Project: Grafana (Observability and Analysis / Observability)
File: instrumentation.md

Guidance, conventions and best practices for instrumenting Grafana using logs, metrics and traces. Logs are files that record events, warnings and errors as they occur within a software environment. Most logs include contextual information, such as the time an event occurred and which user or endpoint was associated with it. Use the package to create a named structured logger. Example: ```go import ( "fmt" "github.com/grafana/grafana/pkg/infra/log" ) logger := log.New("my-logger") logger.Debug("Debug msg") logger.Info("Info msg") logger.Warning("Warning msg") logger.Error("Error msg", "error", fmt.Errorf("BOOM")) ``` Name the logger using lowercase characters, e.g. `log.New("my-logger")` using snake_case or kebab-case styling. Prefix the logger name with an area name when using different loggers across a feature or related packages, e.g. `log.New("plugin.loader")` and `log.New("plugin.client")`. Start the log message with a capital letter, e.g. `logger.Info("Hello world")` instead of `logger.Info("hello world")`. The log message should be an identifier for the log entry, avoid parameterization in favor of key-value pairs for additional data. Prefer using camelCase style when naming log keys, e.g. remoteAddr, to be consistent with Go identifiers. Use the key error when logging Go errors, e.g. `logger.Error("Something failed", "error", fmt.Errorf("BOOM"))`. If log messages or key/value pairs originates from user input they should be validated and sanitized. Be careful to not expose any sensitive information in log messages e.g. secrets, credentials etc. It's especially easy to do by mistake when including a struct as value. When to use which log level? Debug: Informational messages of high frequency and/or less-important messages during normal operations. Info: Informational messages of low frequency and/or important messages. Warning: Should in normal cases not be used/needed. If used should be actionable. Error: Error messages indicating some operation failed (with an error) and the program didn't have a way of handle the error. Use a contextual logger to include additional key/value pairs attached to `context.Context`, e.g. `traceID`, to allow correlating logs with traces and/or correlate logs with a common identifier. You must to get a traceID Example: ```go import ( "context" "fmt" "github.com/grafana/grafana/pkg/infra/log" ) var logger = log.New("my-logger") func doSomething(ctx context.Context) { ctxLogger := logger.FromContext(ctx) ctxLogger.Debug("Debug msg") ctxLogger.Info("Info msg") ctxLogger.Warning("Warning msg") ctxLogger.Error("Error msg", "error", fmt.Errorf("BOOM")) } ``` During development, it's convenient to enable certain log level, e.g. debug, for certain loggers to minimize the generated log output and make it easier to find things. See for information how to configure this. It's also possible to configure multiple loggers: ```ini [log] filters = rendering:debug \ ; alerting.notifier:debug \ oauth.generic_oauth:debug \ ; oauth.okta:debug \ ; tsdb.postgres:debug \ ; tsdb.mssql:debug \ ; provisioning.plugins:debug \ ; provisioning:debug \ ; provisioning.dashboard:debug \ ; provisioning.datasources:debug \ datasources:debug \ data-proxy-log:debug ``` Metrics are quantifiable measurements that reflect the health and performance of applications or

Consider using metrics to provide real-time insight into the state of resources. If you want to know how responsive your application is or identify anomalies that could be early signs of a performance issue, metrics are a key source of visibility. See for a list and description of the different metric types you can use and when to use them. There are many possible types of metrics that can be tracked. One popular method for defining metrics is the . Use the namespace grafana as that would prefix any defined metric names with `grafana`. This will make it clear for operators that any metric named `grafana*` belongs to Grafana. Use snakecase style when naming metrics, e.g. \_http_request_duration_seconds instead of httpRequestDurationSeconds. Use snakecase style when naming labels, e.g. \_status_code instead of statusCode. If metric type is a counter, name it with a `total` suffix, e.g. httprequeststotal_. If metric type is a histogram and you're measuring duration, name it with a `<unit>` suffix, e.g. httprequestdurationseconds. If metric type is a gauge, name it to denote it's a value that can increase and decrease , e.g. httprequestinflight_. Be careful with what label values you add/accept. Using/allowing too many label values could result in . If label values originates from user input they should be validated. Use `metricutil.SanitizeLabelName(<label value>`) from pkg/infra/metrics/metricutil package to sanitize label names. Very important to only allow a pre-defined set of labels to minimize the risk of high cardinality problems. Be careful to not expose any sensitive information in label values, e.g. secrets, credentials etc. If you want to guarantee the existence of metrics before any observations has happened there's a couple of helper methods available in the pkg/infra/metrics/metricutil package. Ensure you have Docker installed and running on your machine Start Prometheus ```bash make devenv sources=prometheus ``` Run Grafana, and create a Prometheus datasource if you do not have one yet. Set the server URL to `http://localhost:9090`, enable basic auth, and type in the same auth you have for local Grafana Use Grafana Explore or dashboards to query any exported Grafana metrics. You can also view them at http://localhost:3000/metrics A distributed trace is data that tracks an application request as it flows through the various parts of an application. The trace records how long it takes each application component to process the request and pass the result to the next component. Traces can also identify which parts of the application trigger an error. Grafana uses for distributed tracing. There's an interface `Tracer` in the pkg/infra/tracing package that implements the , which you can use to create traces and spans. To get a hold of a `Tracer` you would need to get it injected as dependency into your service, see for more

