation will begin to take shape and textual organization that travel in the app's repository.

When the feature is being tested, QA testers are going to want to configure the features within bounds and tolerances set by the designs and the engineers.

Finally, when the feature is part of experiments, then the experiment owner, setting the branches in Experimenter needs to be able configure the branches with variables with spellings and organization that match the app implementation.

The Feature Manifest Language specification would be the best place for engineers to go having read this document.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/mobile-first-run-experiments/index.html b/mobile-first-run-experiments/index.html index 835f55328..f67be95d8 100644 --- a/mobile-first-run-experiments/index.html +++ b/mobile-first-run-experiments/index.html @@ -4,13 +4,13 @@ First Run Experiments | Experimenter Docs - +

First Run Experiments

First Run Experiments are incredibly important to the experimentation ecosystem, but their functionality comes with some key differences from standard experiments. It is highly advised that experiment owners read through this documentation before writing First Run Experiments.

info

While the concept of First Run Experiments is also supported by Firefox for Desktop, this particular documentation only applies to our mobile applications such as Firefox and Focus for Android/iOS.

What is a First Run Experiment?​

A First Run Experiment is one that needs to enroll clients on their first launch of the application. All First Run Experiments bundled with a particular app version would be evaluated for enrollment on the very first run of the application after it is installed.

An experiment can be marked as a First Run Experiment by checking the "First Run Experiment" checkbox while editing the experiment in the audience page.

The First Run Experiment checkbox is shown on the audience page of an experiment being edited

Similarly to sticky enrollment, certain advanced targeting configurations can require an experiment to be first run.

The First Run Experiment checkbox is selected and cannot be edited because the advanced targeting configuration selected requires the experiment to be a First Run Experiment.

The differences between First Run Experiments and standard experiments show themselves in a few key areas, described below.

Application Bundling​

Unlike traditional experiments, First Run Experiments must be bundled with the app. This allows Nimbus to start quickly without needing to make a call to remote settings to fetch the live experiments.

This means that any new First Run Experiments are added to the application's source code repository. This happens automatically via the update-experiments Github action. Below is an example job configuration for this action.

jobs:
  update-nimbus-experiments:
    name: "Update Nimbus Experiments"
    runs-on: ubuntu-latest
    steps:
      - name: "Checkout Main Branch"
        uses: actions/checkout@v3
        with:
          path: $DIRECTORY
          ref: main
          fetch-depth: 0
      - name: "Update Experiments JSON"
        id: update-experiments-json
        uses: mozilla-mobile/update-experiments@v3
        with:
          repo-path: $DIRECTORY
          output-path: path/to/initial_experiments.json
          experimenter-url: https://experimenter.services.mozilla.com/api/v6/experiments-first-run/
          app-name: $APP_NAME
          branch: automation/update-nimbus-experiments
      - name: Create Pull Request
        id: create-pull-request
        uses: peter-evans/create-pull-request@v4
        if: steps.update-experiments-json.outputs.changed == 1 && steps.update-experiments-json.outputs.changed-branch == 1
        with:
          token: ${{ secrets.GITHUB_TOKEN }}
          path: $DIRECTORY
          branch: automation/update-nimbus-experiments
          commit-message: "update initial_experiments.json based on the current first-run experiments in experimenter"
          title: "Update initial experiments JSON for Nimbus"
          body: "This (automated) PR updates the initial_experiments.json on the `main` branch"
          delete-branch: true

The only file type currently supported is JSON, and this is usually named initial_experiments.json. Some examples of how this workflow and file are used can be seen in this PR and the initial_experiments.json file in the Firefox iOS repository.

Enrollment​

For traditional experiments, enrollment is a short period at the beginning of a live experiment's lifecycle where the experiment can enroll new clients. The Nimbus SDK does not do any date calculations when determining whether an experiment is enrolling clients, instead relying on the isEnrollmentPaused experiment field.

Because First Run Experiments are bundled with the application, that field will remain true in the application bundle for the duration of the release. As a result, even after enrollment is paused remotely via Experimenter, First Run Experiments on the app's first run will still be evaluated and enrolled.

Even though clients will be enrolling for the duration of the release, only clients enrolled during the enrollment period as defined in Experimenter will be considered for analysis. As a result, it can be worthwhile to set an earlier end date in Experimenter if only to receive your results earlier.

When defining the enrollment length and experiment lifetime, it is wise to take this fact into account in planning. As an example, if a First Run Experiment should enroll new clients for one week, its enrollment end date should be one week after the date of release for the version it is targeting.

Advanced Targeting​

Many use cases for First Run Experiments require advanced targeting configurations. To add a new advanced targeting configuration, a pull request should be opened to the Experimenter repository, adding the configuration to this file.

Please follow the contributing guidelines when opening PRs to Experimenter. If you require assistance, please reach out in the #ask-experimenter channel on Slack.

How do I know if an experiment should be first run?​

In short, if your experiment makes changes to onboarding, needs data from brand-new clients, or otherwise relates to clients who are using Firefox on their device for the first time, then your experiment should most likely be a First Run Experiment.

Similarly with advanced targeting above, if you have any questions or would like help in making this determination, please reach out in the #ask-experimenter channel on Slack.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/mobile-surveys/index.html b/mobile-surveys/index.html index 7d58bdc23..e65e8f750 100644 --- a/mobile-surveys/index.html +++ b/mobile-surveys/index.html @@ -4,13 +4,13 @@ Surveys | Experimenter Docs - +

Mobile Survey Workflow

The required general steps to launch a mobile survey are:

  1. Create the survey in the survey provider
  2. Determine the survey invitation message copy
  3. Determine the audience (targeting and sizing)
  4. Configure the survey in Nimbus
  5. QA
  6. Launch
  7. End

Below, we'll elaborate on each step.

Create the survey​

  • Make sure the survey is configured to listen for URL parameters.
  • Get the survey link, perhaps something like https://qsurvey.mozilla.com/s3/<survey_name>?app=android&userid={uuid}
    • Note that some URL parameters are added to the end, make sure your survey provider supports these (Alchemer does).
    • Note also the special formatting around the userid={uuid} parameter, this is used to generate a unique identifier for each message click which allows survey responses to be linked to telemetry. See the docs for more info.

Invitation message copy​

The following copy elements are generally available for surveys:

  • Text (required), historically we've used Please help Firefox by taking this short survey
  • Title (optional), historically this was not used
  • Button Label (optional), historically we've used Take survey

The full docs are available here

Determine the audience​

Audience composition for surveys has two parts: the targeting criteria (who are eligible to be enrolled) and the sizing (what fraction of eligible clients will be invited)

Targeting for surveys is either done through Nimbus or through the messaging system. Note that if custom targeting must be implemented, it should be done now and prior to the sizing calculations.

For sizing, the formula for a single survey branch is: desired responses / assumed response rate / fraction viewing invitation / estimated active users in enrollment period

To elaborate on each item:

  • Desired responses: the number of desired survey responses, e.g. 1000. This is determined by the survey owner.
  • Assumed response rate: what proportion of users who see the survey invitation are expected to click on it. Generally values around ~2% (1%-5% range) have been observed historically.
  • Fraction viewing invitation: what proportion of users enrolled in the experiment are expected to see the survey invitation. In the absence of specific targeting, this is expected to be around 50%, due to the large number of new and bouncing users on mobile (many users download the app, are enrolled, but then do not open again). If the targeting for this survey is more specific, this fraction may be wildly different. Reach out to data science for assistance in this case.
  • Estimated active users in enrollment period: this is the estimated number of clients that will be active at all during the enrollment period. For a 1-week enrollment period, this is equivalent to current WAU. For other enrollment periods, custom calculations are necessary.

Example calculation:

Using 1000 desired responses, 2% click-through-rate, 50% viewing the invitation, and 1M estimated active clients during the period, the sizing for this survey would be 10%.

Note that this calculation was done for a single survey branch. If you're running multiple branches in Nimbus (this is not the same thing as multiple branches in the survey provider!) then you'll need to scale this value by the number of branches.

Configure in Nimbus​

Prerequisite: at least one person on the team will have to have gone through experiment owner training.

If you want to run surveys both on Android and iOS, you'll need to configure a separate delivery for each one as deliveries are client-specific.

The schema for the JSON value is below:

{
  "actions": {},
  "message-under-experiment": "<GIVE YOUR MESSAGE A NAME>",
  "messages": {
    "<USE THE SAME NAME AS message-under-experiment>": {
      "action": "<SURVEY LINK GOES HERE>",
      "button-label": "<BOTTON LABEL COPY GOES HERE>",
      "style": "SURVEY",
      "text": "<TEXT COPY GOES HERE>",
      "trigger": ["LIST", "OF", "TARGETING"]
    }
  },
  "on-control": "show-next-message",
  "styles": {},
  "triggers": {}
}

See below for an example (from the Viewpoint survey):

{
  "actions": {},
  "message-under-experiment": "viewpoint-invitation-message",
  "messages": {
    "viewpoint-invitation-message": {
      "action": "https://qsurvey.mozilla.com/s3/302e5c853d63?app=android&userid={uuid}",
      "button-label": "Take survey",
      "style": "SURVEY",
      "text": "Please help Firefox by taking this short survey",
      "trigger": ["USER_EN_SPEAKER"]
    }
  },
  "on-control": "show-next-message",
  "styles": {},
  "triggers": {}
}

QA​

  • QA will test to make sure that users see the message, that tapping the button leads to the survey
  • In addition, you’ll probably want to check the survey results to confirm that you see QA's answers in the responses (and so you can exclude QA responses from your analysis)
  • If you're using the {uuid} URL parameter to link surveys with telemetry, you'll want to confirm that this is configured correctly and that you can find the appropriate telemetry (message clicked event) matching the QA responses.

Launch​

Once QA is complete, the survey can be launched. The survey owner should request to launch and then post a message in #ask-experimenter asking for an approver. As of the time of writing these docs, Rosanne Scholl & Daniel Berry are qualified reviewers for mobile surveys. If your team plans to run many surveys, it's recommended to have some members go through reviewer training so that the team can self-review their configurations without having to wait on external reviewers. Again, reach out in #ask-experimenter for help with this.

End​

Once the survey has reached the end of the scheduled enrollment period, one of two things should happen. If there is sufficient volume of responses, the delivery should be ended (not just enrollment, the entire delivery should be ended). To do this, request to end and find a reviewer (see Launch above) to approve. If there aren't enough responses, the survey can be left open to gather more. It's recommended to extend the invitation period in units of whole weeks, to avoid biasing responses.

Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/monitoring/index.html b/monitoring/index.html index a5dd7f20b..5864e5097 100644 --- a/monitoring/index.html +++ b/monitoring/index.html @@ -4,13 +4,13 @@ Monitoring Your Experiment | Experimenter Docs - +

Monitoring Your Experiment

Once your experiment has launched, from your experimenter page you will now have a "Live Monitoring Dashboard" link in the gray sidebar (on the left of the page).

When to check here:

  1. 15 mins to 2 hours after launch, check that your experiment is enrolling users. If you have no users or gaining users much slower or quicker than you'd expect based on your expected number of clients - share the link and your concern in #ask-experimenter.
  2. Anytime during your experiment you can check that you don't have unexpected enrollment changes.
  3. Before you Close Enrollment, check that you have enrolled the expected number of clients.

