GoFr gives a Go microservice a useful observability baseline out of the box: structured logs, OpenTelemetry traces, Prometheus-compatible metrics, and health endpoints. A production-ready service still needs deliberate choices about where telemetry goes, how much is recorded, who can reach it, and how Kubernetes handles readiness and shutdown.
What GoFr instruments for you
GoFr is an opinionated Go framework that combines service plumbing rather than asking each team to assemble every piece. Its quick start centers on gofr.New(), route registration, and app.Run(); the framework documentation lists routing, structured logging, OpenTelemetry traces, Prometheus metrics, data-source clients, and graceful shutdown as integrated features. See the GoFr documentation for the current setup details.
That baseline is valuable because the signals can be correlated, but emitting telemetry is not the same as operating an observability system. You must still deploy or select collectors and backends, set retention and access controls, and build dashboards and alert rules that fit your service.
Start with the application
- Initialize a Go module with
go mod init, then add the framework withgo get gofr.dev. - Create the application with
gofr.New(), register a handler or route, and start it withapp.Run(). - Check the current Go prerequisite in GoFr’s quick-start documentation before building. The reviewed page lists Go 1.25 or later, and prerequisites can change.
- By default, the HTTP service listens on port 8000. Keep the application endpoint and telemetry endpoints distinct in deployment configuration.
Choose integration over assembly deliberately
GoFr’s Why GoFr? page frames the decision as a trade-off: a minimal router offers more component-level control, while the team must integrate logging, tracing, metrics, clients, and data sources itself. The documentation says, “Both approaches are valid; this page describes the situations where GoFr’s trade-off tends to fit.” Choose GoFr when a coherent integrated baseline reduces repeated setup; choose independently selected components when granular control or an existing platform standard matters more.
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Use logs, metrics, and traces for different questions
| Signal | Best operational question | GoFr behavior documented |
|---|---|---|
| Logs | What event occurred, and what contextual details were recorded? | Structured events can include correlation ID, status, request time, database activity, configuration reads, and missing-configuration events. |
| Metrics | Is the service behaving differently over time or across instances? | A Prometheus-compatible endpoint exposes runtime and service measurements, including HTTP response histograms and dependency activity. |
| Traces | Where did an individual request spend time as it crossed service boundaries? | OpenTelemetry traces cover requests and responses, with correlation propagated to downstream requests and supported Pub/Sub boundaries. |
Use the signals together: a metric can show a rise in slow responses, a trace can expose the slow segment of a particular request, and its correlation ID can help locate related log events. Logs alone do not provide a request-path latency breakdown, and traces do not replace aggregate metrics.
Configure logging without creating noise or risk
GoFr documents INFO as the default log level. Set LOG_LEVEL to one of DEBUG, INFO, NOTICE, WARN, ERROR, or FATAL. A structured operational event might record a request’s correlation ID, status, and duration so an operator can find events related to a request.
Use DEBUG for development or controlled troubleshooting, not as an unexamined production default: GoFr warns that debug logging can affect performance and security. Decide what fields are safe to record and avoid logging credentials, tokens, or unnecessary personal data; the framework’s logging description is not a complete data-redaction policy.
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Collect metrics through the metrics endpoint
GoFr documents a Prometheus-compatible /metrics endpoint on port 2121 by default. Its Kubernetes guide describes the output as OpenMetrics/Prometheus text format and shows exposing a named metrics service port for compatible collectors. The documented measures include:
- Go runtime and memory gauges.
- HTTP response histograms.
- SQL connection and query measures, plus Redis command timings.
- Pub/Sub operation counters, retry counts, and circuit-breaker state.
- GraphQL request counts, errors, and durations.
Set METRICS_PORT=0 to disable the metrics server, according to the GoFr observability guide. Disabling it also means a scraper cannot collect those endpoint metrics.
Scrape deliberately and control cardinality
Prometheus, Grafana Alloy, OpenTelemetry Collector, VictoriaMetrics, and Datadog Agent are named by GoFr as possible collector paths. GoFr does not ship configuration for those collectors: your platform team must configure scraping, authentication and network access where needed, storage, retention, dashboards, and alerts.
