Tell about your experience with distributed systems and the most heavily loaded service — what was the load there?

In one of the projects, I participated in developing a distributed data processing system for a financial company. The system consisted of several microservices interacting through a message queue (Kafka) and using a Cassandra database to store large volumes of data.

The most high-load service processed up to 50,000 requests per second with minimal latency. To ensure scalability, we used horizontal scaling, load balancers, and caching at the service level (Redis). Techniques such as idempotency and data consistency in a distributed environment were also applied.

Architecture example:

• Client requests go to the API Gateway
• Requests are distributed to multiple instances of the microservice
• Microservices exchange messages via Kafka
• Data is stored in Cassandra with replication
• Frequently requested data is cached in Redis

This approach allowed us to ensure fault tolerance and high performance under heavy load.