Of course, most applications won't notice the impact of a slow logger: they already take tens or hundreds of milliseconds for each operation, so an extra millisecond doesn't matter.
On the other hand, why not make structured logging fast? The SugaredLogger
isn't any harder to use than other logging packages, and the Logger
makes
structured logging possible in performance-sensitive contexts. Across a fleet
of Go microservices, making each application even slightly more efficient adds
up quickly.
Unlike the familiar io.Writer
and http.Handler
, Logger
and
SugaredLogger
interfaces would include many methods. As Rob Pike points
out, "The bigger the interface, the weaker the abstraction."
Interfaces are also rigid — any change requires releasing a new major
version, since it breaks all third-party implementations.
Making the Logger
and SugaredLogger
concrete types doesn't sacrifice much
abstraction, and it lets us add methods without introducing breaking changes.
Your applications should define and depend upon an interface that includes
just the methods you use.
Applications often experience runs of errors, either because of a bug or because of a misbehaving user. Logging errors is usually a good idea, but it can easily make this bad situation worse: not only is your application coping with a flood of errors, it's also spending extra CPU cycles and I/O logging those errors. Since writes are typically serialized, logging limits throughput when you need it most.
Sampling fixes this problem by dropping repetitive log entries. Under normal conditions, your application writes out every entry. When similar entries are logged hundreds or thousands of times each second, though, zap begins dropping duplicates to preserve throughput.
Subjectively, we find it helpful to accompany structured context with a brief description. This isn't critical during development, but it makes debugging and operating unfamiliar systems much easier.
More concretely, zap's sampling algorithm uses the message to identify duplicate entries. In our experience, this is a practical middle ground between random sampling (which often drops the exact entry that you need while debugging) and hashing the complete entry (which is prohibitively expensive).
Since so many other logging packages include a global logger, many applications aren't designed to accept loggers as explicit parameters. Changing function signatures is often a breaking change, so zap includes global loggers to simplify migration.
Avoid them where possible.
In general, application code should handle errors gracefully instead of using
panic
or os.Exit
. However, every rule has exceptions, and it's common to
crash when an error is truly unrecoverable. To avoid losing any information
— especially the reason for the crash — the logger must flush any
buffered entries before the process exits.
Zap makes this easy by offering Panic
and Fatal
logging methods that
automatically flush before exiting. Of course, this doesn't guarantee that
logs will never be lost, but it eliminates a common error.
See the discussion in uber-go#207 for more details.
DPanic
stands for "panic in development." In development, it logs at
PanicLevel
; otherwise, it logs at ErrorLevel
. DPanic
makes it easier to
catch errors that are theoretically possible, but shouldn't actually happen,
without crashing in production.
If you've ever written code like this, you need DPanic
:
if err != nil {
panic(fmt.Sprintf("shouldn't ever get here: %v", err))
}
Either zap was installed incorrectly or you're referencing the wrong package name in your code.
Zap's source code happens to be hosted on GitHub, but the import
path is go.uber.org/zap
. This gives us, the project
maintainers, the freedom to move the source code if necessary. However, it
means that you need to take a little care when installing and using the
package.
If you follow two simple rules, everything should work: install zap with go get -u go.uber.org/zap
, and always import it in your code with import "go.uber.org/zap"
. Your code shouldn't contain any references to
github.com/uber-go/zap
.
Zap doesn't natively support rotating log files, since we prefer to leave this
to an external program like logrotate
.
However, it's easy to integrate a log rotation package like
gopkg.in/natefinch/lumberjack.v2
as a zapcore.WriteSyncer
.
// lumberjack.Logger is already safe for concurrent use, so we don't need to
// lock it.
w := zapcore.AddSync(&lumberjack.Logger{
Filename: "/var/log/myapp/foo.log",
MaxSize: 500, // megabytes
MaxBackups: 3,
MaxAge: 28, // days
})
core := zapcore.NewCore(
zapcore.NewJSONEncoder(zap.NewProductionEncoderConfig()),
w,
zap.InfoLevel,
)
logger := zap.New(core)
We'd love to support every logging need within zap itself, but we're only familiar with a handful of log ingestion systems, flag-parsing packages, and the like. Rather than merging code that we can't effectively debug and support, we'd rather grow an ecosystem of zap extensions.
We're aware of the following extensions, but haven't used them ourselves:
Package | Integration |
---|---|
github.com/tchap/zapext |
Sentry, syslog |
github.com/fgrosse/zaptest |
Ginkgo |
github.com/blendle/zapdriver |
Stackdriver |