What the different charts mean:

  1. Cummulative Population Estimate is the total number of people enrolled in your experiment. This is the chart experiment owners most frequently check to see if enrollment is on track for the expected number of clients. Experiments are sized for this cummulative enrollment number, not daily active numbers.
  2. Cumulative Population Estimate by Branch is only used to troubleshoot if enrollment is unhealthy. Example, if the branches are not enrolling evenly (the lines are far apart) it is a sign something could be wrong.
  3. Daily Active Population is just a point of data to know how many of your enrolled users are active every day.
  4. Daily Enrollments and other Enrollment charts provide data around new enrollments. This is used for troubleshooting or evaluating how much longer enrollment may be needed (if we are seeing low enrollment numbers).
  5. Enrollments Overall is similar to Enrollments Daily - except at an hourly level. This would be for troubleshooting to narrow down a time window where something unexpected happened (ex: large spike of enrollments or very low enrollments for that time of day).
  6. Unenrollment Overall and other Unenrollment charts provide data around unenrolled users. This is used for flagging when there may be an issue. We always expect some unenrollments.
Copyright © 2024 Mozilla Corporation
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Using Environment variables

adb and xcrun​

nimbus-cli assumes that adb and xcrun are on the PATH. If they are not (or aren't spelled adb or adb.exe), then you can configure them via the ADB_PATH and XCRUN_PATH environment variables.

export ADB_PATH="/Applications/Android Studio.app/platform-tools/bin/adb"
export XCRUN_PATH="$HOME/Library/CustomXcode/bin/xcrun"

Experimenter URLs​

Experimenter URLs are used for accessing experiments via the /api/v6 endpoints. They're hardcoded into nimbus-cli for both stage and production versions. However, just occassionally you may want to change these URLs.

e.g. for a local development version of experimenter with localhost:

export NIMBUS_API_URL=https://localhost:8080
export NIMBUS_API_STAGE_URL=https://localhost:3030

Remote Settings URLs​

URLs for Remote Settings are also hardcoded into nimbus-cli for both the stage and release versions. Like Experimenter, you may occassionally want to change these URLs.

e.g.

export NIMBUS_URL=https://no-cdn.remote-settings.mozilla.com
export NIMBUS_STAGE_URL=https://no-cdn.stage-remote-settings.mozilla.com

This has been helpful in the past when trying to debug a problem with CDN, or Remote Settings has moved.

Note: while the Remote Settings represents what the live apps see, there is approximately 1 hour latency between changing an experiment on Experimenter and it being synced to Remote Settings.

Manifest Cache Directory​

The manifest downloading happens on each invocation of the enroll, validate and test-feature commands.

You can speed this up by setting directory as the cache for downloading the manifests:

export NIMBUS_MANIFEST_CACHE=./caches/fml

However, it should be noted that for some files (especially downloading from main), they change even though the URL remains the same.

For this, you should consider putting into your .profile file something like

export NIMBUS_MANIFEST_CACHE=$HOME/.local/state/fml/$(date -Idate)

so as to only use the cache for one day.

Suppressing update checking​

The nimbus-cli periodically checks for a new version. This can be disabled by setting the following environment variable.

export NIMBUS_CLI_SUPPRESS_UPDATE_CHECK="true"

The server host and port​

By default, the server that is started with nimbus-cli start-server uses the machine's network interface to derive the external IP address.

Communicating with the server is done by other instances of nimbus-cli by posting to the /post endpoint. The port and host can be configured with the following environment variables.

export NIMBUS_CLI_SERVER_HOST=0.0.0.0
export NIMBUS_CLI_SERVER_PORT=9090
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/nimbus-cli/fml/index.html b/nimbus-cli/fml/index.html index 302fa2abb..3e43dffae 100644 --- a/nimbus-cli/fml/index.html +++ b/nimbus-cli/fml/index.html @@ -4,13 +4,13 @@ Working with Feature Manifests | Experimenter Docs - +

Working with Feature Manifests

The Feature Manifest Language is a powerful way of specify the shape of the feature configurations that each branch is made up of.

The nimbus-cli knows about the feature manifests for each of the apps, and can validate experiments before they launch.

validate​

nimbus-cli --app firefox_ios --channel release validate ios-sync-manager-integration

This downloads the feature manifest (the fml file), and checks each of the features in each of the branches.

✅ Loaded manifest from https://raw.githubusercontent.com/mozilla-mobile/firefox-ios/main/nimbus.fml.yaml
✅ control           rust-sync-manager-component
✅ rust-sync-manager rust-sync-manager-component

By default, this will download the version from main, however you can specify a version of the FML.

nimbus-cli --app firefox_ios --channel release validate preview/release-ios-re-engagement-notifications-ab-experiment-114 --version 114

Using the --version parameter, we can specify 114 which for firefox_ios, this currently translates to the release/v114 tag.

✅ Loaded manifest from https://raw.githubusercontent.com/mozilla-mobile/firefox-ios/release/v114/nimbus.fml.yaml
✅ control         messaging
✅ treatment-a     messaging
✅ treatment-b     messaging
✅ treatment-c     messaging

Using the --ref parameter, we can a tag, a different branch or a commit hash.

Alternatively, we can specify a local manifest file.

nimbus-cli --app firefox_ios --channel release validate preview/release-ios-re-engagement-notifications-ab-experiment-114 --manifest ./my-manifest.fml.yaml

defaults​

The defaults commands outputs the default configuration, for all features of the app configured by Nimbus.

An optional --feature parameter can be specified to only output the default configuration for that particular feature.

For example,

nimbus-cli --app firefox_ios --channel release defaults --feature homescreenFeature

currently outputs the following JSON:

{
  "jump-back-in-synced-tab": true,
  "pocket-sponsored-stories": false,
  "sections-enabled": {
    "jump-back-in": true,
    "pocket": true,
    "recent-explorations": true,
    "recently-saved": true,
    "top-sites": true
  },
  "sponsored-tiles": {
    "max-number-of-tiles": 2,
    "status": true
  }
}

features​

A manifest can be used to change the behavior of the features command.

If the --validate flag is set, then the branch feature configuration is merged with the defaults from the manifest.

For example, without --validate, the command:

nimbus-cli --app firefox_ios --channel developer features ios-search-bar-placement-v2-treatment-a-rollout --branch treatment-a

outputs the following JSON, extracted from the experiment:

{
  "awesome-bar": {
    "position": {
      "is-bottom": false,
    }
  }
}

Using --validate, the defaults from the feature manifest are also shown: this gives an accurate picture of the complete configuration that the app will use for this feature.

nimbus-cli --app firefox_ios --channel developer features ios-search-bar-placement-v2-treatment-a-rollout --branch treatment-a --validate

Gives the above output merged with the defaults from the manifest:

{
  "awesome-bar": {
    "position": {
      "is-bottom": false,
      "is-position-feature-enabled": true,
      "is-toolbar-cfr-on": false
    },
    "search-highlights": false,
    "use-page-content": false
  }
}

Other commands​

By default, the enroll and test-feature validates the experiments before sending them to the device.

This can be overridden with the --no-validate option.

enroll​

nimbus-cli --app firefox_ios --channel release enroll ios-sync-manager-integration --branch rust-sync-manager

The manifest itself is validated on load, so if there is a problem with that feature, you can skip validation with --no-validate.

nimbus-cli --app firefox_ios --channel developer enroll ios-set-to-default-in-onboarding-final --branch control --no-validate

Each of the --manifest, --version and --ref options work with enroll, test-feature, features and defaults.

Working with the nimbus-fml command line​

The Feature Manifest Language has its own command line interface. This can be used to validate manifest files, generate code, and rationalize manifests in to a single file.

For example:

nimbus-cli fml validate @mozilla/blurts-server/config/nimbus.yaml

The documentation there is better placed to explain how to use it, however there is one caveat: flags (i.e. modifiers that begin with --) are caught by the nimbus-cli command line, rather than passed to the nimbus-fml command line.

Thus, you should use -- before their first use. For example:

nimbus-cli fml -- --help

will display the nimbus-fml help message.

You can put the -- anywhere before the first modifier: the following two commands are equivalent.

nimbus-cli fml -- generate-experimenter --channel release @mozilla/blurts-server/config/nimbus.yaml blurts.experimenter.yaml

and

nimbus-cli fml generate-experimenter @mozilla/blurts-server/config/nimbus.yaml blurts.experimenter.yaml -- --channel release
Copyright © 2024 Mozilla Corporation
- + \ No newline at end of file diff --git a/nimbus-cli/getting-started/index.html b/nimbus-cli/getting-started/index.html index 1ec2d971b..7342abd12 100644 --- a/nimbus-cli/getting-started/index.html +++ b/nimbus-cli/getting-started/index.html @@ -4,13 +4,13 @@ Getting Started | Experimenter Docs - +

Getting Started

Most nimbus-cli commands need an --app and --channel parameter. These should correspond to the app as it is installed on the simulator or emulator.

App Name--app valueAvailable values for --channel
Firefox for Androidfenixdeveloper, nightly, beta, release
Firefox for iOSfirefox_iosdeveloper,beta, release
Focus for Androidfocus_androiddeveloper, nightly, beta, release
Focus for iOSfocus_iosdeveloper,beta, release

The app and channel are how they are known to Nimbus. The channel corresponds to the build type: e.g.

  • fenix developer is the Debug build variant of Firefox for Android.
  • firefox_ios developer is the Fennec build configuration of Firefox for iOS.

You can also specify a --device-id for working with multiple simulator/emulator/device at the same time.

list​

You can find experiments you want to use by using the list command.

nimbus-cli --app fenix --channel developer list

The above command gives a formatted table of experiments:

  • with the targeted channel,
  • the percentage of eligible users will be enrolled
  • the affected features
  • whether the recipe is an experiment or a rollout (R)
  • names of the branches

Here is an example:

Experiment slug                                                   | Channel |     % | Features                      |   | Branches
------------------------------------------------------------------+---------+-------+-------------------------------+---+----------------------
 viewpoint-rolling-week-4-expansion-android                       | release |   3 % | messaging                     |   | treatment
 mobile-default-browser-cta-copy-test                             | release |  90 % | messaging                     |   | control, treatment
 lifestyles-images-onboarding-experiment-v3                       | release | 100 % | juno-onboarding               |   | control, treatment-a
 android-research-surface-validation                              | release |  10 % | messaging                     |   | control, treatment
 release-android-onboarding-redesign                              | release | 100 % | juno-onboarding               |   | control, treatment-a
 fx-release-android-re-engagement-notifications-114-rollout       | release | 100 % | re-engagement-notification    | R | control
 notification-worker-validation-experiment                        | nightly | 100 % | messaging                     |   | control, treatment-a

You can see the preview collection, by adding a preview parameter.

By default, this lists what the apps can see: this is coming straight from Remote Settings.

If you want to see what is available via the API, then use --use-api. It should be noted that this is shows both live and completed experiments, so is likely fairly large, and slow. It will not show preview experiments.

nimbus-cli --app fenix --channel developer list preview

You can also use:

  • production or release, or nothing to specify the production server.
  • preview or production/preview to specify the preview collection
  • stage to specify the staging server
  • stage/preview to see the staging server and the preview collection of the staging server.
tip

More information about the experiment can be found with the info command.

You can also filter the experiment list by slug, features, channel, or activity status:

Here we do a substring match on features, looking for onboarding in any of the features, for experiments in the release channel.

nimbus-cli --app firefox_ios list --feature onboarding --channel release

Here we look for any experiments active (either enrolling or observing). We use the --use-api flag because the experiments may not be live anymore.

nimbus-cli --app firefox_ios list --use-api --active-on 2023-05-01

We can use today as a shortcut for the current date.

nimbus-cli --app firefox_ios list --enrolling-on today

enroll​

You can start the app enrolled in a branch of a given experiment.

nimbus-cli --app fenix --channel developer \
    enroll $EXPERIMENT_SLUG --branch $BRANCH

This will download the experiment from Experimenter, and then make changes to it to make it inevitable that the Nimbus SDK will enroll the experiment with that branch:

  • changes the channel of the experiment to that of the installed app
  • set the targeting "true"
  • the bucketing for the particular branch as 100%
  • set the enrollment paused flag to false.

Once these changes have been made, the app is launched, and the experiment is injected into the SDK.

nimbus-cli --app fenix --channel developer \
    enroll android-research-surface-validation --branch treatment

If you're downloading the experiment from preview, stage or stage/preview, you should prepend the server slug to the experiment slug:

nimbus-cli --app fenix --channel developer \
    enroll preview/mobile-default-browser-homepage-banner-copy-test --branch treatment-c

You can use the --preserve-targeting and --preserve-bucketing options to preserve the targeting and bucketing.

Enrolled experiments should persist across restarts of the app, and fetching of new experiments is disabled.

tip

Occassionally, you may want to get the experiment from Remote Settings rather than Experimenter. You can do this by adding the --use-rs flag.

Validating experiments​

By default, the experiment branches will be validated against the feature manifest. This can be configured with --version VERSION, --ref REF and --manifest FILE parameters, or disabled with the --no-validate parameter.

