How to add logging

This guide describes best practices for adding logging to Sourcegraph’s backend components.

The recommended logger at Sourcegraph is github.com/sourcegraph/log, which exports:

  1. A standardized, strongly-typed, and structured logging interface, log.Logger
    1. Production output from this logger (SRC_LOG_FORMAT=json) complies with the OpenTelemetry log data model (also see: Logging: OpenTelemetry)
    2. log.Logger has a variety of constructors for spawning new loggers with context, namely Scoped, WithTrace, and WithFields.
  2. An initialization function to be called in func main(), log.Init(), that must be called.
    1. Log level can be configured with SRC_LOG_LEVEL (also see: Logging: Log levels)
    2. Do not use this in an init() function - we want to explicitly avoid tying logger instances as a compile-time dependency.
  3. A getter to retrieve a log.Logger instance, log.Scoped, and (Logger).Scoped for creating sub-loggers.
  4. Testing utilities

Logging is also available via the all-in-one internal/observation package: How to add observability

Core concepts

  1. github.com/sourcegraph/log intentionally does not export directly usable (global) log functions. Users should hold their own references to Logger instances, so that fields can be attached and log output can be more useful with additional context, and pass Loggers to components as required.
    1. Do not call the package-level log.Scoped wherever you need to log - consider passing a Logger around explicitly, and creating sub-scoped loggers using (Logger).Scoped().
    2. Do not add Logger to context.Context.
    3. Do not create package-level logger instances.
  2. github.com/sourcegraph/log should export everything that is required for logging - do not directly import a third-party logging package such as zap, log15, or the standard log library.

Handling loggers

Before creating loggers, you must initialize github.com/sourcegraph/log package within your program’s main() function. Initialization includes metadata about your service, as well as log sinks to tee log entries to additional destinations.

For example, a typical initialization process looks like this:

import (
  "github.com/sourcegraph/log"
  
  "github.com/sourcegraph/sourcegraph/internal/env"
  "github.com/sourcegraph/sourcegraph/internal/version"
  "github.com/sourcegraph/sourcegraph/internal/hostname"
)

func main() {
  // If unintialized, calls to `log.Scoped` will return a no-op logger in production, or
  // panic in development. It returns a callback to flush the logger buffer, if any, that
  // you should make sure to call before application exit (namely via `defer`)
  //
  // Repeated calls to `log.Init` will panic. Make sure to call this exactly once in `main`!
  liblog := sglog.Init(sglog.Resource{
    Name:       env.MyName,
    Version:    version.Version(),
    InstanceID: hostname.Get(),
  }, log.NewSentrySink())
  defer liblog.Sync()

  // Now we register a hook to update log sinks.
  //
  // Note that the call to conf.Watch must be run in a goroutine, because the initial call
  // is blocking if a connection cannot be established.
  conf.Init()
  go conf.Watch(liblog.Update(conf.GetLogSinks))
}

Basic conventions

  • The logger parameter should either be immediately after ctx context.Context, or be the first parameter.
  • In some cases there might already be a log module imported. Use the alias sglog to refer to github.com/sourcegraph/log, for example import sglog "github.com/sourcegraph/log".
  • When testing, provide test loggers for improved output management.
  • For more conventions, see relevant subsections in this guide, such as top-level loggers, sub-loggers, and writing log messages.

Top-level loggers

Once initialized, you can use log.Scoped() to create some top-level loggers to propagate. From each logger, you can:

The first top-level scoped logger is typically "server", since most logging is related to server initialization and service-level logging - the name of the service itself is already logged as part of the Resource field provided during initialization.

Background jobs, etc. can have additional top-level loggers created that better describe each components.

Sub-loggers

When your service starts logging, obtain a log.Logger instance, attach some relevant context, and start propagating your logger for use. From a parent logger, you can create sub-loggers that have additional context attached to them using functions on the log.Logger instance that returns a new log.Logger instance:

  1. Scoped loggers
  2. Fields sub-loggers
  3. Traced sub-loggers

All the above mechanisms attach metadata to all log entries created by the sub-logger, and they do not modify the parent logger. Using sub-loggers allows you to easily trace, for example, the execution of an event or a particular execution type by looking for shared log fields.

