Sign inDemoInstall

Package Overview
File Explorer

Install Socket

Protect your apps from supply chain attacks


Package logr defines a general-purpose logging API and abstract interfaces to back that API. Packages in the Go ecosystem can depend on this package, while callers can implement logging with whatever backend is appropriate. Logging is done using a Logger instance. Logger is a concrete type with methods, which defers the actual logging to a LogSink interface. The main methods of Logger are Info() and Error(). Arguments to Info() and Error() are key/value pairs rather than printf-style formatted strings, emphasizing "structured logging". With Go's standard log package, we might write: With logr's structured logging, we'd write: Errors are much the same. Instead of: We'd write: Info() and Error() are very similar, but they are separate methods so that LogSink implementations can choose to do things like attach additional information (such as stack traces) on calls to Error(). Error() messages are always logged, regardless of the current verbosity. If there is no error instance available, passing nil is valid. Often we want to log information only when the application in "verbose mode". To write log lines that are more verbose, Logger has a V() method. The higher the V-level of a log line, the less critical it is considered. Log-lines with V-levels that are not enabled (as per the LogSink) will not be written. Level V(0) is the default, and logger.V(0).Info() has the same meaning as logger.Info(). Negative V-levels have the same meaning as V(0). Error messages do not have a verbosity level and are always logged. Where we might have written: We can write: Logger instances can have name strings so that all messages logged through that instance have additional context. For example, you might want to add a subsystem name: The WithName() method returns a new Logger, which can be passed to constructors or other functions for further use. Repeated use of WithName() will accumulate name "segments". These name segments will be joined in some way by the LogSink implementation. It is strongly recommended that name segments contain simple identifiers (letters, digits, and hyphen), and do not contain characters that could muddle the log output or confuse the joining operation (e.g. whitespace, commas, periods, slashes, brackets, quotes, etc). Logger instances can store any number of key/value pairs, which will be logged alongside all messages logged through that instance. For example, you might want to create a Logger instance per managed object: With the standard log package, we might write: With logr we'd write: Logger has very few hard rules, with the goal that LogSink implementations might have a lot of freedom to differentiate. There are, however, some things to consider. The log message consists of a constant message attached to the log line. This should generally be a simple description of what's occurring, and should never be a format string. Variable information can then be attached using named values. Keys are arbitrary strings, but should generally be constant values. Values may be any Go value, but how the value is formatted is determined by the LogSink implementation. Logger instances are meant to be passed around by value. Code that receives such a value can call its methods without having to check whether the instance is ready for use. The zero logger (= Logger{}) is identical to Discard() and discards all log entries. Code that receives a Logger by value can simply call it, the methods will never crash. For cases where passing a logger is optional, a pointer to Logger should be used. Keys are not strictly required to conform to any specification or regex, but it is recommended that they: These guidelines help ensure that log data is processed properly regardless of the log implementation. For example, log implementations will try to output JSON data or will store data for later database (e.g. SQL) queries. While users are generally free to use key names of their choice, it's generally best to avoid using the following keys, as they're frequently used by implementations: Implementations are encouraged to make use of these keys to represent the above concepts, when necessary (for example, in a pure-JSON output form, it would be necessary to represent at least message and timestamp as ordinary named values). Implementations may choose to give callers access to the underlying logging implementation. The recommended pattern for this is: Logger grants access to the sink to enable type assertions like this: Custom `With*` functions can be implemented by copying the complete Logger struct and replacing the sink in the copy: Don't use New to construct a new Logger with a LogSink retrieved from an existing Logger. Source code attribution might not work correctly and unexported fields in Logger get lost. Beware that the same LogSink instance may be shared by different logger instances. Calling functions that modify the LogSink will affect all of those.


Version published


# A minimal logging API for Go

[![Go Reference](](
[![OpenSSF Scorecard](](

logr offers an(other) opinion on how Go programs and libraries can do logging
without becoming coupled to a particular logging implementation.  This is not
an implementation of logging - it is an API.  In fact it is two APIs with two
different sets of users.

The `Logger` type is intended for application and library authors.  It provides
a relatively small API which can be used everywhere you want to emit logs.  It
defers the actual act of writing logs (to files, to stdout, or whatever) to the
`LogSink` interface.

The `LogSink` interface is intended for logging library implementers.  It is a
pure interface which can be implemented by logging frameworks to provide the actual logging

This decoupling allows application and library developers to write code in
terms of `logr.Logger` (which has very low dependency fan-out) while the
implementation of logging is managed "up stack" (e.g. in or near `main()`.)
Application developers can then switch out implementations as necessary.

Many people assert that libraries should not be logging, and as such efforts
like this are pointless.  Those people are welcome to convince the authors of
the tens-of-thousands of libraries that *DO* write logs that they are all
wrong.  In the meantime, logr takes a more practical approach.

