Table of Contents
2. Logrus (by sirupsen)
3. Slog (Go 1.21 built-in structured logger)
4. Zerolog (by valyala)
Summary of Use Cases
Home Backend Development Golang What are the alternatives to standard library logging in Golang?

What are the alternatives to standard library logging in Golang?

Aug 05, 2025 pm 08:36 PM
golang log

For new Go 1.21 projects, use slog for official structured logging support; 2. For high-performance production services, choose Zap or Zerolog due to their speed and low allocations; 3. For ease of use and rich integrations like Slack or Sentry hooks, Logrus is ideal despite lower performance; 4. For cloud-native applications with multiple output backends, Apex/log is suitable; 5. The best logging library depends on your Go version, performance requirements, and need for structured logs, with each option offering distinct trade-offs in speed, ecosystem, and usability.

What are the alternatives to standard library logging in Golang?

While Go’s standard library log package is simple and sufficient for basic logging needs, several third-party libraries offer enhanced features like structured logging, better performance, leveled logging, and flexible output formatting. Here are some popular alternatives:

What are the alternatives to standard library logging in Golang?

1. Zap (by Uber)

Zap is one of the most widely used high-performance logging libraries in Go. It’s designed for applications that require fast, structured, and leveled logging.

  • Pros:

    What are the alternatives to standard library logging in Golang?
    • Extremely fast (one of the fastest in Go).
    • Structured logging with JSON output by default.
    • Supports both reflection-based (SugaredLogger) and zero-allocation (Logger) modes.
    • Built-in support for log levels (debug, info, warn, error, etc.).
    • Integrates well with observability tools.
  • Use case: Ideal for production services where performance and structured logs are important.

logger, _ := zap.NewProduction()
defer logger.Sync()
logger.Info("message", zap.String("key", "value"))

2. Logrus (by sirupsen)

Logrus is a popular structured logger with a familiar API similar to the standard log package.

What are the alternatives to standard library logging in Golang?
  • Pros:

    • Easy to use and migrate to from log.
    • Supports structured logging with fields.
    • Pluggable hooks (e.g., send errors to Slack or Sentry).
    • Multiple formatters (JSON, text, logstash).
    • Active community and well-documented.
  • Cons:

    • Slower than Zap due to reflection and interface-heavy design.
  • Use case: Great for development and mid-performance applications where ease of use matters.

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

3. Slog (Go 1.21 built-in structured logger)

Introduced in Go 1.21, slog is the new built-in structured logging package, intended to eventually replace or complement the old log package.

  • Pros:

    • Official structured logging support.
    • Built-in support for attributes, levels, and handlers (text, JSON).
    • Can replace the default logger via log.SetDefault().
    • Designed with extensibility and performance in mind.
  • Cons:

    • Newer, so ecosystem and tooling still maturing.
    • Fewer third-party integrations compared to Zap or Logrus.
  • Use case: Recommended for new Go projects targeting 1.21 .

logger := slog.New(slog.NewJSONHandler(os.Stdout, nil))
slog.SetDefault(logger)
slog.Info("hello", "name", "World")

4. Zerolog (by valyala)

Zerolog emphasizes performance and zero memory allocations, leveraging composition and compile-time optimizations.

  • Pros:

    • Very fast, often benchmarks faster than Zap.
    • Minimal memory allocations.
    • Simple API with fluent chaining.
    • Supports both JSON and pretty-printed output.
  • Cons:

    • Less familiar syntax for beginners.
    • Smaller ecosystem compared to Zap or Logrus.
  • Use case: High-performance services, especially in constrained environments.

logger := zerolog.New(os.Stdout).With().Timestamp().Logger()
logger.Info().Str("name", "john").Msg("hello")

5. Apex/log

A structured logger with support for multiple backends and dynamic verbosity.

  • Pros:

    • Clean API with context-based logging.
    • Multiple handlers (cloudwatch, papertrail, etc.).
    • Integrates with AWS Lambda and other cloud platforms.
    • Supports hierarchical loggers.
  • Cons:

    • Less actively maintained than Zap or Zerolog.
    • Smaller community.

Summary of Use Cases

Logger Best For Performance Structured
log Simple scripts, minimal needs Low No
slog New Go 1.21 apps, official support Medium Yes
Zap High-performance production services High Yes
Logrus Easy migration, rich ecosystem Medium Yes
Zerolog Max performance, low allocation Very High Yes
Apex Cloud-native apps, multiple outputs Medium Yes

If you're starting a new project in Go 1.21 , consider using slog for built-in structured logging. For maximum performance, go with Zap or Zerolog. If you value simplicity and hooks, Logrus is still a solid choice.

Basically, the right choice depends on your performance needs, Go version, and whether you want structured logs.

