Production - Observability and Performance
Learn production essentials for Java services: structured logging, metrics, tracing, profiling, and performance tuning workflow.
#java #production #observability #performance
Why this step matters
If you cannot observe your system, you cannot operate it reliably. Production quality depends on both visibility and performance discipline.
Structured logs
Prefer structured logs over free text. Include stable fields like:
- timestamp
- level
- service name
- request id / trace id
- user or tenant id when relevant
This makes search and correlation much faster.
Metrics and tracing
Metrics answer: "what is happening?" Tracing answers: "where is time spent across services?"
Core metrics to expose:
- request rate
- latency percentiles (p50/p95/p99)
- error rate
- JVM memory/GC
Alerting mindset
Alert on symptoms that impact users, not on noisy low-level events. Define SLO-oriented thresholds when possible.
Profiling and tuning workflow
- measure baseline
- identify bottleneck
- change one thing
- measure again
Never optimize blindly.
Typical bottlenecks
- excessive DB round-trips
- blocking I/O under high load
- high object allocation and GC pressure
- hot code paths with inefficient algorithms
Common mistakes
- no correlation id in logs
- collecting metrics but not using dashboards/alerts
- tuning JVM flags without evidence
- optimizing micro-code before fixing architecture hotspots
Takeaway
- Observability is part of the product, not an optional add-on
- Use logs, metrics, and traces together
- Tune performance through measurement loops
- Focus first on user-impacting bottlenecks