Tell us about the loads and interesting cases related to load optimization.

Load testing optimization is the process of adapting a service to high or peak loads to ensure stable operation and minimize delays.

Typical loads:

• High request frequency (QPS).
• Large data volume.
• Peak loads at certain hours.

Interesting cases and approaches:

1. Horizontal scaling — adding new service instances behind a load balancer.
2. Caching — using Redis or Memcached to reduce database requests.
3. Connection pool — limiting and reusing database connections.
4. Asynchronous processing — offloading heavy tasks to a queue (e.g., RabbitMQ, Kafka) for background processing.
5. Profiling and bottleneck elimination — analyzing CPU, memory, locks, optimizing algorithms.
6. Using efficient data structures and algorithms — for example, replacing linear search with hash tables.

Example: In one project, as load on the authorization service increased, we implemented token caching in Redis, reducing database requests by 70%, and added asynchronous logging, which decreased response delays.

In Go, profiling with pprof, optimizing goroutines and channels, minimizing locks, and using worker pools for parallel processing are common optimization techniques.