Performance ‐ Microservice Architecture
To ensure our system can grow efficiently and handle increased demand, we’ll be using a microservice architecture. This approach allows us to split the application into smaller, independent services, each responsible for a specific function. Microservices offer flexibility, allowing each part of the system to scale independently and improving the overall resilience and performance of the system. Here’s how we plan to make the system scalable:
Our microservice architecture allows each part of the system to scale independently. This means if one part, like user management or data processing, starts experiencing high demand, we can scale that specific service up without needing to increase resources for the entire system. This modular approach avoids overloading unrelated parts of the application and keeps resource usage efficient.
By separating services, we ensure that each microservice is focused and only requires resources for its specific function, which makes scaling up (or down) faster and more targeted.
Kubernetes offers built-in capabilities for automatic scaling through Horizontal Pod Autoscaling (HPA). HPA dynamically adjusts the number of instances (or "pods") of each microservice based on real-time demand, so we don't have to manage scaling manually.
If demand spikes, Kubernetes can create additional pods to handle the load, distributing traffic evenly to prevent any single instance from getting overwhelmed. When demand decreases, Kubernetes scales down resources, which helps reduce costs and conserve resources.
Kubernetes supports both horizontal scaling (adding more instances of a service) and vertical scaling (increasing the resources allocated to each instance). This flexibility allows us to choose the most efficient scaling approach for each service. For example, a service that handles large data processing tasks might benefit from vertical scaling, while a web-facing service with fluctuating traffic would benefit from horizontal scaling.
For efficient communication and data streaming between services, we use Apache Kafka as a message broker. Kafka enables asynchronous communication, so each service can send and receive messages without waiting, keeping services loosely coupled and scalable. Kafka handles high throughput, making it perfect for processing large volumes of data in real-time and ensuring fast data flow as the system grows.
Additionally, as our system grows, we can use a service mesh like Istio to handle complex networking between microservices. Service meshes add advanced traffic control and load balancing at the service level, ensuring that requests are routed to the optimal instances of each service. This combination of Kafka and service meshes improves scalability by ensuring efficient data flow and high availability across services.
Scalability also applies to data management. Our database setup can include sharding (splitting data across multiple databases) and replication (keeping copies of data across instances) to handle increasing data loads efficiently. This way, as data demands grow, we can add more database nodes to distribute the load while ensuring fast access and high availability for data-intensive operations.
We’ll set up continuous monitoring for real-time insights into system performance, resource usage, and traffic patterns. Tools like Prometheus and Grafana can help us observe trends and identify bottlenecks before they become critical. With predictive scaling, we can use historical data to anticipate future demand spikes and proactively scale up resources, ensuring smooth operation even during unexpected load increases.
Reactive native will be optimized for rendering performance, with hooks like useMemo and useCallback, ensuring that unnecessary re-renders are avoided. Lazy Loading will be implemented for screens and components improving initial load time. We will also use Redux to manage the application state in a way that prevents re-computation and selective state updates will minimize performance overhead. Axios will also be used to facilitate concurrent API request and caching , reducing the frequency of network calls and optimizing data transfer.
In summary, our scalability plan combines 🧩 microservices, ☁️ Kubernetes autoscaling, 🔗 Kafka and service meshes, 🗄️ database sharding and replication, 📊 monitoring, and 
frontend to ensure that our system grows efficiently. This approach means we can handle increased demand without impacting performance, costs, or user experience.