Testing interactions between experiments and rollouts​

In addition to an experiment slug and branch, zero or more experiment slugs can be specified.

These must be rollouts. This is to test the interactions between rollouts and experiments acting upon the same feature.

nimbus-cli --app fenix --channel developer enroll mobile-default-browser-cta-copy-test --branch treatment viewpoint-rolling-week-2-expansion-android

--preserve-targeting and --preserve-bucketing acts on these rollouts too.

Console output​

The corresponding adb commands and xcrun commands are shown in yellow. These can be useful for attaching to bug reports as part of the Steps To Reproduce (STR). The nimbus-cli command used will also be useful for STR.

reset-app​

The app can be put back into the just installed state. On Android, there is an adb command to do this, but not in iOS. For iOS:

  • the app container for the given simulator is deleted.
  • the app's group containers for the given simulator are all deleted.

For both the command is the same:

nimbus-cli --app fenix --channel developer reset-app
nimbus-cli --app firefox_ios --channel developer reset-app

unenroll​

From time to time you may need to test how the app responds to unenrolling from an experiment. You can force the unenrollment from all previously enrolled experiments with this command.

nimbus-cli --app fenix --channel developer \
    unenroll

info​

When you're starting from just the experiment slug, you can use the info command to get more information:

nimbus-cli --app fenix --channel developer info mobile-default-browser-cta-copy-test
Slug        mobile-default-browser-cta-copy-test
Name        Mobile Default Browser CTA Copy test
Description A copy test on the default browser message on homepage.
URL         https://experimenter.services.mozilla.com/nimbus/mobile-default-browser-cta-copy-test/summary
App         fenix
Channel     release
E/R         Experiment with 2 branches
Enrollment  2023-06-06 ➞ 2023-06-14 (paused)
Observing   2023-06-06, proposed ending after 29 days
Targeting   "(app_version|versionCompare('112.!') >= 0) && (language in ['en'])"
Bucketing    90 %
Branches    control, treatment
Features    messaging

Notice:

- `E/R` stands for Experiment or Rollout.
- the targeting string is a JEXL expression, generated by Experimenter, and is the test all clients must pass in order to be considered _eligible_ for this experiment.
- the bucketing is the percentage of eligible clients that will enroll in the experiment, across all branches.

You can also get this outputed to a file as JSON or YAML with the --output parameter.

nimbus-cli --app fenix --channel developer info mobile-default-browser-cta-copy-test --output info.json
Experiment Slugs

Experiment identifiers, or slugs, are used to identify experiments and rollouts throughout Nimbus and nimbus-cli.

You can copy the slug to the clipboard in Experimenter by pressing the icon next to the experiment slug at the top of the summary page:

Alternatively, you can use the link you get from Experimenter.

e.g.

nimbus-cli --app fenix --channel developer \
    enroll \
    https://experimenter.services.mozilla.com/nimbus/mobile-default-browser-cta-copy-test/summary \
    --branch treatment

or

nimbus-cli --app fenix --channel developer \
    info \
    https://experimenter.services.mozilla.com/nimbus/viewpoint-rolling-week-4-expansion-android
Copyright © 2024 Mozilla Corporation
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Introduction

The nimbus-cli is a command line tool for interacting with the Nimbus SDK embedded in Mozilla's mobile applications.

It aims to provide a uniform way of interacting with an app, no matter what the app or the platform.

tip

If you're looking for installation instructions, start here.

Goals​

The principle goals for the nimbus-cli is to make testing and developing experiments easier:

Additionally, it is useful to be developers to investigate problems with the apps and with the internal state of the Nimbus SDK:

Non-goals​

The tool works by using the existing tooling provided by platform vendors (for Android, adb; for iOS, simctl) to communicate with an installed app on a device, emulator or simulator.

No attempt is made to help installing or uninstalling of the apps on to devices.

There is limited support for working with iOS devices.

Copyright © 2024 Mozilla Corporation
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Installation Guide

The nimbus-cli is a command line tool to help develop and test experiments and features on an instrumented mobile app.

It runs on macOS, Linux and Windows. It works with adb to drive Android apps on Android emulators and devices, and with simctl on macOS to drive iOS apps on the simulator.

Prerequisites​

For working with Android:​

For working with iOS:​

  • Install Xcode.
  • Run xcode-select --install

Installing nimbus-cli on macOS or Linux​

Copy the following command, and paste it into a command line.

curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/mozilla/application-services/main/install-nimbus-cli.sh | bash

This should give you output something like this:

✅ Downloading https://firefox-ci-tc.services.mozilla.com/api/index/v1/task/project.application-services.v2.nimbus-cli.latest/artifacts/public/build/nimbus-cli-aarch64-apple-darwin.zip
✅ Preparing nimbus-cli for install
ℹ️  Using for the first time may trigger a malicious software warning. Fix with: https://support.apple.com/en-us/guide/mac-help/mchleab3a043/mac
✅ nimbus-cli installed into /Users/jhugman/.local/bin
✅ Cleaning up installation directory
✅ Success!

Depending on your setup, it may ask for a password just after the "Preparing" step.

Checking the installation​

Run the following command:

nimbus-cli --help

This should output something that starts with:

Mozilla Nimbus' command line tool for mobile apps

Usage: nimbus-cli [OPTIONS] --app <APP> --channel <CHANNEL> <COMMAND>

macOS and the malicious software warning​

The installation script tries to mitigate this, but on first run, macOS may give you a warning:

  • Click on "Show in Finder"
  • Right click on the nimbus-cli icon
  • Select "Open"
  • Confirm that you Trust this Developer.

Installing for Windows​

  1. Download the nimbus-cli zip file for Windows.
  2. Uncompress the file.
  3. Move the file x86_64-pc-windows-gnu/release/nimbus-cli.exe to a convenient location (e.g. C:\Platform-Tools).
  4. Search for system environment in the Windows Start search and select Edit the system environment variables from the results.
  5. Click Environment Variables toward the bottom of the window that opened.
  6. In the lower section of the next window, under System variables, look for the Path row. Select it and click Edit.
  7. Select the New button in the Edit environment variable window and add the path with the platform tools. In our example, that's C:\Platform-Tools.
  8. Exit all windows and save the changes you made by selecting OK.
  9. Restart the terminal, and test.

Manual install for macOS or Linux​

  1. Download the nimbus-cli zip file for Apple Silicon macOS, Intel macOS or x86_64 Linux.
  2. Uncompress the file.
  3. Move the file release/nimbus-cli to a convenient location, say $HOME/bin
  4. Add this location to your PATH, by running in a terminal:
echo 'export PATH=$PATH:$HOME/bin' >> "~/.${SHELL}rc"
  1. Restart the terminal, and test.

Building from source​

  1. Install the rust toolchain, using rustup.
  2. Clone the application-services repo.
git clone https://github.com/mozilla/application-services.git
  1. Change into the application-services directory.
cd application-services
  1. Build the nimbus-cli using cargo:
cargo build --manifest-path components/support/nimbus-cli/Cargo.toml --

The binary will be at target/release/nimbus-cli relative to the application-services directory.

  1. Alternatively, set an alias to build and run from source:
alias nimbus-cli="cargo run --manifest-path $(pwd)/components/support/nimbus-cli/Cargo.toml --"
Copyright © 2024 Mozilla Corporation
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Instrumentation Guide

The nimbus-cli is a tool to communicate with the Nimbus SDK that is embedded in an app.

Adding new applications​

Adding a new app to the cli is less straightforward, but not hard. You should start collecting the following information:

For all apps:

  • the app_name according to Experimenter.
  • the names of the channels for the app, according to Experimenter.
  • the Github repo for the app
  • the location of the app's FML file within the Github repo.
  • the mapping between version names and branch names.
  • the Remote Settings collection names for both production and preview.

For Android:

  • the package name, for each channel
  • the main activity name.
  • the deep link scheme, for each channel

For iOS:

  • the app identifier, for each channel
  • the deep link scheme, for each channel

Once you have collected these, you should either talk to the Nimbus Team, or open a PR against the project.

Copyright © 2024 Mozilla Corporation
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Working with Devices

nimbus-cli works best with iOS when working directly with the simulator, via xcrun simctl.

However, there are times when limitations in the simulator mean that it is not possible to use. These are:

  • working on iOS devices
  • opening deeplinks and enrolling in experiments.

For these important use-cases, the nimbus-cli and associated tooling supports experiment enrollment via URL.

You can see the URL used with the --pbcopy option for any command that sends experiments to the app; for example:

nimbus-cli --app firefox_ios --channel release enroll viewpoint-survey-2023-april-ios --branch treatment --pbcopy

gives this output:

✅ Loaded manifest from https://raw.githubusercontent.com/mozilla-mobile/firefox-ios/main/nimbus.fml.yaml
✅ treatment       messaging
$ # Enrolling in the 'treatment' branch of 'viewpoint-survey-2023-april-ios'
$ # Copied a deeplink URL (7609 characters) in to the clipboard

Note the size of the URL which has been copied to the clipboard.

Once on your clipboard, this must be sent to the device; users have reported success with third-party chat apps.

However, this is error prone, and a URL of that length may easily be truncated, corrupting the experiment that is encoded within it.

Instead, we can start a server embedded with nimbus-cli.

nimbus-cli start-server

gives the following output:

Copy the address http://192.168.1.199:8080/ into your mobile browser

Once you open the webpage at the specified URL in Safari or Chrome, any command used to send experiments to the app (apply-file, enroll, log-state, open, test-feature, unenroll) can have the --pbpaste option, which will encode the experiments as URLs, update the server, and then the webpage will open the URL on the device.

The webpage is one button: if you issued a command while the browser was in the background, then you can hit the button when you return.

If the webpage is in the foreground when the command is issued, then you should get a dialog asking you if you want to open the app.

note

The protocol to talk to the device is very limited; we can only work with what we can encode in a URL and send to the app.

Resetting the app, terminating the app, working with passthrough args, getting logs out of the app: none of this is possible on iOS devices from the command line.

Copyright © 2024 Mozilla Corporation
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  • --stdout=<path> Redirect the application's standard output to a file.
  • --stderr=<path> Redirect the application's standard error to a file.
  • --terminate-running-process Terminate any running copy of the application. Note: Log output is often directed to stderr, not stdout.
    • Copyright © 2024 Mozilla Corporation
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    What's New

    0.4.0​

    • --app and --channel parameters are now optional. If a command needs them and you haven't specified them, then it will error.
    • list and fetch-list now have filters. You can filter on slug, feature, is-rollout, active-on date, and enroll date.
    • --patch FILE is now available for any command which accepts an experiment.
    • start-server – a new command to start a web server, which devices can connect to. Firefox for iOS and Android support this from v117.
      • pbcopy adds to the clipboard a URL with experiments encoded in them.
      • pbpaste sends the URL to the server. In this way, we can open enroll apps on device into experiments with the CLI.
    • fml – a new command to interact with the lower level nimbus-fml command line.

    0.3.0​

    • Now using the Experimenter API to get experiments, instead of Remote Settings. This saves time waiting for the sync between Experimenter and RS.
    • Split fetch into a fetch for named recipes and fetch-list for getting a list; this changes the parameters for fetch.
    • Passthrough parameters for finer control of the app launch process. Thanks Ben, for the suggestion.
    • A new features command to get the feature configuration out of the experiment. You can also merge with the defaults from the manifest.
    • A new defaults command to get the defaults from the manifest.
    • A new info command to get information about the experiment. This can be good for investigations.
    • Experiment slugs can be derived from Experimenter URLs.
    • Where you can read and write JSON, you can now read and write YAML.
    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/nimbus-cli/working-with-features/index.html b/nimbus-cli/working-with-features/index.html index 26b5e2157..067d91f2e 100644 --- a/nimbus-cli/working-with-features/index.html +++ b/nimbus-cli/working-with-features/index.html @@ -4,13 +4,13 @@ Working with Features | Experimenter Docs - +

    Working with Features

    The nimbus-cli provides features to download experiments from Remote Settings. However, sometimes you wish to do some exploratory testing of a feature without going through the experimenter interface.

    test-feature​

    The test-feature command allows you to specify a feature-id and one or more JSON files that correspond to the feature configuration for that feature: i.e. to exercise a feature.

    e.g.

    nimbus-cli --app firefox_ios --channel developer test-feature messaging my-treatment.json my-control.json

    For the above nimbus-cli will generate an experiment which will enroll in to the given app and channel for the messaging, with two branches: my-treatment and my-control. It will enroll in the first of the branches, i.e. my-treatment.