Scoped loggers

Scopes are used to identify the component of a system a log message comes from, and generally should provide enough information for an uninitiated reader (such as a new teammate, or a Sourcegraph administrator) to get a rough idea the context in which a log message might have occurred.

There are several ways to create scoped loggers:

  • a top-level scoped logger, from the package-level log.Scoped() function, is used mostly in a main()-type function or in places where no logger has been propagated.
  • a scoped sub-logger, which can be created from an existing logger with (Logger).Scoped(). The sub-scope is appended onto the parent scope with a . separator.

In general:

  • From the caller, only add a scope if, as a caller, you care that the log output enough to want to differentiate it
    • For example, if you create multiple clients for a service, you will probably want to create a separate scoped logger for each
  • From the callee, add a scope if you will be logging output that should be meaningfully differentiated (e.g. inside a client, or inside a significant helper function)
  • Scope names should be CamelCase or camelCase, and the scope description should follow the same conventions as a log message.

Example:

func NewClient() *Client {
  return &Client{logger: log.Scoped("Client", "client for a certain thing")}
}

func (p *Client) Request(logger log.Logger) {
  requestLog := p.logger.Scoped("Request", "executes a request") // creates scope "Public.Process"

  requestLog.Info("starting tasks")
  // ... things ...

  requestLog.Debug("mid checkpoint")

  helperFunc(requestLog)

  // ... things ...
  requestLog.Info("finalizing some things!")
}

func helperFunc(logger log.Logger) {
  // This is a small helper function, so no need to create another scope
  logger.Info("I'm helping!")
}

Fields sub-loggers

In a particular scope, some fields might be repeatedly emitted - most commonly, you might want to associate a set of log entries to an ID of some sort (user ID, iteration ID, etc). In these scenarios you should create a sub-logger with prepended fields by using logger.With(...fields), and use it directly to maintain relevant context:

func (w *Worker) DoBigThing(ctx context.Context, id int) {
  doLog := w.logger.Scoped(ctx).
    With(log.Int("id", id))

  // subsequent entries will have trace and params attached
  doLog.Info("starting the big thing")
  // {
  //   "InstrumentationScope": "...",
  //   "Message": "starting the big thing",
  //   "Attributes": { "id": 1 },
  // }

  // pass the logger to maintain context across function boundaries
  doSubTask(doLog, /*... */)
}

Traced sub-loggers

Traced loggers are loggers with trace context (trace and span IDs) attached to them. These loggers can be created in several ways:

  1. internal/trace.Logger(ctx, logger) creates a sub-logger with trace context from context.Context
  2. (internal/observation.Operation).With creates a sub-logger with trace context and fields from the operation
  3. (Logger).WithTrace(...) creates a sub-logger with explicitly provided trace context

For example:

import (
  "github.com/sourcegraph/log"

  "github.com/sourcegraph/sourcegraph/internal/trace"
)

func (w *Worker) DoBigThing(ctx context.Context, id int) {
  // ...

  // start a trace in the context
  tr, ctx := trace.New(ctx, /* ... */)
  logger := trace.Logger(ctx, w.logger)
  doSubTask(logger, /*... */)
}

func doSubTask(logger log.Logger) {
  logger.Info("a message")
  // {
  //   "InstrumentationScope": "...",
  //   "TraceID": "...",
  //   "Message": "a message",
  // }
}

Writing log messages

The message in a log line should generally be in lowercase, and should generally not have ending punctuation.

log.Info("this is my lowercase log line")
log.Error("this is an error")

If writing log messages that, for example, indicate the results of a function, simply use the Go conventions for naming (i.e. just copy the function name).