## Typical usage

Somewhere, early in an application's life, it will make a decision about which
logging library (implementation) it actually wants to use.  Something like:

    func main() {
        // ... other setup code ...

        // Create the "root" logger.  We have chosen the "logimpl" implementation,
        // which takes some initial parameters and returns a logr.Logger.
        logger := logimpl.New(param1, param2)

        // ... other setup code ...

Most apps will call into other libraries, create structures to govern the flow,
etc.  The `logr.Logger` object can be passed to these other libraries, stored
in structs, or even used as a package-global variable, if needed.  For example:

    app := createTheAppObject(logger)

Outside of this early setup, no other packages need to know about the choice of
implementation.  They write logs in terms of the `logr.Logger` that they

    type appObject struct {
        // ... other fields ...
        logger logr.Logger
        // ... other fields ...

    func (app *appObject) Run() {
        app.logger.Info("starting up", "timestamp", time.Now())

        // ... app code ...

## Background

If the Go standard library had defined an interface for logging, this project
probably would not be needed.  Alas, here we are.

When the Go developers started developing such an interface with
[slog](, they adopted some of the
logr design but also left out some parts and changed others:

| Feature | logr | slog |
| High-level API | `Logger` (passed by value) | `Logger` (passed by [pointer]( |
| Low-level API | `LogSink` | `Handler` |
| Stack unwinding | done by `LogSink` | done by `Logger` |
| Skipping helper functions | `WithCallDepth`, `WithCallStackHelper` | [not supported by Logger]( |
| Generating a value for logging on demand | `Marshaler` | `LogValuer` |
| Log levels | >= 0, higher meaning "less important" | positive and negative, with 0 for "info" and higher meaning "more important" |
| Error log entries | always logged, don't have a verbosity level | normal log entries with level >= `LevelError` |
| Passing logger via context | `NewContext`, `FromContext` | no API |
| Adding a name to a logger | `WithName` | no API |
| Modify verbosity of log entries in a call chain | `V` | no API |
| Grouping of key/value pairs | not supported | `WithGroup`, `GroupValue` |
| Pass context for extracting additional values | no API | API variants like `InfoCtx` |

The high-level slog API is explicitly meant to be one of many different APIs
that can be layered on top of a shared `slog.Handler`. logr is one such
alternative API, with [interoperability](#slog-interoperability) provided by
some conversion functions.

### Inspiration

Before you consider this package, please read [this blog post by the
inimitable Dave Cheney][warning-makes-no-sense].  We really appreciate what
he has to say, and it largely aligns with our own experiences.

### Differences from Dave's ideas

The main differences are:

1. Dave basically proposes doing away with the notion of a logging API in favor
of `fmt.Printf()`.  We disagree, especially when you consider things like output
locations, timestamps, file and line decorations, and structured logging.  This
package restricts the logging API to just 2 types of logs: info and error.

Info logs are things you want to tell the user which are not errors.  Error
logs are, well, errors.  If your code receives an `error` from a subordinate
function call and is logging that `error` *and not returning it*, use error

2. Verbosity-levels on info logs.  This gives developers a chance to indicate
arbitrary grades of importance for info logs, without assigning names with
semantic meaning such as "warning", "trace", and "debug."  Superficially this
may feel very similar, but the primary difference is the lack of semantics.
Because verbosity is a numerical value, it's safe to assume that an app running
with higher verbosity means more (and less important) logs will be generated.

## Implementations (non-exhaustive)

There are implementations for the following logging libraries:

- **a function** (can bridge to non-structured libraries): [funcr](
- **a testing.T** (for use in Go tests, with JSON-like output): [testr](
- ****: [glogr](
- **** (for Kubernetes): [klogr](
- **a testing.T** (with klog-like text output): [ktesting](
- ****: [zapr](
- **log** (the Go standard library logger): [stdr](
- ****: [logrusr](
- ****: [genericr]( (makes it easy to implement your own backend)
- **logfmt** (Heroku style [logging]( [logfmtr](
- ****: [zerologr](
- ****: [gokitlogr]( (also compatible with since v0.12.0)
- **bytes.Buffer** (writing to a buffer): [bufrlogr]( (useful for ensuring values were logged, like during testing)

## slog interoperability

Interoperability goes both ways, using the `logr.Logger` API with a `slog.Handler`
and using the `slog.Logger` API with a `logr.LogSink`. `FromSlogHandler` and
`ToSlogHandler` convert between a `logr.Logger` and a `slog.Handler`.
As usual, `slog.New` can be used to wrap such a `slog.Handler` in the high-level
slog API.