    The other branches are available to enroll using the Experiments screen available through the app's UI.

    By default, the app's feature manifest will be downloaded and used to validate each feature configuration. This can be overridden with the --no-validate option.

    features​

    The features command accepts an experiment and branch, and outputs the feature configuration from that branch.

    nimbus-cli --app fenix --channel developer features release-android-onboarding-redesign --branch treatment-a

    will output the JSON as specified by the branch:

    {
      "enabled": true
    }

    You can specify an optional --output FILE to write it to disk.

    nimbus-cli --app fenix --channel developer features release-android-onboarding-redesign --branch treatment-a --output new-onboarding.json
    tip

    Wherever a JSON file is specified, you can use JSON or YAML, just by changing the file name.

    nimbus-cli --app fenix --channel developer features release-android-onboarding-redesign --branch treatment-a --output new-onboarding.yaml

    This can be useful to add the winning branch configuration to the feature defaults in the feature manifest in the app's codebase, to make the change permanent.

    By default, the features command outputs the experimental feature configurations for all features involved in the experiment. At the moment, this is always one.

    When more than one feature per branch is available, the output will be of the format:

    {
      "$feature1_id": {},
      "$feature2_id": {},
    }

    You can force a single feature experiment to use this format with the --multi flag:

    nimbus-cli --app fenix --channel developer features release-android-onboarding-redesign --branch treatment-a --multi

    gives this output:

    {
      "juno-onboarding": {
        "enabled": true
      }
    }

    Alternatively, if you only want one feature of many, you can specify the feature id with the --feature parameter.

    nimbus-cli --app fenix --channel developer features release-android-onboarding-redesign --branch treatment-a --feature juno-onboarding

    If you are writing automated tooling on top of the features command, it is strongly recommended you use one of these options to get consistent output.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/nimbus-cli/working-with-files/index.html b/nimbus-cli/working-with-files/index.html index acdbbc4f8..93e9b0d44 100644 --- a/nimbus-cli/working-with-files/index.html +++ b/nimbus-cli/working-with-files/index.html @@ -4,13 +4,13 @@ Working with Files | Experimenter Docs - +

    Working with Files

    fetch​

    You can fetch a set of one or experiment/rollout recipes:

    nimbus-cli --app fenix --channel developer fetch --output archived.json android-notification-surface-validation

    You can use stage/production to specify a Nimbus API instance.

    This command fetches the android-research-surface-validation experiment from production and crolloutmobile from stage, and puts it in archived.json.

    nimbus-cli --app fenix --channel developer fetch --output archived.json android-research-surface-validation stage/crolloutmobile

    This might be useful for attaching to bug reports.

    tip

    If --output is missing, the file is printed to the console.

    You can get a view of the experiment JSON from an experimenter URL like this:

    nimbus-cli --app fenix --channel developer fetch https://experimenter.services.mozilla.com/nimbus/release-android-onboarding-redesign

    This may be helpful when info isn't enough.

    nimbus-cli --app fenix --channel developer info https://experimenter.services.mozilla.com/nimbus/release-android-onboarding-redesign

    fetch-list​

    Alternatively, you fetch the entire contents of a server's experiments:

    nimbus-cli --app fenix --channel developer \
        fetch-list \
        --output archived.json

    By default, this uses Remote Settings: you can see the contents of that the apps are currently seeing.

    This is quick— it shows only live experiments, but changes in the experiments in Experimenter aren't reflected in Remote Settings for approximately an hour.

    You can get all experiments from the API— both live and completed— using the --use-api flag.

    list​

    The list command takes a --file parameter to make working with these files a little easier:

    nimbus-cli --app fenix --channel developer list --file archived.json
    tip

    Anywhere you specify an experiment, you can load it from a file with --file.

    For example: you may want fetch-list from a Remote Settings

    nimbus-cli --app fenix --channel developer fetch-list --output list-for-bugreport.json

    Later, you may investigate the file, and find that two experiments in particular are causing problems.

    nimbus-cli --app fenix --channel developer list --file list-for-bugreport.json

    You can isolate those experiments in a different file using the fetch command with an input --file and an output --output file parameters.

    nimbus-cli --app fenix --channel developer \
        fetch \
        mobile-default-browser-cta-copy-test fx-release-android-re-engagement-notifications-114-rollout \
        --file list-for-bugreport.json \
        --output isolated-list.json

    enroll​

    The enroll command takes --file parameter, so you can enroll from recipes stored locally:

    nimbus-cli --app fenix --channel developer \
        --file archived.json \
        enroll android-research-surface-validation --branch treatment

    apply-file​

    You can also upload the whole file straight to the SDK:

    nimbus-cli --app fenix --channel developer \
        apply-file archived.json
    info

    Many nimbus-cli commands reads (--file) and writes (--output) to JSON files. For all these commands, If the file extension is yaml or yml, then it will treat it as YAML.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/nimbus-cli/working-with-logs/index.html b/nimbus-cli/working-with-logs/index.html index 906de3e6c..6394e470c 100644 --- a/nimbus-cli/working-with-logs/index.html +++ b/nimbus-cli/working-with-logs/index.html @@ -4,7 +4,7 @@ Working with Logs | Experimenter Docs - + @@ -15,7 +15,7 @@ 2023-06-15 17:06:29.644 💙 INFO [sync] Profile - [RUST][nimbus::nimbus_client] Slug | Features | Branch 2023-06-15 17:06:29.645 💙 INFO [sync] Profile - [RUST][nimbus::nimbus_client] ios-search-bar-placement-v2-treatment-a-rollout | search | treatment-a 2023-06-15 17:06:29.645 💙 INFO [experiments] Experiments - Nimbus is ready!

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/nimbus-cli/working-with-patch-files/index.html b/nimbus-cli/working-with-patch-files/index.html index 169816dc1..05d03df80 100644 --- a/nimbus-cli/working-with-patch-files/index.html +++ b/nimbus-cli/working-with-patch-files/index.html @@ -4,13 +4,13 @@ Working with Patch Files | Experimenter Docs - +

    Working with Patch Files

    The nimbus-cli lets you source experiments from multiple places and then change them before inserting them into the app where they can be evaluated, and the app can naturally enroll into them.

    When experimenting on more complicated features, the configuration itself may need to be patched.

    --patch FILE​

    Any command that takes an experiment as an argument (enroll, features, fetch, info, validate) accepts a patch file.

    The file is of the format:

    {
        "$feature-id": { … configuration patch … }
    }

    for example:

    {
        "messaging": {
            "triggers": {
                "AFTER_THREE_LAUNCHES_THIS_WEEK": "true",
                "ALLOWED_TIPS_NOTIFICATIONS": "true",
                "INACTIVE_NEW_USER": "true",
                "I_AM_DEFAULT_BROWSER": "true",
                "I_AM_NOT_DEFAULT_BROWSER": "true",
                "MORE_THAN_24H_SINCE_INSTALLED_OR_UPDATED": "true",
                "NOT_INSTALLED_TODAY": "true",
                "NOT_LAUNCHED_YESTERDAY": "true",
                "USER_RECENTLY_INSTALLED": "true",
                "USER_RECENTLY_UPDATED": "true"
            }
        },

        "onboarding": {
            "conditions": {
                "I_AM_DEFAULT_BROWSER": "false",
            }
        }
    }

    You can generate a defaults file with the defaults command, or for individual features:

    nimbus-cli --app firefox_ios --channel release defaults --output defaults.json

    or using the --multi option with features:

    nimbus-cli --app firefox_ios --channel release features mobile-default-browser-cta-copy-test-ios --branch treatment-a --multi --output feature.json

    You can hand edit these files, or write jq scripts to change them:

    For example, switching the triggers for all messages to ["ALWAYS"] in a messaging experiment:

    jq '.messaging|{ messaging: { messages: .messages|map_values({ trigger: [\"ALWAYS\"]}) } }' feature.json > patch.json

    However you get the patch file: the patch file is applied to each feature in each branch.

    nimbus-cli --app firefox_ios --channel developer features mobile-default-browser-cta-copy-test-ios --branch treatment-a --patch patch.json --multi

    Notice how the additional triggers are added to the messaging feature. The onboarding feature from the patch file is ignored because it is not involved in the mobile-default-browser-cta-copy-test-ios experiment.

    {
      "messaging": {
        "actions": {},
        "messages": {
          "default-browser": {
            "action": "MAKE_DEFAULT_BROWSER_WITH_TUTORIAL",
            "button-label": "Open My Links With Firefox",
            "experiment": "{experiment}",
            "style": "FALLBACK",
            "surface": "new-tab-card",
            "text": "Default Browser/DefaultBrowserCard.Description",
            "title": "Default Browser/DefaultBrowserCard.Title",
            "trigger": [
              "I_AM_NOT_DEFAULT_BROWSER",
              "SUPPORTS_DEFAULT_BROWSER"
            ]
          }
        },
        "on-control": "show-next-message",
        "styles": {},
        "triggers": {
          "AFTER_THREE_LAUNCHES_THIS_WEEK": "true",
          "ALLOWED_TIPS_NOTIFICATIONS": "true",
          "INACTIVE_NEW_USER": "true",
          "I_AM_DEFAULT_BROWSER": "true",
          "I_AM_NOT_DEFAULT_BROWSER": "true",
          "MORE_THAN_24H_SINCE_INSTALLED_OR_UPDATED": "true",
          "NOT_INSTALLED_TODAY": "true",
          "NOT_LAUNCHED_YESTERDAY": "true",
          "USER_RECENTLY_INSTALLED": "true",
          "USER_RECENTLY_UPDATED": "true"
        }
      }
    }

    You can then enroll the patched experiment:

    nimbus-cli --app firefox_ios --channel developer enroll mobile-default-browser-cta-copy-test-ios --branch treatment-a --patch patch.json

    For fetch and enroll, which accept multiple experiment slugs, then the same patch file is applied before sending to disk or the device.

    nimbus-cli fetch viewpoint-ios-july-2023 mobile-default-browser-cta-copy-test-ios --patch patch.json --output archived-with-patch.json
    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/onboarding/onboarding-feature-android/index.html b/onboarding/onboarding-feature-android/index.html index a97ef46fc..4eca39ee9 100644 --- a/onboarding/onboarding-feature-android/index.html +++ b/onboarding/onboarding-feature-android/index.html @@ -4,14 +4,14 @@ Onboarding Feature for Android | Experimenter Docs - +

    Introduction

    The onboarding feature enables experimentation with the 'new user onboarding flow'. The onboarding flow is presented to the user on each new install and is made up of a series of full screen 'views', referred to as 'cards'. The purpose of the onboarding flow is to enable the user to configure a small number of app enhancing settings. Each card provides context for each setting and the ability to enable/skip.

    The onboarding feature enables staff — most likely experiment owners, product owners, user research and marketing teams to customize each card's:

    • title copy
    • body copy
    • imagery
    • button copy
    • sequencing

    About this document

    This document is a guide for staff who wish to configure the new user onboarding flow through the experimenter interface.

    It is also a living document:

    • the onboarding feature may be under active development
    • card types may be added
    • card attributes may be added

    Scene setting

    The onboarding feature is built on top of Nimbus, Mozilla's experimentation platform. Nimbus allows you to send bits of configuration to application features from Experimenter, the web-application staff use to launch and manage experiments and rollouts.

    Using Experimenter in the general case is documented elsewhere, so this document is specifically concerned with configuring the onboarding feature.