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Metric labels can multiply time-series volume. GoFr documents a default cardinality limit of 2,000 distinct label sets per instrument per collection cycle, inclusive of the overflow slot, and provides a configurable limit. Treat this as a guardrail, not permission to use unbounded labels: do not label metrics with request IDs, arbitrary user input, or other values that vary per request. Select stable dimensions that help compare service behavior.
Export distributed traces with OpenTelemetry
GoFr documents automatic OpenTelemetry traces for requests and responses. It generates an X-Correlation-ID, includes it in response headers, and propagates it to downstream requests. The documentation also describes propagating active trace context across supported Pub/Sub publish and subscribe boundaries.
The observability guide recommends OTLP and documents TRACE_EXPORTER, TRACER_URL, TRACER_RATIO, and optional TRACER_HEADERS settings. It describes Jaeger and GoFr Tracer options; its Zipkin exporter is marked deprecated in favor of OTLP. Confirm supported exporter names and configuration against the current GoFr observability guide when deploying, since framework configuration can evolve.
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Set sampling to match the service
TRACER_RATIO ranges from zero to one in GoFr’s documentation. A ratio of 0.1 is presented in its Kubernetes guide as a sensible production starting example, not a universal optimum. Validate sampling against traffic volume, retention and cost limits, and the traces needed to investigate incidents. A low ratio reduces volume but also reduces the chance of retaining a trace for a particular request; choose the policy with your operational needs in view.
Make Kubernetes probes reflect their actual purpose
GoFr’s Kubernetes guide maps two well-known paths to different probe semantics. Use liveness to identify a wedged process; use readiness to decide whether the instance should receive traffic. Dependency-sensitive checks belong in readiness when those dependencies are needed to serve requests.
| Path | Kubernetes use | What it should establish |
|---|---|---|
/.well-known/alive |
Liveness probe | The process is alive and not stuck such that it requires a restart. |
/.well-known/health |
Readiness probe | The instance is ready to receive traffic, including relevant dependency checks where configured. |
/metrics on the metrics port |
Collector scrape target | Compatible monitoring infrastructure can retrieve the service’s exported measurements. |
Do not make liveness depend on a database or other transient external service. If a dependency briefly fails, a liveness check coupled to that dependency can restart otherwise healthy pods, amplify load, and prolong recovery. Configure readiness to remove an instance from traffic when it cannot serve correctly.
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Configure Kubernetes deployment and shutdown
Separate ordinary configuration from secrets
GoFr’s Kubernetes pattern uses a ConfigMap for non-secret environment configuration and a Secret for credentials and API keys. Keep sensitive values out of ConfigMaps and application logs, and limit access to the Secret through the cluster’s normal permissions model.
Expose application and metrics ports intentionally
Make the HTTP service available to the intended callers and expose the named metrics port only to the collector path that needs it. Confirm the collector can reach the endpoint and that network policy and authentication choices match your environment; an application that exports metrics without a configured scraper does not create retained, queryable monitoring data.
Allow graceful termination to finish
GoFr supports graceful shutdown on termination. Ensure Kubernetes termination grace is long enough for in-flight requests and shutdown work to complete. The guide uses 45 seconds as a typical API example, not a universal setting: tailor it to request duration, shutdown behavior, warmup, and platform constraints.
The guide also shows optional horizontal pod autoscaling and replica settings. Treat these as deployment examples rather than sizing recommendations; establish replica counts and scaling thresholds from the service’s actual capacity and availability requirements.
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Plan the telemetry system beyond GoFr
Before choosing a collector or backend, compare the operational fit across your environment:
- Protocol compatibility: determine whether your platform collects OTLP traces and Prometheus/OpenMetrics metrics, or needs an intermediate collector.
- Signal coverage: verify which signals the backend can ingest and correlate, rather than assuming support for one means support for all.
- Operations: account for collector deployment, upgrades, credentials, tenancy, networking, and failure handling.
- Sampling and retention: match trace volume and storage periods to investigation needs and budget.
- Existing standards and portability: prefer a path consistent with organization-wide conventions, while considering the work involved in moving instrumentation or stored data later.
GoFr’s framework defaults do not remove these platform decisions. They give the service a common starting point; the collector and backend complete the path from emitted telemetry to searchable, retained, actionable information.
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