### Using a `logr.LogSink` as backend for slog

Ideally, a logr sink implementation should support both logr and slog by
implementing both the normal logr interface(s) and `SlogSink`.  Because
of a conflict in the parameters of the common `Enabled` method, it is [not
possible to implement both slog.Handler and logr.Sink in the same

If both are supported, log calls can go from the high-level APIs to the backend
without the need to convert parameters. `FromSlogHandler` and `ToSlogHandler` can
convert back and forth without adding additional wrappers, with one exception:
when `Logger.V` was used to adjust the verbosity for a `slog.Handler`, then
`ToSlogHandler` has to use a wrapper which adjusts the verbosity for future
log calls.

Such an implementation should also support values that implement specific
interfaces from both packages for logging (`logr.Marshaler`, `slog.LogValuer`,
`slog.GroupValue`). logr does not convert those.

Not supporting slog has several drawbacks:
- Recording source code locations works correctly if the handler gets called
  through `slog.Logger`, but may be wrong in other cases. That's because a
  `logr.Sink` does its own stack unwinding instead of using the program counter
  provided by the high-level API.
- slog levels <= 0 can be mapped to logr levels by negating the level without a
  loss of information. But all slog levels > 0 (e.g. `slog.LevelWarning` as
  used by `slog.Logger.Warn`) must be mapped to 0 before calling the sink
  because logr does not support "more important than info" levels.
- The slog group concept is supported by prefixing each key in a key/value
  pair with the group names, separated by a dot. For structured output like
  JSON it would be better to group the key/value pairs inside an object.
- Special slog values and interfaces don't work as expected.
- The overhead is likely to be higher.

These drawbacks are severe enough that applications using a mixture of slog and
logr should switch to a different backend.

### Using a `slog.Handler` as backend for logr

Using a plain `slog.Handler` without support for logr works better than the
other direction:
- All logr verbosity levels can be mapped 1:1 to their corresponding slog level
  by negating them.
- Stack unwinding is done by the `SlogSink` and the resulting program
  counter is passed to the `slog.Handler`.
- Names added via `Logger.WithName` are gathered and recorded in an additional
  attribute with `logger` as key and the names separated by slash as value.
- `Logger.Error` is turned into a log record with `slog.LevelError` as level
  and an additional attribute with `err` as key, if an error was provided.

The main drawback is that `logr.Marshaler` will not be supported. Types should
ideally support both `logr.Marshaler` and `slog.Valuer`. If compatibility
with logr implementations without slog support is not important, then
`slog.Valuer` is sufficient.

### Context support for slog

Storing a logger in a `context.Context` is not supported by
slog. `NewContextWithSlogLogger` and `FromContextAsSlogLogger` can be
used to fill this gap. They store and retrieve a `slog.Logger` pointer
under the same context key that is also used by `NewContext` and
`FromContext` for `logr.Logger` value.

When `NewContextWithSlogLogger` is followed by `FromContext`, the latter will
automatically convert the `slog.Logger` to a
`logr.Logger`. `FromContextAsSlogLogger` does the same for the other direction.

With this approach, binaries which use either slog or logr are as efficient as
possible with no unnecessary allocations. This is also why the API stores a
`slog.Logger` pointer: when storing a `slog.Handler`, creating a `slog.Logger`
on retrieval would need to allocate one.

The downside is that switching back and forth needs more allocations. Because
logr is the API that is already in use by different packages, in particular
Kubernetes, the recommendation is to use the `logr.Logger` API in code which
uses contextual logging.

An alternative to adding values to a logger and storing that logger in the
context is to store the values in the context and to configure a logging
backend to extract those values when emitting log entries. This only works when
log calls are passed the context, which is not supported by the logr API.

With the slog API, it is possible, but not
required. is a package for slog which
provides additional support code for this approach. It also contains wrappers
for the context functions in logr, so developers who prefer to not use the logr
APIs directly can use those instead and the resulting code will still be
interoperable with logr.

## FAQ

### Conceptual

#### Why structured logging?

- **Structured logs are more easily queryable**: Since you've got
  key-value pairs, it's much easier to query your structured logs for
  particular values by filtering on the contents of a particular key --
  think searching request logs for error codes, Kubernetes reconcilers for
  the name and namespace of the reconciled object, etc.

- **Structured logging makes it easier to have cross-referenceable logs**:
  Similarly to searchability, if you maintain conventions around your
  keys, it becomes easy to gather all log lines related to a particular

- **Structured logs allow better dimensions of filtering**: if you have
  structure to your logs, you've got more precise control over how much
  information is logged -- you might choose in a particular configuration
  to log certain keys but not others, only log lines where a certain key
  matches a certain value, etc., instead of just having v-levels and names
  to key off of.

- **Structured logs better represent structured data**: sometimes, the
  data that you want to log is inherently structured (think tuple-link
  objects.)  Structured logs allow you to preserve that structure when

#### Why V-levels?

**V-levels give operators an easy way to control the chattiness of log
operations**.  V-levels provide a way for a given package to distinguish
the relative importance or verbosity of a given log message.  Then, if
a particular logger or package is logging too many messages, the user
of the package can simply change the v-levels for that library.