    References

    For the most up-to-date configurations, the main code base will always be the best place to check.

    danger

    The onboarding feature is a first run experiment

    Creating an experiment

    Only values that differ from the default card(s) needs providing by the Experimenter. E.g:

    Existing default card​

    {
       "cards":{
          "default-browser":{
             "card-type":"default-browser",
             "enabled":"true"
             "title":"juno_onboarding_default_browser_title_nimbus",
             "ordering":10,
             "body":"juno_onboarding_default_browser_description_nimbus",
             "link-text":"juno_onboarding_default_browser_description_link_text",
             "image-res":"ic_onboarding_welcome",
             "primary-button-label":"juno_onboarding_default_browser_positive_button",
             "secondary-button-label":"juno_onboarding_default_browser_negative_button"
          }
       }
    }

    Experimenter card config​

    {
       "cards":{
          "default-browser":{
             "ordering":15,
             "image-res":"onboarding_default_browser",
          }
       }
    }

    Output (the merged result of the existing card and experiment card)​

     "cards":{
          "default-browser":{
             "card-type":"default-browser",
             "enabled":"true"
             "title":"juno_onboarding_default_browser_title_nimbus",
             "ordering":15,
             "body":"juno_onboarding_default_browser_description_nimbus",
             "link-text":"juno_onboarding_default_browser_description_link_text",
             "image-res":"onboarding_default_browser",
             "primary-button-label":"juno_onboarding_default_browser_positive_button",
             "secondary-button-label":"juno_onboarding_default_browser_negative_button"
          }
       }

    Card definition

    AttributeTypeDescriptionNotes
    card-typeCard TypeThe type of the cardSee defined cards types
    enabledBooleanIf true, this card is shown to the user.True by default.
    titleFree text or Resource IDThe title text displayed to the user
    bodyFree text or Resource IDThe message text displayed to the userMay contain linkable text
    link-text (optional)Free text or Resource IDThe text to link from the ‘body’ textMust match the linkable text from the ‘body’ exactly e.g. body: This is a policy link, link-text: policy link
    image-resResource IDThe resource ID of the image to be displayed
    orderingIntegerUsed to sequence the cardsThe system used for ordering is counting in tens, e.g. 10, 20, 30…
    primary-button-labelFree text or Resource IDThe text to display on the primary button
    secondary-button-labelFree text or Resource IDThe text to display on the secondary button

    Card types​

    • default-browser
    • add-search-widget
    • sync-sign-in
    • notification-permission

    Default cards

    By default, the app is bundled with a collection of pre-defined cards which will be used if no other configuration is provided for the cards. See the appendix for an overview of the default cards or the code the most up-to-date configuration.

    Available resources

    String resources​

    All existing app strings are available to Experimenter. See the full list here. Free text may also be used instead of a string resource.

    ⚠️ Localization of Free Text is not currently supported.

    Image resources​

    Campaign - Default​

    Card typeResource ID
    default-browseric_onboarding_welcome
    add-search-widgetic_onboarding_search_widget
    sync-sign-inic_onboarding_sync
    notification-permissionic_notification_permission

    Appendix

    The add search widget is part of an ongoing experiment found here: Android Onboarding - search widget

    Default cards overview​

    Default browser card​

    AttributeValue
    card-typedefault-browser
    enabledtrue
    titlejuno_onboarding_default_browser_title_nimbus
    bodyjuno_onboarding_default_browser_description_nimbus
    link-text (optional)juno_onboarding_default_browser_description_link_text
    image-resic_onboarding_welcome
    ordering10
    primary-button-labeljuno_onboarding_default_browser_positive_button
    secondary-button-labeljuno_onboarding_default_browser_negative_button

    Add search widget​

    AttributeValue
    card-typeadd-search-widget
    enabledfalse
    titlejuno_onboarding_add_search_widget_title
    bodyjuno_onboarding_add_search_widget_description
    link-text (optional)null
    image-resic_onboarding_search_widget
    ordering15
    primary-button-labeljuno_onboarding_add_search_widget_positive_button
    secondary-button-labeljuno_onboarding_add_search_widget_negative_button

    Sync card​

    AttributeValue
    card-typesync-sign-in
    enabledtrue
    titlejuno_onboarding_sign_in_title
    bodyjuno_onboarding_sign_in_description
    link-text (optional)null
    image-resic_onboarding_sync
    ordering20
    primary-button-labeljuno_onboarding_sign_in_positive_button
    secondary-button-labeljuno_onboarding_sign_in_negative_button

    Notification card​

    AttributeValue
    card-typenotification-permission
    enabledtrue
    titlejuno_onboarding_enable_notifications_title_nimbus
    bodyjuno_onboarding_enable_notifications_description_nimbus
    link-text (optional)juno_onboarding_default_browser_description_link_text
    image-resic_notification_permission
    ordering30
    primary-button-labeljuno_onboarding_enable_notifications_positive_button
    secondary-button-labeljuno_onboarding_enable_notifications_negative_button
    secondary-button-labeljuno_onboarding_sign_in_negative_button
    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/onboarding/onboarding-feature-ios/index.html b/onboarding/onboarding-feature-ios/index.html index 282cab163..92dc39881 100644 --- a/onboarding/onboarding-feature-ios/index.html +++ b/onboarding/onboarding-feature-ios/index.html @@ -4,14 +4,14 @@ Onboarding Feature for iOS | Experimenter Docs - +

    Introduction

    The onboarding feature enables experimentating with the 'new user onboarding flow'. The onboarding flow is presented to the user on each new install, and is made up of a series of full screen 'views', referred to as 'cards'. The purpose of the onboarding flow is to enable the user to configure a small number of app enhancing settings. Each card provides context for each setting and the ability to take an appropratie action, or skip to the next card.

    The onboarding feature enables customize each card's:

    • title copy
    • body copy
    • imagery
    • button copy
    • button actions
    • number of buttons (one or two)
    • sequencing

    About this document

    This document is a guide for staff who wish to configure the new user onboarding flow through the experimenter interface.

    It is also a living document:

    • the onboarding feature may be under active development
    • card types may be added
    • card attributes may be added

    Scene setting

    The onboarding feature is built on top of Nimbus, Mozilla's experimentation platform. Nimbus allows you to send bits of configuration to application features from Experimenter, the web-application staff use to launch and manage experiments and rollouts.

    Using Experimenter in the general case is documented elsewhere, so this document is specifically concerned with configuring the onboarding feature.

    References

    For the most up-to-date configurations, the main code base will always be the best place to check.

    danger

    The onboarding feature is a first run experiment

    Creating an experiment

    Only values that differ from the card's default values need to be provided to Experimenter. E.g:

    Existing default card​

    {
       "cards":{
          "welcome": {
             "title": "Onboarding/Onboarding.Welcome.Title.v114",
             "body": "Onboarding/Onboarding.Welcome.Description.v114",
             "order": 10,
             "image": "welcome-globe",
             "link": {
                "title": "Onboarding/Onboarding.Welcome.Link.Action.v114",
                "url": "https://www.mozilla.org/privacy/firefox/",
             },
              "buttons": {
                "primary": {
                  "title": "Onboarding/Onboarding.Welcome.Action.v114",
                  "action": "next-card",
                },
              },
              "type": "fresh-install",
              "prerequisites": ["ALWAYS"]
          }
       }
    }

    Experimenter card config​

    {
       "cards":{
          "welcome": {
             "title": "A new title",
          }
       }
    }

    Output (the merged result of the existing card and experiment card)​

       "cards":{
          "welcome": {
             "title": "A new title",
             "body": "Onboarding/Onboarding.Welcome.Description.v114",
             "order": 10,
             "image": "welcome-globe",
             "link": {
                "title": "Onboarding/Onboarding.Welcome.Link.Action.v114",
                "url": "https://www.mozilla.org/privacy/firefox/",
             },
              "buttons": {
                "primary": {
                  "title": "Onboarding/Onboarding.Welcome.Action.v114",
                  "action": "next-card",
                },
              },
              "type": "fresh-install",
              "prerequisites": ["ALWAYS"]
          }
       }

    Feature Definition

    The onboarding feature is split into several values:

    • conditions
    • cards
    • dismissable

    Conditions​

    The conditions table is a set of triggers, like messaging, that can be used on cards to include or exclude them from appearing. The conditions list is a json of valid JEXL expressions. These expressions can be used in the prerequisites or the disqualifiers fields in cards. E.g.:

    "conditions": {
        "ALWAYS": "true",
        "NOT_INSTALLED_TODAY": "days_since_install > 0"
    }

    Card definition​

    AttributeTypeDescriptionDefault Value
    titleFree text or TextIDThe title text displayed to the user""
    bodyFree text or TextIDThe body text displayed to the user""
    orderIntUsed for sequencing cards10
    imageImageIDThe image to show for a particular cardwelcome-globe
    linkNimbusOnboardingLinkAn optional link. If available, it will appear on the card. If not, no link will be present.null
    buttonsNimbusOnboardingButtonsThe configuartion for buttons.A Skip button with a default action of going to the next card.
    prerequisitesList of JEXL keysThe list of JEXL keys guiding whether a card can show up for certain users. All prerequisites must be true for a card to show.[]
    disqualifiersList of JEXL keysThe list of JEXL keys guiding a card should not show up for certain users. If any disqualifiers are met, then the card will not show.[]
    typeTypeIDThe type of onboarding the user is seeing.fresh-install

    Default cards​

    By default, the app is bundled with a collection of pre-defined cards which will be used if no other configuration is provided for the cards. See the appendix for an overview of the default cards or the code the most up-to-date configuration.

    FieldTypeDescriptionDefault
    titleFree text or StringIDThe title of the link"Learn more about our privacy policy"
    urlStringThe url to which the link would take the user."https://www.mozilla.org/privacy/firefox/"

    NimbusOnboardingButtons​

    FieldTypeDescriptionDefault
    primaryNimbusOnboardingButtonThe primary button on the card, coloured blue.A Skip button.
    secondaryNimbusOnboardingButtonAn optional second button. If defined, it will be in grey, below the primary button.null

    NimbusOnboardingButton​

    FieldTypeDescriptionDefault
    titleFree text or StringIDThe title of the button"Skip"
    actionOnboardingActionsThe action the button will take.next-card
    OnboardingActions​
    ActionDescription
    next-cardWill take the user to the next card
    sync-sign-inWill take the user to the sync sign in flow
    request-notificationsWill request to allow notifications from the user
    set-default-browserWill send the user to settings to set Firefox as their default browser
    open-default-browser-popupWill open up a popup with instructions for setting Firefox as their default browser
    read-privacy-policyWill open a webview where the user can read the privacy policy

    Dismissable​

    This is a property for the whole onboarding, and controls whether there is an x at the top of the screen or not. This is a simple boolean value:

    {
        "dismissable": true
    }

    Available resources

    TextID​

    All existing app strings are available to Experimenter. See the full list here. Note, that only strings with a tableName and a key can be used. Free text may also be used instead of a string resource.

    ⚠️ Localization of Free Text is not currently supported.

    Image resources​

    Base Resources​

    Resource IDDescription
    welcome-globeAn image of a person hugging the firefox logo
    sync-devicesAn image of a variety of devices
    notificationsAn image of notifications

    Campaign - Challenge the Default​

    Resource IDDescription
    welcome-ctdThe welcome image for CTD campaign.
    sync-devices-ctdThe sync image for CTD campaign.
    notification-ctdThe notifications image for CTD campaign.