The above is the detailed content of What are the alternatives to standard library logging in Golang?. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undress AI Tool

Undress AI Tool

Undress images for free

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Hot Topics

PHP Tutorial
1510
276
Strategies for Integrating Golang Services with Existing Python Infrastructure Strategies for Integrating Golang Services with Existing Python Infrastructure Jul 02, 2025 pm 04:39 PM

TointegrateGolangserviceswithexistingPythoninfrastructure,useRESTAPIsorgRPCforinter-servicecommunication,allowingGoandPythonappstointeractseamlesslythroughstandardizedprotocols.1.UseRESTAPIs(viaframeworkslikeGininGoandFlaskinPython)orgRPC(withProtoco

Understanding the Performance Differences Between Golang and Python for Web APIs Understanding the Performance Differences Between Golang and Python for Web APIs Jul 03, 2025 am 02:40 AM

Golangofferssuperiorperformance,nativeconcurrencyviagoroutines,andefficientresourceusage,makingitidealforhigh-traffic,low-latencyAPIs;2.Python,whileslowerduetointerpretationandtheGIL,provideseasierdevelopment,arichecosystem,andisbettersuitedforI/O-bo

Memory Footprint Comparison: Running Identical Web Service Workloads in Golang and Python Memory Footprint Comparison: Running Identical Web Service Workloads in Golang and Python Jul 03, 2025 am 02:32 AM

GousessignificantlylessmemorythanPythonwhenrunningwebservicesduetolanguagedesignandconcurrencymodeldifferences.1.Go'sgoroutinesarelightweightwithminimalstackoverhead,allowingefficienthandlingofthousandsofconnections.2.Itsgarbagecollectorisoptimizedfo

The State of Machine Learning Libraries: Golang's Offerings vs the Extensive Python Ecosystem The State of Machine Learning Libraries: Golang's Offerings vs the Extensive Python Ecosystem Jul 03, 2025 am 02:00 AM

Pythonisthedominantlanguageformachinelearningduetoitsmatureecosystem,whileGoofferslightweighttoolssuitedforspecificusecases.PythonexcelswithlibrarieslikeTensorFlow,PyTorch,Scikit-learn,andPandas,makingitidealforresearch,prototyping,anddeployment.Go,d

Understanding Memory Management Differences: Golang's GC vs Python's Reference Counting Understanding Memory Management Differences: Golang's GC vs Python's Reference Counting Jul 03, 2025 am 02:31 AM

The core difference between Go and Python in memory management is the different garbage collection mechanisms. Go uses concurrent mark clearance (MarkandSweep) GC, which automatically runs and executes concurrently with program logic, effectively deals with circular references. It is suitable for high concurrency scenarios, but cannot accurately control the recycling time; while Python mainly relies on reference counting, and object references are immediately released when zeroed. The advantage is that they are instant recycling and simple implementation, but there is a circular reference problem, so they need to use the GC module to assist in cleaning. In actual development, Go is more suitable for high-performance server programs, while Python is suitable for script classes or applications with low performance requirements.

Evaluating the Community and Support for Golang vs Python in Enterprise Environments Evaluating the Community and Support for Golang vs Python in Enterprise Environments Jul 02, 2025 pm 03:34 PM

Python is suitable for scenarios that require extensive applicability, rich community resources and mature enterprise support. It has a huge community, rich library and business support, and is suitable for data science, automation, and traditional system integration. 2. Golang is suitable for high-performance backend scenarios such as cloud native, microservices, and DevOps. The community is small but grows rapidly. It has excellent concurrency models and performance, and is suitable for infrastructure modernization. 3. The choice depends on the specific needs of the enterprise: if you need to get started quickly and have a wide range of talent reserves, choose Python; if you focus on high concurrency and scalable service construction, Golang is better. Both communities have their own strengths, and ultimately, decisions should be made based on technical goals rather than absolute strengths and weaknesses.

Building Command-Line Tools: Advantages of Golang Over Python for Distribution Building Command-Line Tools: Advantages of Golang Over Python for Distribution Jul 02, 2025 pm 04:24 PM

When building command line tools for distribution, Golang is more suitable than Python. The reasons include: 1. Simple distribution, and a single static binary file is generated after Go compiles, without additional dependencies; 2. Fast startup speed, low resource usage, Go is a compiled language, high execution efficiency and small memory usage; 3. Supports cross-platform compilation, no additional packaging tools are required, and executable files of different platforms can be generated with simple commands. In contrast, Python requires installation of runtime and dependency libraries, which are slow to start, complex packaging processes, and prone to compatibility and false positives, so it is not as good as Go in terms of deployment experience and maintenance costs.

Migrating a Python Web Application Monolith to Golang Microservices Architecture Migrating a Python Web Application Monolith to Golang Microservices Architecture Jul 03, 2025 am 01:53 AM

The core of migrating to Golang microservices architecture is to clarify service boundaries, select communication modes, manage data flows, and optimize deployment monitoring. First, independent services are defined by identifying business logic boundaries such as user management, payment and other modules, and the principles of high cohesion and low coupling and domain-driven design are followed; second, REST, gRPC or message queues are selected as communication methods according to needs, such as using event asynchronous notifications instead of direct calls; then, each service independently manages the database and exchanges data through API or event, and uses CQRS or Saga to process distributed transactions; finally, Docker containerization and Kubernetes orchestration and deployment services are used to combine logs, metrics and tracking tools to achieve comprehensive observability.

See all articles