#### Why not named levels, like Info/Warning/Error?

Read [Dave Cheney's post][warning-makes-no-sense].  Then read [Differences
from Dave's ideas](#differences-from-daves-ideas).

#### Why not allow format strings, too?

**Format strings negate many of the benefits of structured logs**:

- They're not easily searchable without resorting to fuzzy searching,
  regular expressions, etc.

- They don't store structured data well, since contents are flattened into
  a string.

- They're not cross-referenceable.

- They don't compress easily, since the message is not constant.

(Unless you turn positional parameters into key-value pairs with numerical
keys, at which point you've gotten key-value logging with meaningless

### Practical

#### Why key-value pairs, and not a map?

Key-value pairs are *much* easier to optimize, especially around
allocations.  Zap (a structured logger that inspired logr's interface) has
[performance measurements](
that show this quite nicely.

While the interface ends up being a little less obvious, you get
potentially better performance, plus avoid making users type
`map[string]string{}` every time they want to log.

#### What if my V-levels differ between libraries?

That's fine.  Control your V-levels on a per-logger basis, and use the
`WithName` method to pass different loggers to different libraries.

Generally, you should take care to ensure that you have relatively
consistent V-levels within a given logger, however, as this makes deciding
on what verbosity of logs to request easier.

#### But I really want to use a format string!

That's not actually a question.  Assuming your question is "how do
I convert my mental model of logging with format strings to logging with
constant messages":

1. Figure out what the error actually is, as you'd write in a TL;DR style,
   and use that as a message.

2. For every place you'd write a format specifier, look to the word before
   it, and add that as a key value pair.

For instance, consider the following examples (all taken from spots in the
Kubernetes codebase):

- `klog.V(4).Infof("Client is returning errors: code %v, error %v",
  responseCode, err)` becomes `logger.Error(err, "client returned an
  error", "code", responseCode)`

- `klog.V(4).Infof("Got a Retry-After %ds response for attempt %d to %v",
  seconds, retries, url)` becomes `logger.V(4).Info("got a retry-after
  response when requesting url", "attempt", retries, "after
  seconds", seconds, "url", url)`

If you *really* must use a format string, use it in a key's value, and
call `fmt.Sprintf` yourself.  For instance: `log.Printf("unable to
reflect over type %T")` becomes `logger.Info("unable to reflect over
type", "type", fmt.Sprintf("%T"))`.  In general though, the cases where
this is necessary should be few and far between.

#### How do I choose my V-levels?

This is basically the only hard constraint: increase V-levels to denote
more verbose or more debug-y logs.

Otherwise, you can start out with `0` as "you always want to see this",
`1` as "common logging that you might *possibly* want to turn off", and
`10` as "I would like to performance-test your log collection stack."

Then gradually choose levels in between as you need them, working your way
down from 10 (for debug and trace style logs) and up from 1 (for chattier
info-type logs). For reference, slog pre-defines -4 for debug logs
(corresponds to 4 in logr), which matches what is
[recommended for Kubernetes](

#### How do I choose my keys?

Keys are fairly flexible, and can hold more or less any string
value. For best compatibility with implementations and consistency
with existing code in other projects, there are a few conventions you
should consider.

- Make your keys human-readable.
- Constant keys are generally a good idea.
- Be consistent across your codebase.
- Keys should naturally match parts of the message string.
- Use lower case for simple keys and
  [lowerCamelCase]( for
  more complex ones. Kubernetes is one example of a project that has
  [adopted that

While key names are mostly unrestricted (and spaces are acceptable),
it's generally a good idea to stick to printable ascii characters, or at
least match the general character set of your log lines.

#### Why should keys be constant values?

The point of structured logging is to make later log processing easier.  Your
keys are, effectively, the schema of each log message.  If you use different
keys across instances of the same log line, you will make your structured logs
much harder to use.  `Sprintf()` is for values, not for keys!

#### Why is this not a pure interface?

The Logger type is implemented as a struct in order to allow the Go compiler to
optimize things like high-V `Info` logs that are not triggered.  Not all of
these implementations are implemented yet, but this structure was suggested as
a way to ensure they *can* be implemented.  All of the real work is behind the
`LogSink` interface.



Last updated on 21 Dec 2023

Did you know?

Socket installs a GitHub app to automatically flag issues on every pull request and report the health of your dependencies. Find out what is inside your node modules and prevent malicious activity before you update the dependencies.


Related posts

SocketSocket SOC 2 Logo


  • Package Alerts
  • Integrations
  • Docs
  • Pricing
  • FAQ
  • Roadmap

Stay in touch

Get open source security insights delivered straight into your inbox.

  • Terms
  • Privacy
  • Security

Made with ⚡️ by Socket Inc