    Appendix

    Default cards overview​

    Default Welcome Card​

    AttributeValue
    typefresh-install
    titleOnboarding/Onboarding.Welcome.Title.v114
    bodyOnboarding/Onboarding.Welcome.Description.v114
    link
    - titleOnboarding/Onboarding.Welcome.Link.Action.v114
    - url"https://www.mozilla.org/privacy/firefox/"
    imagewelcome-globe
    ordering10
    buttons
    - primary
    -- titleOnboarding/Onboarding.Welcome.Action.v114
    -- actionnext-card
    prerequisitesALWAYS

    Default Sync card​

    AttributeValue
    typefresh-install
    titleOnboarding/Onboarding.Sync.Title.v114
    bodyOnboarding/Onboarding.Sync.Description.v114
    imagesync-devices
    ordering20
    buttons
    - primary
    -- titleOnboarding/Onboarding.Sync.SignIn.Action.v114
    -- actionsync-sign-in
    - secondary
    -- titleOnboarding/Onboarding.Sync.Skip.Action.v114
    -- actionnext-card
    prerequisitesALWAYS

    Default Notification card​

    AttributeValue
    typefresh-install
    titleOnboarding/Onboarding.Notification.Title.v114
    bodyOnboarding/Onboarding.Notification.Description.v114
    imagenotifications
    ordering30
    buttons
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    -- titleOnboarding/Onboarding.Notification.TurnOnNotifications.Action.v114
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    prerequisitesALWAYS
    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/overview/index.html b/overview/index.html index 74b971fde..5a04d4cc9 100644 --- a/overview/index.html +++ b/overview/index.html @@ -4,7 +4,7 @@ Experimentation for data scientists | Experimenter Docs - + @@ -26,7 +26,7 @@ "start": 3678, "total": 10000 },
  • The example JSON above shows that the most recent experiment used buckets 3678 to 7156 (= 3678 + 3478). If your new experiment needs less than 28.44% (= (10,000 - 7156)/100) of the clients, then you do not need to inflate the percentage to account for Nimbus being unaware of clients enrolled in previous experiments.
  • If your new experiment needs more than 28.44% of the clients, then you must inflate the percentage to account for 71.56% of the clients already being enrolled in experiments. For example, if your new experiment needs 30% of the clients, then you must input 41.92% (= 30% / 71.56%) into "Population %" in the Nimbus front-end.

    • Non-normal distributions​

    Most of our telemetry metrics are not normally distributed. A couple approaches that you may find helpful are:

    • powering a Mann-Whitney U-test. Gpower implements the Mann-Whitney U-test.
    • log-transforming the data and the expected difference and powering a t test in log space.

    Metrics and statistics​

    Two weeks after the beginning of the enrollment period, Jetstream will begin to produce auto-generated reports summarizing the results of the experiment. Here is one such report (internal link).

    To see which metrics are included by default to this auto-generated report as well as information on adding custom and default metrics, statistics and confidence, see the Jetstream documentation.

    If you want to perform some analysis by hand, Jetstream datasets are also available in BigQuery. Many telemetry datasets also include an experiments field, which when filtered on the experiment slug, can identify rows in the dataset enrolled in the experiment.

    For certain experiments, data scientists may need to construct confidence intervals for relative or percent differences. Example implementations can be found in this notebook.

    Watch out for​

    Here are other things to watch out for:

    • Are there other experiments taking place at the same time as yours? How might that affect the interpretation of your results, or limit your sample sizes?
    • Are there multiple implementation phases to this new feature? Does Product want to compare results from one phase to another?
    • ...
    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/previewing-experiments/index.html b/previewing-experiments/index.html index 3d1d5b0e7..8825535f3 100644 --- a/previewing-experiments/index.html +++ b/previewing-experiments/index.html @@ -4,14 +4,14 @@ Previewing Experiments | Experimenter Docs - +

    Previewing Experiments

    This guide will help you preview what each branch of an experiment will look like for users in the target application.
    Preview does NOT launch your experiment to external users. Preview enables you to self test your experiment and for QA to test.

    danger

    These steps only apply to Firefox Desktop Version 90+. You can find instructions for iOS here, Fenix instructions are coming soon!

    1. For experiments that are already live, go to the summary page. For experiments that have not yet launched, you will need the author to click "Launch to Preview" on the Review & Launch page.

    Launch to Preview Button

    1. Scroll down to the Preview Url section of the page. Select the branch you want and copy the about:studies URL.

    2. Open about:config in Firefox Desktop v90+ and set nimbus.debug to true. Paste the URL you copied above in your browser.

    3. You should be enrolled! To exit the experiment, go to about:studies and click "Remove"

    Earlier Desktop Firefox versions (<90)​

    To use preview with earlier versions of Firefox, you can use your Browser Toolbox devtools to run this code snippet.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/results-availability/index.html b/results-availability/index.html index 9751e5739..ab2f9e851 100644 --- a/results-availability/index.html +++ b/results-availability/index.html @@ -4,13 +4,13 @@ Results | Experimenter Docs - +

    Results

    Question​

    Is there a straightforward way to get the results from an experiment programmatically? Like a JSON object or something that I could convert into a dataframe?

    Answer​

    Yes. Results are available in bigquery and GCS, though what you see in Experimenter has been transformed a bit.

    Daily, weekly and overall results are in this bucket as JSON (you may need to request permission to access this data):

    Example query:

    from jetstream.bigquery_client import BigQueryClient
    PROJECT_ID = ‘moz-fx-data-experiments’
    DATASET_ID = ‘mozanalysis’
    client = BigQueryClient(PROJECT_ID, DATASET_ID)
    df = client.table_to_dataframe(‘statistics_<slug>_overall_1’)

    If you already have mozanalysis set up, here's another example query:

    slug = "device-migration-new-user-onboarding-pt-2".replace("-", "_")
    results_query = f"""
    SELECT *
      FROM `moz-fx-data-experiments.mozanalysis.statistics_{slug}_overall_1`
    """
    results_df = bq_context.run_query(results_query).to_dataframe()
    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/rollouts/rollouts-and-experiments/index.html b/rollouts/rollouts-and-experiments/index.html index 1e825d3b4..8f4f2026e 100644 --- a/rollouts/rollouts-and-experiments/index.html +++ b/rollouts/rollouts-and-experiments/index.html @@ -4,7 +4,7 @@ Rollouts and Experiment Interaction | Experimenter Docs - + @@ -12,7 +12,7 @@

    Rollouts and Experiment Interaction

    tip

    See the Rollout FAQ for general rollout information

    Question​

    Can a client be in a rollout and an experiment for the same feature at the same time?

    Answer​

    Yes, we have separate namespaces which means you can run rollouts and experiments simultaneously and each namespace does its accounting for users separately. You can run a 100% rollout and a 5% experiment and both will fill up, but everyone in the experiment is also in the rollout.

    Question​

    If there is a concurrent experiment and rollout on the same featureId, once the experiment ends, assuming the rollout is at 100%, a given client will immediately be put into the rollout. Correct?

    Answer​

    The short answer is “yes”, in most situations.

    The long answer is: while both are running, an experiment client with actually be enrolled in both and the treatment they’ll receive will be the combination of both. This enables clients in subsequent experiments to easily get rolled out winners of prior experiments.

    If both the experiment and the rollout are focused on the same fields of the treatment JSON, the experiment will take precedence and in this case the behavior will be as you state: effectively, the client immediately joins the rollout.

    If they’re focused on different fields of the JSON, the behavior the clients gets will go from “experiment+rollout” to “rollout only”.

    Question​

    I have a feature that is now on by default in v115, and I want to wait until it has saturated the user base before shutting off the rollout. Do we have a standard around how long we should keep a rollout going in a situation like this?

    Answer​

    We typically wait 1-3 releases for the upgrade tail to catch up. You'll probably have the vast majority, so it just depends on the ramifications if people have it enabled on 114 and then lose it.

    If it's not a big deal, turn off the rollout in 116 and most people will have it and the stragglers will get it when they upgrade. If it is a big deal - turn off in 117 or 118 and you'll have reached a few percent more with each release.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/rollouts/rollouts-bucketing-warning/index.html b/rollouts/rollouts-bucketing-warning/index.html index 76f38d3c1..e87f0bb44 100644 --- a/rollouts/rollouts-bucketing-warning/index.html +++ b/rollouts/rollouts-bucketing-warning/index.html @@ -4,13 +4,13 @@ Rollout bucketing warning | Experimenter Docs - +

    Rollout bucketing warning

    tip

    See the Rollout FAQ for general rollout information

    Question​

    What does this warning mean?

    Answer​

    If you are using the multiple-rollout approach, this warning can be safely ignored because you are explicitly creating more than one rollout in the same bucket.

    However, if you are using live editability for rollouts, this warning tells you that there is a rollout that is already live that has the same application, channel, feature, and advanced targeting as the rollout that you are creating. This means that there will be collision, and your rollout will not function as-is. Users are already enrolled in the live rollout with the same criteria, and they will not be able to enroll in your rollout.

    If you are encountering this and you would still like to proceed with your rollout, feel free to reach out to us in #ask-experimenter on Slack and we will be able to provide more guidance.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/rollouts/saturation/index.html b/rollouts/saturation/index.html index 047d445d8..ccb1baf6f 100644 --- a/rollouts/saturation/index.html +++ b/rollouts/saturation/index.html @@ -4,13 +4,13 @@ Rollout feature saturation | Experimenter Docs - +

    Rollout feature saturation

    tip

    See the Rollout FAQ for general rollout information

    Question​

    I have a feature that is now on by default in v115, and I want to wait until it has saturated the user base before shutting off the rollout. Do we have a standard around how long we should keep a rollout going in a situation like this?

    Answer​

    Yes, we typically wait 1-3 releases for the upgrade tail to catch up. You'll probably have the vast majority, so it just depends on the ramifications if people have it enabled on 114 and then lose it.

    If it's not a big deal, turn off the rollout in 116 and most people will have it and the stragglers will get it when they upgrade. If it is a big deal - turn off in 117 or 118 and you'll have reached a few percent more with each release.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/search-index-docs-default-current.json b/search-index-docs-default-current.json index 295282d42..7c35691da 100644 --- a/search-index-docs-default-current.json +++ b/search-index-docs-default-current.json @@ -1 +1 @@ -{"documents":[{"id":1,"pageTitle":"Welcome","sectionTitle":"Welcome","sectionRoute":"/","type":"docs"},{"id":2,"pageTitle":"Welcome","sectionTitle":"What is covered here?","sectionRoute":"/#what-is-covered-here","type":"docs"},{"id":3,"pageTitle":"Welcome","sectionTitle":"What other tools exist","sectionRoute":"/#what-other-tools-exist","type":"docs"},{"id":4,"pageTitle":"Welcome","sectionTitle":"About these docs","sectionRoute":"/#about-these-docs","type":"docs"},{"id":5,"pageTitle":"What's Newsletter","sectionTitle":"What's Newsletter","sectionRoute":"/2021-02","type":"docs"},{"id":6,"pageTitle":"What's Newsletter","sectionTitle":"February 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System architecture / components | Experimenter Docs - + @@ -16,7 +16,7 @@ The SDK talks to Remote Settings to get the experiment configurations and changes.

    Telemetry​

    The SDK also generates telemetry on the clients participation in the experiment (state of enrollment, branch, unenrolls).
    This and the client telemetry use the applications existing Glean or Legacy Telemetry pipeline.

    Analysis​

    Automated Analysis System (AKA Jetstream)​

    Jetstream runs nightly to automatically analyze the metrics for experiments.
    It runs some metrics for every experiment (varies by platform) and can run additional metrics if there are custom configuration files applied to the experiment.

    Custom Configuration (AKA Jetstream Configuration Files)​

    A data scientist can write a custom configuration file named after the slug of the experiment to add metrics to an experiment.

    Analysis library (MozAnalysis)​

    Only interfaced with through the data team - this is a repository of vetted metric formulas that can be applied when needed by Data Scientists.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/targeting/advanced-targeting/index.html b/targeting/advanced-targeting/index.html index 35e909133..3e80537fe 100644 --- a/targeting/advanced-targeting/index.html +++ b/targeting/advanced-targeting/index.html @@ -4,13 +4,13 @@ Advanced targeting | Experimenter Docs - +

    Advanced targeting

    Question​

    How can I add a new advanced targeting option for experiment audiences?

    Answer​

    To add new advanced targeting you can open a PR against the constants.py file in Experimenter. All that’s needed is to add a new NimbusTargetingConfig object, e.g.:

    NEW_ANDROID_13_USERS = NimbusTargetingConfig(
        name="New Android 13 Users",
        slug="new_android_13_users",
        description="Users who have Android 13 and are on their first run of the application",
        targeting="(android_sdk_version|versionCompare('33') >= 0) && is_first_run",
        desktop_telemetry="",
        sticky_required=True,
        is_first_run_required=True,
        application_choice_names=(Application.FENIX.name,),
    )

    If you need help writing the targeting expression, let us know in #ask-experimenter on Slack.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/targeting/version-targeting/index.html b/targeting/version-targeting/index.html index 4ead450bb..54b4df4d5 100644 --- a/targeting/version-targeting/index.html +++ b/targeting/version-targeting/index.html @@ -4,13 +4,13 @@ Targeting dot releases or specific betas | Experimenter Docs - +

    Targeting dot releases or specific betas

    Question​

    Can Nimbus experiments/rollouts target specific beta releases, i.e. can I express "roll this out to everyone on 104b6 and later"?

