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how-to-deploy.md

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How to deploy a RAFT instance, Step by Step

The following guide should get you up and running with an instance of RAFT.

There are two main ways you can approach setting up RAFT.

  • Download all requirements to your workstation and then use the RAFT CLI in a command window.
  • Use the Azure Portal Cloud Shell. This requires no changes to your workstation.

Step 1: Install the RAFT Command Line Interface

Let's start out by getting the RAFT command line interface (CLI from now on) up and running. It functions just the same on Windows and Linux clients.

If you are using your workstation

  • Install Python if you don't have it installed already; RAFT requires at least version 3.6.
  • Install the Azure CLI if you haven't already; RAFT requires at least version 2.15.

If you are using the Cloud Shell

If you are going to use the Cloud Shell it is assumed that you have already acquired an Azure subscription from https://azure.com/free or you have an existing subscription.

Access the Cloud Shell from your browser by clicking on the Cloud Shell icon.

Or access it directly from your browser at https://shell.azure.com. When using the shell for the first time it will create a storage account. This is normal and is needed to persist data from one session to another.

In the cloud shell the path to Python version 3.6 /opt/az/bin/python3

Common install instructions

You will need the RAFT CLI files. You can do this in a number of ways:

  • Download the RAFT CLI from a specific release
    For example:
    wget https://github.com/microsoft/rest-api-fuzz-testing/releases/download/LATEST_VERSION_OF_CLI/raft-cli.zip where LATEST_VERSION_OF_CLI is the latest released version of CLI
    Then run unzip unzip cli.zip
  • Clone the repo
  • Copy the sources

Once you have the python CLI files, you will need to install a few dependencies using Python's pip package installer from the root of the RAFT CLI folder.

$ pip install -r .\requirements.txt

If you have not included the Python scripts in your PATH on Windows, then you will have to run this from your local Python folder (wherever it may be) and provide the full path to the requirements.txt file:

C:\Users\[user]\AppData\Local\Programs\Python\Python39\Scripts> pip.exe install -r d:\repo\raft\cli\requirements.txt

The RAFT CLI is now functional.

D:\REPO\raft\cli>py raft.py --help
usage: raft.py [-h] [--defaults-context-path DEFAULTS_CONTEXT_PATH] [--defaults-context-json DEFAULTS_CONTEXT_JSON] [--secret SECRET] [--skip-sp-deployment] {cli,service,job,webhook} ...

RAFT CLI

positional arguments:
  {cli,service,job,webhook}

optional arguments:
  -h, --help            show this help message and exit
  --defaults-context-path DEFAULTS_CONTEXT_PATH
                        Path to the defaults.json
  --defaults-context-json DEFAULTS_CONTEXT_JSON
                        JSON blob containing service configuration
  --secret SECRET
  --skip-sp-deployment

Step 2: Azure Subscription Prep

You will need an Azure subscription to host the RAFT service. If you don't already have access to an Azure subscription, please follow these instructions to sign up for one, or sign up for a free subscription at https://azure.com/free.

You must be an owner on the subscription to deploy RAFT, though once it's deployed you only need contributor rights to use it.

RAFT uses container instances to host running jobs; by default, Azure subscriptions are limited to 100 container instances. If your subscription is already using container instances, or you anticipate running more than 100 simultaneous jobs, you should reach out to Azure support and request they increase your quota of this object type.


Step 3: Choose Configuration Options

To deploy RAFT, you will need to settle on a few configuration options in advance.

Option Required? Description
subscription Yes The subscription ID (GUID) of the subscription to which to deploy an instance of the RAFT service
deploymentName Yes The name of your deployment; we will use this as the base name for all objects we create to instantiate the RAFT service
region Yes The region identifier that is closest to your location, or that's necessary for any compliance purposes
metricsOptIn* Yes Whether you want the service to send us anonymized usage data; we use this to improve the service and respond to errors and other problems proactively (Note: to change you choice, just update the field and redeploy) Default: true
isDevelop No Is this deployment for developing the RAFT service? Default: false
isPrivateRegistry No When developing for the RAFT service, indicates a private registry is used to find images. Default: false
useAppInsights Yes Create AppInsights resource as part of your deployment and use it to write all service logs. Default: true
registry Yes Registry which stores service images. Default: mcr.microsoft.com

*Telemetry

*By default, we collect anonymous usage data from your RAFT instance, which helps us understand how users use RAFT and the problems they experience, which in turn, helps us improve the quality of the offering over time. Specifically, We do not collect any data about the targets and results of tools you might run. The data fields we collect are defined in the src/Contracts/Telemetry.fs source file. To opt-out of sending this data to Microsoft, simply set the metricsOptIn field in the defaults.json file set to false. You may also manually opt out by clearing the value from the setting RAFT_METRICS_APP_INSIGHTS_KEY in the apiservice and the orchestrator function app. (Do not delete the setting; simply clear the value.)

RESTler telemetry

It is also easy to opt-out of sending anonymous usage telemetry metrics for RESTler. Under the cli/raft-tools/tools/RESTler folder edit the config.json file. Set the RESTLER_TELEMETRY_OPTOUT environment variable to "1".

"environmentVariables" : {
	"RESTLER_TELEMETRY_OPTOUT" : "1"	
}

If you make these changes after the very first time you deploy, you will need to re-run the command

    python raft.py service upload-tools

Step 4: Run the Deployment Script

The first time you execute the py raft.py service deploy command, you'll see the following.

D:\REPO\raft\cli>py raft.py service deploy

subscription - The Azure Subscription ID to which RAFT is deployed

deploymentName - RAFT deployment name
    deployment name requirements:
        - only letters or numbers
        - at most 20 characters long
        - no capital letters
        - no dashes

region - Region to deploy RAFT (e.g. westus2)
    See the documentation on container instance region availability at
    https://docs.microsoft.com/en-us/azure/container-instances/container-instances-region-availability
    to pick the optimal region for your deployment.
    All jobs will be deployed by default in the same
    region as your service deployment

metricsOptIn - allow Microsoft collect anonymized metrics from the deployment.

useAppInsights - deploy AppInsights and use it to write all service logs

registry - registry which stores service images.
    Default: mcr.microsoft.com

-------------------------
To apply any changes made to the defaults.json file,
please run 'raft.py service deploy'
-------------------------

Using a text editor of your choice, update the defaults.json file with the values you determined in Step 3, and then re-run:

D:\REPO\raft\cli>py raft.py service deploy

Most deployments complete in about 15 minutes.


Step 5: Validate your RAFT Instance

Once your deployment has completed, you can check to see if the service is running by executing the CLI command:

D:\REPO\raft\cli>py raft.py service info

This will return information about the service including the version of the service installed and the date and time the service was last started.

There are a number of samples in the sample folder. These are setup so that you can run the python scripts directly to exercise the service. Take some time to look at the log files and get familiar with the output.

Three tools are deployed by default: RESTler, ZAP and Dredd.

You can see how they are configured by looking at the configuration files under the cli/raft-tools/tools folder.

See an explanation of the config.json file in How to onboard a tool.