If multiple log lines have similar components (such as a message prefix, or the same log fields) prefer to create a sub-logger instead. For example:

Log levels

Guidance on when to use each log level is available on the docstrings of each respective logging function on Logger:

Production usage

See observability: logs in the administration docs.

Automatic error Reporting with Sentry

If the optional sink log.NewSentrySink() is passed when initializing the logger, when an error is passed in a field to the logger with log.Error(err), it will be reported to Sentry automatically if and only if the log level is above or equal to Warn. The log message and all fields will be used to annotate the error report and the logger scope will be used as a tag, which being indexed by Sentry, enables to group reports.

For example, the Sentry search query is:unresolved scope:*codeintel* will surface all error reports coming from errors that were logged by loggers whose scope includes codeintel.

If multiple error fields are passed, an individual report will be created for each of them.

The Sentry project to which the reports are sent is configured through the log.sentry.backendDSN site-config entry.

Development usage

With SRC_DEVELOPMENT=true and SRC_LOG_FORMAT=condensed or SRC_LOG_FORMAT=console, loggers will generate a human-readable summary format like the following:

DEBUG TestLogger log/logger_test.go:22 a debug message
INFO TestLogger log/logger_test.go:26 hello world {"some": "field", "hello": "world"}
INFO TestLogger log/logger_test.go:29 goodbye {"TraceId": "1234abcde", "some": "field", "world": "hello"}
WARN TestLogger log/logger_test.go:30 another message {"TraceId": "1234abcde", "some": "field"}
ERROR TestLogger log/logger_test.go:32 object of fields {"TraceId": "1234abcde", "some": "field", "object": {"field1": "value", "field2": "value"}}

This format omits fields like OpenTelemetry Resource and renders certain field types in a more friendly manner. Levels are also coloured, and the caller link with filename:line should be clickable in iTerm and VS Code such that you can jump straight to the source of the log entry.

Additionally, in SRC_DEVELOPMENT=true using log.Scoped without calling log.Init will panic (in production, a no-op logger will be returned).

Testing usage

For testing purposes, we also provide:

  1. A getter to retrieve a log.Logger instance and a callback to programmatically iterate log output, logtest.Scoped
    1. The standard log.Scoped will also work after logtest.Init
    2. Programatically iterable logs are available from the logtest.Captured logger instance
    3. Can be provided in code that accepts injected loggers
  2. An optional initialization function to be called in func TestMain(*testing.M), logtest.Init.
    1. Useful for testing code that does not accept injected loggers

Injected loggers

If the code you are testing accepts Logger instances as a parameter, you can skip the above and simply use logtest.Scoped to instantiate a Logger to provide. You can also use logtest.Captured, which also provides a callback for exporting logs, though be wary of making overly strict assertions on log entries to avoid brittle tests:

import (
  "testing"

  "github.com/sourcegraph/log/logtest"
)

func TestFooBar(t *testing.T) {
  logger, exportLogs := logtest.Captured(t)

  t.Run("test my thing", func(t *testing.T) {
    fooBar(logger)
  })

  // export log entries that were written during this time
  logs := exportLogs()
}

When writing a test, ensure that logtest.Scope in the tightest scope possible. This ensures that if a test fails, that the logging is closely tied to the test that failed. Especially if you testcase has sub tests with t.Run, prefer to created the test logger inside t.Run.

Alternatively, logtest.NoOp() creates a logger that can be used to silence output. Levels can also be adjusted using (Logger).IncreaseLevel.

Instantiated loggers

In the absense of log.Init in main(), testing code that instantiates loggers with log.Scoped (as opposed to (Logger).Scoped), github.com/sourcegraph/log can be initialized using libtest in packages that use log.Scoped:

import (
  "os"
  "testing"

  "github.com/sourcegraph/log/logtest"
)

func TestMain(m *testing.M) {
  logtest.Init(m)
  os.Exit(m.Run())
}

You can control the log level used during initialization with logtest.InitWithLevel.