    Answer​

    Yes.

    If you are a mobile engineer, see steps here for how to add a version.

    Otherwise, file an issue here to add a specific version (of beta, nightly, or release) and then ping in #ask-experimenter on Slack.

    The version requested will get added as an option to the Minimum Version and Maximum Version drop down menues. It is a minor and quick change. By default Experimenter (Nimbus) only exposes the whole valued versions, until requested.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/telemetry/index.html b/telemetry/index.html index b08e79824..d4fc60623 100644 --- a/telemetry/index.html +++ b/telemetry/index.html @@ -4,7 +4,7 @@ Telemetry for Experiments | Experimenter Docs - + @@ -27,7 +27,7 @@ ping_info.experiments field of all Glean pings. The information that is recorded in this way includes the experiment slug, the branch slug, and the enrollment ID.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/testing-on-mobile/index.html b/testing-on-mobile/index.html index cfa2416b2..2b6690d35 100644 --- a/testing-on-mobile/index.html +++ b/testing-on-mobile/index.html @@ -4,13 +4,13 @@ Testing on mobile | Experimenter Docs - +

    Testing on mobile

    We have three parts to test:

    1. The experiment definition document, as defined in Experimenter and delivered by Remote Settings. Problems with this definition should be resolved by the experiment owner.
    2. The Nimbus system (Nimbus client side SDK and the Experimenter/Remote Settings/Glean)
    3. How the app responds to different branches/feature configurations. Problems with the app's response to different branches should be resolved by the app's engineering team.

    Testing the Nimbus system is out of the scope of this document.

    At this point, there are few if any tools for QA to use to test either of 1, or 3.

    info

    Much of this document can now be considered deprecated: the manual steps below are now done for you using the nimbus-cli tooling.

    Please refer to the documentation for nimbus-cli.

    However, using a local build, and by changing the NIMBUS_URL to the Remote Settings staging server at https://firefox.settings.services.allizom.org we can effectively vary the experiments definition document to test the app, and to replicate the experiment definition document used in production. This is documented here.

    Building Fenix locally is documented in the Fenix repository.

    Overview​

    We wish to get the app to ingest the experiment definition of our choice. Here is a sample experiment definition, which you can generate with the staging instance of Experimenter. 

    {
      "data": [
        {
          "slug": "nimbus-aa-validation-for-ios-v2",
          "appId": "org.mozilla.ios.FirefoxBeta",
          "appName": "firefox_ios",
          "channel": "beta",
          "endDate": null,
          "branches": [
            {
              "slug": "treatment",
              "ratio": 40,
              "feature": {
                "value": {},
                "enabled": true,
                "featureId": "the-feature-id"
              }
            },
            {
              "slug": "control",
              "ratio": 60,
              "feature": {
                "value": {},
                "enabled": true,
                "featureId": "the-feature-id"
              }
            }
          ],
          "outcomes": [],
          "arguments": {},
          "probeSets": [],
          "startDate": "2021-04-19T22:40:44.614622Z",
          "targeting": "true",
          "featureIds": [
            "the-feature-id"
          ],
          "application": "org.mozilla.ios.FirefoxBeta",
          "bucketConfig": {
            "count": 8000,
            "start": 0,
            "total": 10000,
            "namespace": "nimbus-aa-validation-for-ios-v2-1",
            "randomizationUnit": "nimbus_id"
          },
          "schemaVersion": "1.4.0",
          "userFacingName": "A replica of the experiment under test",
          "referenceBranch": "control",
          "proposedDuration": 28,
          "isEnrollmentPaused": false,
          "proposedEnrollment": 7,
          "userFacingDescription": "Is Nimbus working? This experiment tries to find out.",
          "id": "nimbus-aa-validation-for-ios-v2",
          "last_modified": 1619530368808
        }
      ]
    }

    The annotated guide to the experiment definition​

    Many of the fields in the above JSON correspond to the experiment UI.

    These fields affect which OS, app and build the experiment is for. These should align with the developer or nightly builds of the app your using, on the platform you're using. If these don't line up, the app will not pick up this experiment.

    • appName: pick fenix or firefox_ios
    • appId: The experimenter UI should help you select the right one.
    • channel: this must be nightly or developer. The experimenter UI should help you select the right one.
    • feature_id/featureIds: this is the identifier of the app feature under test. This should match what is hard coded into the App. Experimenter will put these in all the right places. If the feature id is not already listed, you can [add it here][experimenter-admin].

    These fields affect segment the population for eligibility for the experiment, and which branch they'll be given.

    • targeting: Experimenter will help you generate this JEXL query string. If Nimbus evaluates this on a given device to true, then the device is eligible for the experiment. If your testing the app, then you (TODO)
    • bucketConfig: start count and total. Of the total eligible users, the proportion that will actually be enrolled in the experiment is given by (count - start) / total. For testing purposes, you should make start = 0, count = 10000, total = 10000, i.e. enroll 100% of eligible devices.

    If the device is enrolled in the experiment (i.e. is targeted as eligible, and in the experiment bucket), then it will be enrolled in to one of the two or more branches. Once enrolled it will not change branches.

    • branch -> slug: This should match the branches that the app's feature responds to. In most cases, it will be treatment and control.
    • branch -> ratio: The ratio property of each branch, gives the proportion of the enrolled population will get a particular branch. Tip: make your ratios add up to 100. In the above example, the control branch gets 60 out of every (60 + 40) enrollments, i.e. 60%.

    Changing between experiments​

    To test the app's behaviour in the face of the branches, you'll need one experiment per branch. Each experiment needs the same feature id. They cannot be run in parallel.

    Experimenter​

    The experiment can be ended remotely.

    Client side​

    Once enrolled in an experiment, the user should not be able to enroll in a different branch.

    Once the experiment has been ended, you can reset the app by clearing app data and caches. The experiment is downloaded on first run after the reset or install, after a few second; and the experiment enrollment will happen on second run.

    Alternatives to Experimenter​

    It may not be desirable to use Experimenter: you should be able to set up a simple file server on your network. It should be able to serve the experiment JSON file directly, which you can edit the JSON directly.

    This local server should serve the file from the path: /buckets/main/collections/nimbus-mobile-experiments/records. Setting the NIMBUS_URL to this local server should be done in the same way as above.

    Experiment set-ups for testing branches​

    You should have one experiment per branch:

    • they should have targeting = "true", bucketConfig to have start = 0, total = 10000 and count = 10000.
    • For the branch under test, set the branch ratio to 100 and the others to 0.

    This will ensure that all clients that load the experiment will enroll in it and choose that exact branch.

    Testing the experiment document itself​

    The only thing you can test on small scale is the targeting string. You should make an experiment with the same targeting as the experiment brief. You should ensure every client enrolls in a branch which has visible changes (e.g. treatment).

    You can then vary the device (e.g. the locale or language), or by using a small range of phones of different sizes and operating system versions.

    Conclusion​

    Much of these will be ameliorated by testing tooling. We appreciate that the current situation is less than ideal.

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/validating-experiments/index.html b/validating-experiments/index.html index ccfacb9d6..f8101e2b1 100644 --- a/validating-experiments/index.html +++ b/validating-experiments/index.html @@ -4,14 +4,14 @@ Validating Experiments | Experimenter Docs - +

    Validating Experiments

    This section describes some common problems with experiments that you should look out for, how we recommend testing for them, and some potential causes to investigate if you do see that something is wrong.

    You should consider this a starting point, not a comprehensive list. Generally speaking, interesting results warrant a higher degree of scrutiny.

    Branch imbalance (Sample Ratio Mismatch)​

    We expect to see some variation between the observed v.s. expected ratios for enrollment in branches for experiments. However, too much imbalance might be indication that there might be a problem with the validity of the experiment configuration, implementation, or execution.

    As a first step, you should continuously monitor daily active population and enrollment by branch to see if anything is obviously wrong. We do this with standard Grafana monitoring dashboards generated for every experiment:

    Daily active population is 2.152m control, 2.150m treatment

    You will also see a "Sample Ratio Mismatch" health check, which is a chi-squared test of independence to determine whether the difference between the actual v.s. expected ratio of branches is statistically significant. Note that this can have some temporary fluctuation, but you see a sustained period of enrollment for which the p-value is less than 0.01, you should consider this cause for further investigation.

    SRM check screenshot

    You will see SRM checks for daily active clients, enrollments, and unenrollments. Note that it is always important for enrollments to be balanced, whereas noise in unenrollments is less concerning. Some potential causes of SRM if you do see a potential issue:

    • Low overall enrollment
    • High unenrollments in treatment due to implementation details of the experience being tested
    • Irregularities in deployment of experiments/updating in flight

    Low enrollments​

    Once your experiment has been enrolling for a day or so, you should check your monitoring dashboards to see the difference between the actual and expected rate of enrollment. If numbers are lower than expected, this can be due to:

    • An error in sizing calculations (your expected count was off)
    • The experiment conflicts with other experiments or roll-outs targeting the same feature
    • Misconfiguration in experimenter (e.g. you selected the wrong release version or channel)
    • A bug in server-side targeting expressions
    • A bug, timeout, or implementation error in client-side targeting attributes;
    • You launched the experiment during the first week of a new release, during which actual users are still updating to the latest version of Firefox

    High unenrollments​

    Users can be removed from experiments for a number of reasons, which shows up on your dashboard as "unenrollments". When unenrollments look suspiciously high, these are some possible causes to investigate:

    • A targeting condition that was true during enrollment is no longer satisfied (e.g. user switched regions)
    • A preference that is required for the experiment to be on was changed by the user
    • Infrastructure failures (e.g. signing, remote settings delivery is somehow compromised)
    • Users are opting out of the experiment at an unexpected rate

    Debugging unenrollments by reason​

    In order to see which "reasons" are responsible for unenrollment, you can take a look at this part of the dashboard: image

    Alternatively, you can run your own query for unenrollment events. For example, this will show you unenrollments by reason and branch:

    SELECT
        submission_date,
        mozfun.map.get_key(event_map_values, 'branch') as branch,
        mozfun.map.get_key(event_map_values, 'reason') as reason,
        COUNT(*) AS events
    FROM telemetry.events
    WHERE
        event_category = 'normandy'
        AND event_method = 'unenroll'
        AND event_string_value = 'YOUR_EXPERIMENT_SLUG'
        AND submission_date >= '2021-8-10'
    GROUP BY 1, 2, 3
    ORDER BY events DESC
    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/whats-news/2023-Q2/index.html b/whats-news/2023-Q2/index.html index 5c9513471..a384fccce 100644 --- a/whats-news/2023-Q2/index.html +++ b/whats-news/2023-Q2/index.html @@ -4,13 +4,13 @@ What's New in Nimbus - Q2 2023 | Experimenter Docs - +

    What's New in Nimbus - Q2 2023

    This is a non-exhaustive list of the Nimbus team's accomplishments in Q2, including some deep dives into a few of the more exciting features that have landed.

