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Table of Contents
Standard library slog: simple and direct, suitable for new projects
Uber's zap: Performance-first, suitable for high concurrency scenarios
logrus: rich in features, suitable for old projects or plug-in development
How to choose a model? Look at these dimensions
Home Backend Development Golang What are the best practices for logging in Go (slog vs zap vs logrus)?

What are the best practices for logging in Go (slog vs zap vs logrus)?

Jul 29, 2025 am 04:24 AM

The Go log library should be selected according to the project requirements, as follows: 1. The standard library slog is preferred for new projects or lightweight requirements, because it has built-in structured log support and no additional dependencies; 2. Recommended zap for high-performance and high-concurrency scenarios, with type safety and excellent throughput capabilities; 3. Select logrus when old projects or need rich plug-ins to support, which has strong compatibility but weak performance. If structured logs are required, slog or zap is preferred. If there is performance pressure, choose zap. If there is already logrus dependencies, you can continue to be used or migrated gradually. Overall, you can select the most suitable log library based on team familiarity and project reality.

What are the best practices for logging in Go (slog vs zap vs logrus)?

Go's log library selection is actually quite critical, especially the three common options: slog, zap and logrus. If you are just starting out or want to optimize your logging system, the following suggestions may help you.

What are the best practices for logging in Go (slog vs zap vs logrus)?

Standard library slog: simple and direct, suitable for new projects

Go 1.21 introduces the standard library slog , which provides an interface for structured logs without the need to introduce additional third-party packages. For most small and medium-sized projects, slog is enough, especially when you don't want to introduce too many dependencies.

  • advantage :
    • Built in the standard library, no installation required
    • API is simple and clear, quick to get started
    • Support structured log output (JSON or text)
  • shortcoming :
    • The functions are relatively basic, such as not adding call stack information automatically
    • Not as extensible as zap or logrus

If you are writing an internal service or gadget, using slog is a lightweight and safe choice. It's easy to configure, just set the handler:

What are the best practices for logging in Go (slog vs zap vs logrus)?
 logger := slog.New(slog.NewJSONHandler(os.Stdout, nil))
slog.SetDefault(logger)

Uber's zap: Performance-first, suitable for high concurrency scenarios

zap is an open source log library for Uber, focusing on high performance and type safety. If your application is sensitive to performance, such as high-frequency trading systems, API gateways, etc., zap is a good choice.

  • advantage :
    • Excellent performance, especially suitable for services with large throughput
    • Two modes are available: high-performance zap.Logger and more flexible zap.SugaredLogger
    • Supports rich log levels, field types and log rotations
  • shortcoming :
    • The configuration is a little more complicated
    • The call stack is not recorded by default and needs to be enabled manually.

When using zap, it is recommended to use NewProductionConfig() or custom configuration to unify the format from the beginning:

What are the best practices for logging in Go (slog vs zap vs logrus)?
 cfg := zap.NewProductionConfig()
cfg.Level = zap.NewAtomicLevelAt(zap.InfoLevel)
logger, _ := cfg.Build()

In addition, zap provides integration capabilities with systems such as opentelemetry, which is very helpful in building observability systems.


logrus: rich in features, suitable for old projects or plug-in development

logrus is an old-fashioned log library that supports hooks and output in multiple formats (including JSON and text), and is also compatible with the log interface of the standard library. If you are maintaining an old project or want to quickly access the logging function, logrus is more suitable.

  • advantage :
    • The plug-in ecosystem is mature and supports various hooks (such as sending to Slack and writing to the database)
    • The usage method is close to the standard library and the learning cost is low
    • Support field addition and log level control
  • shortcoming :
    • Performance is not as good as zap, especially in high concurrency
    • The default output is not structured enough, which can easily lead to difficulty in parsing logs.

The typical usage of logrus is this:

 log := logrus.New()
log.SetLevel(logrus.DebugLevel)
log.WithFields(logrus.Fields{
    "animal": "walrus",
}).Info("A walrus appears")

If you are already using logrus but want to upgrade performance, you can consider migrating to zap or combining both (such as zap to replace the underlying implementation).


How to choose a model? Look at these dimensions

You can compare these three libraries from the following aspects:

  • Are structured logs required ?
    • Yes → slog / zap
    • No → logrus
  • Is there performance pressure ?
    • Yes → zap
    • No → slog or logrus
  • Is there any historical code that depends on logrus ?
    • Yes → Continue to use logrus or gradually migrate
    • No → It is recommended to use slog or zap

Another compromise is to abstract a layer of your own logger interface, so that switching is more convenient in the future.


Basically that's it. Which library to choose is actually not that absolute. The key is to look at your team's familiarity and the specific needs of the project. If it is a new project, slog is enough; if it pursues performance, zap is stronger; if it requires flexibility and historical compatibility, logrus is not bad either.

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