How did you handle data streaming at 870 RPS in your last job?

To handle a data stream with a load of about 870 RPS (requests per second) at my last job, I used the following approaches:

• Asynchronous processing: Used non-blocking queues and thread pools to distribute the load and increase throughput.
• Load balancing: Deployed multiple instances of the service behind a load balancer (e.g., Nginx or HAProxy) for even request distribution.
• Caching: Implemented caching of frequently requested data (e.g., using Redis or built-in cache) to reduce database load.
• Query and algorithm optimization: Analyzed bottlenecks and optimized SQL queries, used indexes, minimized external service calls.
• Monitoring and alerting: Set up metrics and logs for timely detection of performance issues.

Example architecture of processing:

Client -> Load balancer -> Thread pools/Asynchronous handlers -> Cache/Database

In Java, for asynchronous processing, I used CompletableFuture, ExecutorService, and reactive libraries (e.g., Project Reactor or RxJava) for efficient thread and resource management.