For more information, see https://opentelemetry.io/docs/instrumentation/go/manual/. Example: ```go import ( "fmt" "github.com/grafana/grafana/pkg/infra/tracing" "go.opentelemetry.io/otel/attribute" "go.opentelemetry.io/otel/trace" ) type MyService struct { tracer tracing.Tracer } func ProvideService(tracer tracing.Tracer) *MyService { return &MyService{ tracer: tracer, } } func (s *MyService) Hello(ctx context.Context, name string) (string, error) { ctx, span := s.tracer.Start(ctx, "MyService.Hello", trace.WithAttributes( attribute.String("my_attribute", "val"), )) // this make sure the span is marked as finished when this // method ends to allow the span to be flushed and sent to // storage backend. defer span.End() // Add some event to show Events usage span.AddEvent("checking name...") if name == "" { err := fmt.Errorf("name cannot be empty") // sets the spans status to Error to make the span tracking // a failed operation as an error span. span.SetStatus(codes.Error, "failed to check name") // record err as an exception span event for this span span.RecordError(err) return "", err } // Add some other event to show Events usage span.AddEvent("name checked") // Add attribute to show Attributes usage span.SetAttributes( attribute.String("my_service.name", name), attribute.Int64("myservice.someother", int64(1337)), ) return fmt.Sprintf("Hello %s", name), nil } ``` Span names should follow the . | Span Name | Guidance | | -- | -- | | get | Too general | | get_account/42 | Too specific | | getaccount | Good, and accountid=42 would make a nice Span attribute | | get_account/{accountId} | Also good (using the HTTP route) | Span attribute and span event attributes should follow the . Good attribute key examples: service.version http.status_code See for additional conventions regarding well-known protocols and operations. Be careful with what span names you add/accept. Using/allowing too many span names could result in high cardinality problems. If span names, attribute or event values originates from user input they should be validated and sanitized. It's very important to only allow a pre-defined set of span names to minimize the risk of high cardinality problems. Be careful to not expose any sensitive information in span names, attribute or event values, e.g. secrets, credentials etc. Consider using `attributes.<Type>("<key>", <value>)` in favor of `attributes.Key("<key>").<Type>(<value>)` since it requires less characters and thereby reads easier. Example: ```go attribute.String("datasource_name", proxy.ds.Name) // vs attribute.Key("datasource_name").String(proxy.ds.Name) attribute.Int64("org_id", proxy.ctx.SignedInUser.OrgID) // vs attribute.Key("org_id").Int64(proxy.ctx.SignedInUser.OrgID) ``` ```bash make devenv sources=jaeger ``` To enable tracing in Grafana, you must set the address in your config.ini file ```ini [tracing.opentelemetry.jaeger] address = http://localhost:14268/api/traces ``` You need provisioned gdev-jaeger and gdev-loki datasources, see for setup instructions. Open Grafana explore and select gdev-loki datasource and use the query `{filename="/var/log/grafana/grafana.log"} | logfmt`. You can then inspect any log message that includes a `traceID` and from there click on `gdev-jaeger` to split view and inspect the trace in question. You can open http://localhost:16686 to use the Jaeger UI for browsing and searching traces.