Performance Monitoring and Optimization Practices for Backend Systems

After completing a reservation system and deploying it, the author observes that as user volume grows, hidden performance problems emerge, requiring more than just functional development to address.

Three independent monitoring dimensions are recommended: database slow‑query logs to capture long‑running SQL, interface performance monitoring to measure end‑to‑end service latency (including potential excessive internal calls), and message‑queue backlog monitoring to detect consumption bottlenecks.

To pinpoint issues, the author suggests inserting segment‑timing logs at strategic points in code (e.g., after 30, 60, 100 lines) and developing a sense of typical operation durations—tens of milliseconds for DB calls, nanoseconds to microseconds for memory access, and hundreds of milliseconds to seconds for external services.

Common optimization tactics include merging remote calls into batch operations, employing in‑memory caching for frequently accessed data, and applying multithreaded parallelism to transform serial I/O‑bound work into concurrent execution while handling synchronization carefully.

Database‑specific improvements focus on adding appropriate indexes, understanding and minimizing lock waiting, selecting suitable transaction isolation levels (e.g., switching from "repeatable read" to "read committed" when safe), and using intermediate result caching or batch calculations to avoid costly full‑table scans.

The article concludes by inviting readers to discuss these ideas, offering additional resources such as interview question collections and links to open‑source projects, and reminding that effective performance engineering requires continuous monitoring and systematic optimization.