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What are the ways to identify and diagnose performance issues in production?
sobes.tech AI
Answer from AI
- Metrics Monitoring:
- Built-in packages:
expvar(simple key-value metrics),runtime(GC statistics, goroutines, etc.). - Third-party libraries: Prometheus, Datadog, New Relic.
- Tracking RPS, request latency, errors, resource consumption (CPU, RAM).
- Built-in packages:
- Profiling:
- Using the
net/http/pprofpackage for HTTP services. - Using the
runtime/pprofpackage to generate CPU, memory, blocking, goroutine profiles. - Analyzing profiles with the
go tool pprofutility.
# Example of using go tool pprof go tool pprof http://localhost:6060/debug/pprof/heap # Heap profile- Profiling types: CPU, Heap, Goroutine, Blocking, Mutex, ThreadCreate, Trace.
- Using the
- Tracing:
- Using the
golang.org/x/net/tracepackage. - Using OpenTracing/OpenTelemetry for distributed tracing.
- Tracking request paths through the system, identifying bottlenecks in service interactions.
// Example of using trace import "golang.org/x/net/trace" func handleRequest(w http.ResponseWriter, r *http.Request) { tr := trace.New("my_service", "handleRequest") defer tr.Finish() // Logging events in the trace tr.LazyLog("Starting request processing", false) // ... request handling logic ... tr.LazyLog("Finished request processing", false) } - Using the
- Log analysis:
- Collecting and centralized storage of logs (ELK Stack, Loki/Grafana).
- Detecting anomalies, errors, slow requests in logs.
- Integrating logs with tracing to link events.
- Using specialized APM (Application Performance Monitoring) solutions:
- Dynatrace, AppDynamics, New Relic.
- Providing comprehensive tools for monitoring, profiling, and tracing.
- Comparison of monitoring, profiling, and tracing:
| Tool | Purpose | Level of detail | Overhead |
|---|---|---|---|
| Monitoring | Overall system health, trends | Low | Low |
| Profiling | Identifying hot spots (functions, allocations) | High | Medium |
| Tracing | Request path, component interactions | Medium | Medium/High |
- Conduct load testing before deploying to production.
- Using Go Dog (a plugin for
go test) for benchmarking at function/method level. - Collecting and analyzing memory dumps (Heap Dumps) to identify memory leaks or excessive consumption.