d02c5b387e | ||
---|---|---|
examples | ||
README.md | ||
entry.go | ||
formatter.go | ||
hooks.go | ||
json_formatter.go | ||
logger.go | ||
logrus.go | ||
logrus_test.go | ||
text_formatter.go |
README.md
Logrus
Logrus is a structured logger for Go (golang), completely API compatible with the standard library logger.
Nicely color-coded in development (when a TTY is attached, otherwise just plain text):
With log.Formatter = new(logrus.JSONFormatter)
, for easy parsing by logstash
or Splunk:
{"animal":"walrus","level":"info","msg":"A group of walrus emerges from the
ocean","size":"10","time":"2014-03-10 19:57:38.562264131 -0400 EDT"}
{"level":"warning","msg":"The group's number increased tremendously!",
"number":122,"omg":true,"time":"2014-03-10 19:57:38.562471297 -0400 EDT"}
{"animal":"walrus","level":"info","msg":"A giant walrus appears!",
"size":"10","time":"2014-03-10 19:57:38.562500591 -0400 EDT"}
{"animal":"walrus","level":"info","msg":"Tremendously sized cow enters the ocean.",
"size":"9","time":"2014-03-10 19:57:38.562527896 -0400 EDT"}
{"level":"fatal","msg":"The ice breaks!","number":100,"omg":true,
"time":"2014-03-10 19:57:38.562543128 -0400 EDT"}
Fields
Logrus encourages careful, structured logging. It encourages the use of logging
fields instead of long, unparseable error messages. For example, instead of:
log.Fatalf("Failed to send event %s to topic %s with key %d")
, you should log
the much more discoverable:
log = logrus.New()
log.WithFields(logrus.Fields{
"event": event,
"topic": topic,
"key": key
}).Fatal("Failed to send event")
We've found this API forces you to think about logging in a way that produces
much more useful logging messages. We've been in countless situations where just
a single added field to a log statement that was already there would've saved us
hours. The WithFields
call is optional.
In general, with Logrus using any of the printf
-family functions should be
seen as a hint you want to add a field, however, you can still use the
printf
-family functions with Logrus.
Hooks
You can add hooks for logging levels. For example to send errors to an exception
tracking service on Error
, Fatal
and Panic
or info to StatsD.
log = logrus.New()
log.Hooks.Add(new(AirbrakeHook))
type AirbrakeHook struct{}
// `Fire()` takes the entry that the hook is fired for. `entry.Data[]` contains
// the fields for the entry. See the Fields section of the README.
func (hook *AirbrakeHook) Fire(entry *logrus.Entry) error {
err := airbrake.Notify(entry.Data["error"].(error))
if err != nil {
log.WithFields(logrus.Fields{
"source": "airbrake",
"endpoint": airbrake.Endpoint,
}).Info("Failed to send error to Airbrake")
}
return nil
}
// `Levels()` returns a slice of `Levels` the hook is fired for.
func (hook *AirbrakeHook) Levels() []logrus.Level {
return []logrus.Level{
logrus.Error,
logrus.Fatal,
logrus.Panic,
}
}
Level logging
Logrus has six logging levels: Debug, Info, Warning, Error, Fatal and Panic.
log.Debug("Useful debugging information.")
log.Info("Something noteworthy happened!")
log.Warn("You should probably take a look at this.")
log.Error("Something failed but I'm not quitting.")
// Calls os.Exit(1) after logging
log.Fatal("Bye.")
// Calls panic() after logging
log.Panic("I'm bailing.")
You can set the logging level on a Logger
, then it will only log entries with
that severity or anything above it:
// Will log anything that is info or above (warn, error, fatal, panic). Default.
log.Level = logrus.Info
It may be useful to set log.Level = logrus.Debug
in a debug or verbose
environment if your application has that.
Entries
Besides the fields added with WithField
or WithFields
some fields are
automatically added to all logging events:
time
. The timestamp when the entry was created.msg
. The logging message passed to{Info,Warn,Error,Fatal,Panic}
after theAddFields
call. E.g.Failed to send event.
level
. The logging level. E.g.info
.
Environments
Logrus has no notion of environment.
If you wish for hooks and formatters to only be used in specific environments,
you should handle that yourself. For example, if your application has a global
variable Environment
, which is a string representation of the environment you
could do:
init() {
// do something here to set environment depending on an environment variable
// or command-line flag
log := logrus.New()
if Environment == "production" {
log.Formatter = new(logrus.JSONFormatter)
} else {
// The TextFormatter is default, you don't actually have to do this.
log.Formatter = new(logrus.TextFormatter)
}
}
This configuration is how logrus
was intended to be used, but JSON in
production is mostly only useful if you do log aggregation with tools like
Splunk or Logstash.
Formatters
The built logging formatters are:
logrus.TextFormatter
. Logs the event in colors if stdout is a tty, otherwise without colors.logrus.JSONFormatter
. Logs fields as JSON.
You can define your formatter by implementing the Formatter
interface,
requiring a Format
method. Format
takes an *Entry
. entry.Data
is a
Fields
type (map[string]interface{}
) with all your fields as well as the
default ones (see Entries section above):
type MyJSONFormatter struct {
}
log.Formatter = new(MyJSONFormatter)
func (f *JSONFormatter) Format(entry *Entry) ([]byte, error) {
serialized, err := json.Marshal(entry.Data)
if err != nil {
return nil, fmt.Errorf("Failed to marshal fields to JSON, %v", err)
}
return append(serialized, '\n'), nil
}
TODO
- Performance
- Default fields for an instance and inheritance
- Default available hooks (airbrake, statsd, dump cores)
- Revisit TextFormatter