    Mobile​

    🤖 Android Messaging Surface Configurations

    Adds native notifications as a messaging surface that can be targeted by the mobile-messaging system for the Retention/Growth Push Campaigns, and includes the pre-permission prompt shown to users before asking for Notification permission. This covers work in Fenix as well as in the Nimbus SDK. (jhugman)

    🍏 Simplify Startup (iOS)

    The startup sequence currently lives across the app and Application Services, requiring coordination with multiple repos. This makes it very hard to test and difficult to reason about state close to startup performance. This work moves Nimbus Startup out of the iOS apps, making it possible to move it to the beginning of the app startup, and makes the initialization API simpler and more stable. (jhugman)

    🍏🤖 Improve Mobile experiment QA

    As we improve our feature set and support for mobile clients, testing the experiments created using these features will grow in complexity and length. Ensuring that these experiments can be tested in a reasonable time frame is key to allowing us to be able to get more experiments out. These improvements to the mobile clients will allow Softvision to test more experiments quicker, as well as lend a hand to future automation. (b4hand)

    🍏🤖 First-run Testing Tools

    The Nimbus CLI (see below) was created to help with first-run testing. First-run is a frequently used, high value surface that requires the experiment configuration to ship with the mobile build, so it is crucial to have support for these experiments to test before shipping. (jhugman)

    🍏🤖 Deprecate Feature Keys in v6 API

    To prepare for the switch from single feature to multi-feature, the V6 serializer needed to switch over from using the feature key in the branch schema to using the features key. Desktop had already switched to features, but mobile was still using singular feature. This work switches the V6 serializer to always use the features key. (brennie)

    🍏🤖 First-run Release Date

    Adds a Release Date field for mobile first run experiments to accurately calculate enrollment and durations to save multiple manual calculations. (erichards)

    🍏🤖 Supporting Mobile Teams

    Ongoing support for mobile engineers using Nimbus. (jhugman)

    🍏🤖 Core Active Behavioral Targeting

    Applications are now able to share information with Nimbus necessary for the categorization of users as “core-active” and experiments/messaging are able to target based on this categorization. (chumphreys)

    Desktop​

    💻 Desktop Localization

    Enables localized experiments on the Nimbus Desktop client. (brennie)

    Jetstream & Results Analysis​

    📈 Improve Results Links

    Experimenter links to Results page are now disabled for experiments that have no results, and the Results page redirects to the Summary and shows appropriate messages to the user when there are no results. (mikew)

    📈 Shared Schema for Jetstream Results

    Define and create a new shared schema for consumption by Experimenter and Jetstream, ingestion of analysis results, and export of analysis results from Jetstream. (mikew)

    Cirrus​

    ☁️ Cirrus API Core Behavior

    Implements the core behaviour of the Cirrus API such that it receives a request from the client and can respond with the features and feature values. This requires fetching active experiments from Remote Setting, using the Nimbus SDK to compute enrollment, and using the FML to evaluate feature configurations. (ykhurana)

    ☁️ Nimbus FML for Cirrus

    Creates FML client and Python bindings for Cirrus. (chumphreys)

    ☁️ Nimbus SDK for Cirrus

    Given the existing Nimbus SDK library, this work creates an extensible, shared Nimbus library in which the majority of the functions are static as opposed to the stateful functions within the existing library. (chumphreys)

    ☁️ Cirrus Documentation

    Document/create ADRs for requirements, implementations, and system design diagrams. (ykhurana)

    Experimenter​

    🙌 Outreachy contributions

    Tasks that were completed by Outreachy intern applicants to improve the Experimenter UI. (ykhurana)

    🙌 Outreachy History Tracker Project [in progress]

    This work is being completed by our Outreachy intern. This will introduce a new page which show the changelogs of a particualr experiment within the existing project. The primary goal is to introduce a new page that leverages Django, Tailwind CSS, and Alpine.js (new pattern!) to enhance the functionality and user experience of the application. (ykhurana, avi-88)

    🔧 Multifeature

    Enables users to to run experiments that target multiple features at once. We modified experiment creation pages to be able to select multiple features via a multiselect at the top of the page (before editing branches). Each feature value is validated against its respective schema as before. Modified client and API to support multiple features in each branch and ensure existing experiments that are already published continue to use the single feature branch type without impact. (brennie, jlockhart)

    🔧 Additional Rollouts Improvements

    Non-user facing work for rollouts, including improved backend logic to handle the Dirty state, integration tests, updated state diagrams and sequence diagrams. (erichards)

    Deeper Dives​

    The nimbus-cli​

    🔥 The nimbus-cli was conceived at the team's Q2 workweek. It is designed to help QA engineers test mobile first run experiments, but has become a tool useful for interacting, inspecting and testing all experiments running on the supported mobile apps, and any feature that those apps configure via Nimbus.

    Installation is:

    curl --proto '=https' --tlsv1.2 -sSf https://raw.githubusercontent.com/mozilla/application-services/main/install-nimbus-cli.sh | bash

    Further details are at https://experimenter.info/nimbus-cli.

    Computing the date for expected results​

    🔧 We are now computing the date that the Jetstream results will be available for a given experiment. This new value is based on enrollment end date (or proposed enrollment end date if enrollment is not yet ended).

    Release date for First Run experiments​

    Our approach for calculating dates and duration for experiments did not previously take the release trains into account. For example, a first run experiment targeting Firefox desktop v123 might be launched (status=Live) on 1/2/2023. However, v123 might not be released until 1/15/2023. The start_date for this experiment will therefore be 1/2/2023, but users will not start seeing it until 1/15 when the version is released.

    We added another date, "proposed release date" or "first run release date", for first-run mobile experiments which will represent the release date of the browser version that is being targeted by the user.

    This date gives us the ability to:

    • 📈 Correctly compute enrollment and proposed end dates based on when users will actually start getting the release version
    • ⏰ Send reminder emails to experiment owners at the right time so that they can end enrollment/end experiments

    This new field shows up on the Audience page of the experiment, and is also viewable on the Summary page and on the timeline:

    "First Run Release Date" field on the Audience page


    "Release Date" field on the Summary page timeline


    "Release Date" field in the Audience section of the Summary page

    Desktop Rollout re-enrollment and resizing​

    • 📈 You can now re-enroll in rollouts that have increased in size (Bug 1833248)
    • 💪 When force-enrolling you can add &apply_targeting to the URL to check the targeting instead of skipping it (Bug 1736587)
    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/whats-news/2023-Q4/index.html b/whats-news/2023-Q4/index.html index 69403855c..14cd845fd 100644 --- a/whats-news/2023-Q4/index.html +++ b/whats-news/2023-Q4/index.html @@ -4,13 +4,13 @@ What's New in Nimbus - Q4 2023 | Experimenter Docs - +

    What's New in Nimbus - Q4 2023

    Mobile​

    Coming soon...

    Desktop​

    Coming soon...

    Jetstream & Results Analysis​

    Coming soon...

    Cirrus​

    Coming soon...

    Experimenter​

    🙌 FML error detection

    Error detection using the Feature Manifest Language is now available in Experimenter! For all applications that utilize the FML (non-desktop apps), the "Branches" page now uses the FML to detect when a feature configuration is invalid for the application's feature manifest. This is especially useful for deeply nested feature configurations. (erichards) EXP-3873, EXP-2365

    The FML also detects errors based on the specified versions that have been ch sen. For example, if you are targeting Fenix version 120 and the feature configuration shows errors for that version, you will now get helpful error messages that tell you which version(s) of the feature manifest are throwing these errors. (erichards) EXP-3875

    Copyright © 2024 Mozilla Corporation
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    What's New in Nimbus - Q3 2023

    Mobile​

    🤖🍏☁️ Pre-built nimbus-cli

    We can now use pre-built nimbus-cli for FML transformations. This change installs the nimbus-cli as part of deployment and uses this to fetch and transform FML files from the Firefox Mobile, Focus, and Monitor apps. This means faster deploy times and we can remove the .experimenter.yaml files from the apps' repos and build scripts. (jhugman)

    🍏 Support for testing on iOS devices to nimbus-cli

    Previously, we had been restricted to tested on iOS simulators only, which meant a tester needed to have Xcode installed on a Mac, and certain OS integrations were not testable. This new capability opens up testing experiments and features to many more people, and on AppStore released apps and on real devices. The QA team have said that nimbus-cli has made "testing [on mobile] a cakewalk", and on device testing as "mind-blowing". (jhugman)

    Desktop​

    💻 Rich JSON editing

    The Branches page now has a new editor for entering branch value! The new editor supports auto-completion of most features on Desktop and Mobile, as well as syntax hilighting and JSON linting. It finds errors in your values live as you type them and highlight them for easy correction! (brennie)


    Jetstream & Results Analysis​

    📈 Automated population sizing

    Population sizing tool results are now available (for certain applications/locales/countries) in Experimenter during sizing configuration. (mwilliams)

    📈 Support for downsampling

    Previously, if a job failed due to memory issues, a Data Scientist would have to manually override the enrollments query to do downsampling. This is complex, time-consuming, and error-prone. Jetstream now has the sample_size (an integer 1-100 representing the sample rate) as a config option.

    Cirrus​

    ☁️ Glean integration for Cirrus

    In order to get enrollment telemetry for Cirrus, we hooked up the Glean Python SDK. (ykhurana)

    ☁️ Record enrollment metrics

    Add the ability to record enrollment metrics for Cirrus clients. (chumphreys)

    ☁️ Demo app

    A simple test app with the node backend and react frontend that integrates cirrus that we can use for local testing and integration. (ykhurana)

    ☁️ Configurations for integrating Monitor

    Adding new application options, configurations, and collections to allow Monitor to be hooked up with Cirrus. (ykhurana)

    Experimenter​

    🙌 Inclusion and exclusion targeting

    Experiments now have an extra dimension of targeting: participation in other experiments and rollouts! This allows experiment owners to easily launch follow-up studies to the same cohort of users. Additionally, exclusion can be used to prevent enrolling in one experiment if they're already in a conflicting experiment on a different feature. This capability enables experiment owners to launch more experiments -- and especially mobile messaging campaigns -- without worrying that users will have conflicting experiences. (brennie)

    🙌 New fields in Takeaways section

    Three new fields have been added to the Takeaways section for completed experiments: a checkbox for "QBR notable learnings", a checkbox for "Promising metric gains" (a.k.a. "Statistically Significant DaU Gain"), and a free-form text area for "Gain amount" that allows the user to dig deeper into what metrics changed and the significance. In addition to that, the two checkbox fields have been added as a filter on the homepage to allow easy access to impactful experiments. (erichards)


    🙌 Outreachy History Tracker Project

    This work was completed by our Outreachy intern. This introduces a new page which show the changelogs of a particualr experiment within the existing project. The link to this page is now available underneath "Summary" on the left sidebar of an experiment's detail view. (ykhurana, avi-88)

    Copyright © 2024 Mozilla Corporation
    - + \ No newline at end of file diff --git a/workflow/implementing/custom-audiences/index.html b/workflow/implementing/custom-audiences/index.html index b5b2fa2e1..44bde9016 100644 --- a/workflow/implementing/custom-audiences/index.html +++ b/workflow/implementing/custom-audiences/index.html @@ -4,13 +4,13 @@ Custom Audiences | Experimenter Docs - +

    Custom Audiences

    Custom audiences let you target specific groups of users beyond the regular targeting options. Custom audiences can be configured for any experiment through the experimenter UI:

    custom audience screenshot

    They are:

    • Optional - Don't use them unless you need to limit the experiment to specific group of users.
    • Specific to a particular application (Not all audiences can target all applications)
    • Evaluated on clients via an expression language (JEXL)

    How to add a new custom audience​

    Create a PR against targeting/constants.py that adds a new JEXL expression. There is a recorded session showing 2 examples of adding custom targeting to demoonstrate where to start.

    Request review from a console core team member. It should be available in the Experimenter UI a few minutes after the PR has merged.

    If you're an iOS or a Android developer check the following instructions for how to add new client-side targeting attributes:

    info

    New custom audiences should be tested by the core Nimbus team before they are used in production. This could involve any or all of the following:

    • Test the whole JEXL expression on each application/device it is intended to run on.
    • Ask QA to help verify it by creating a dummy experiment.
    • Launch an A/A in nightly to see if real users enroll correctly
    Copyright © 2024 Mozilla Corporation
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    Workflow

    Experimentation Workflow​

    Click here for 5 minute recorded overview of Experiment Workflow . Here's a direct link to view the full Workflow Miro board. Here's the link to the Stage environment for Nimbus console.

    Find an existing experiment​

    If the experiment was run with Nimbus, check out the directory of live and completed experiments. Normandy experiments can be found here.

    Here is the link to Nimbus Console - where new experiments are created, reviewed/launched, monitored, and eventually have the results. Here is the link to STAGE for Nimbus Console. This is where you can create experiments and learn how to interact with the tools without impacting production users.

    If the experiment is not yet live, you will probably be able to find a relevant Jira ticket on this Data Science Experimentation Collaboration Board

    Copyright © 2024 Mozilla Corporation
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