performance-optimization

To optimize Spring Boot hot paths, establish measurable performance baselines using async-profiler, JFR, and JMH, then validate improvements against SLO-driven criteria. This profiling-first workflow produces before/after metrics to ensure corrections actually resolve the identified bottlenecks.

N+1 queries, unbounded collections, and HikariCP connection pool sizing issues are common Spring Boot performance antipatterns. They are detected by profiling hot paths and applying correction techniques during the plan and review phases to prevent performance regressions.

You need JMH for microbenchmark measurements of specific code segments, while async-profiler and JFR capture broader CPU and memory profiling data. Use them together to establish baselines and validate SLIs for hot paths in Spring Boot applications.

Yes, diagnosing virtual-thread pinning is supported alongside GC behavior and caching analysis. The profiling toolchain identifies these JVM-level performance antipatterns, allowing you to apply targeted correction techniques and verify the results against your SLOs.

Validate SLOs and SLIs during Spring Boot refactoring by applying profiling, metrics, and antipattern checks to hot paths. This enforces SLO-driven criteria and produces before/after evidence, ensuring the refactor does not introduce performance regressions.

The best way to measure Micrometer metrics is integrating them with async-profiler and JFR data to establish baselines for hot paths. This SLO-driven approach validates that performance corrections meet service level objectives with before/after evidence.