How to set up slog-multi for sending logs to both console and a remote service?

Use the Fanout pattern to distribute logs to multiple handlers in parallel, as shown in the README with examples combining stderr and Logstash. Simply pass multiple handlers to slogmulti.Fanout() to broadcast logs simultaneously.

slog-multi vs using multiple slog handlers manually: what's the benefit?

slog-multi provides structured composition patterns like failover and load balancing that are hard to implement manually, reducing boilerplate and improving reliability. It handles error recovery and conditional routing out-of-the-box, whereas manual setups require custom logic.

How to handle sensitive data in logs with slog-multi?

Implement middleware pipelines to redact PII, using inline middleware functions to filter attributes before they reach handlers. The README shows a GDPR example where attributes like email are masked in a pipelining chain.

Does slog-multi work with Zap or other logging libraries?

Yes, through adapter handlers like slog-zap from the samber ecosystem, but it requires additional setup and might not leverage all native features of those libraries. You'll need to integrate these handlers as part of the composition.

What are the performance implications of using slog-multi in high-throughput applications?

It can introduce overhead due to multiple handler processing; the README recommends careful use, sampling strategies, and monitoring to mitigate costs. For critical paths, consider reducing log volume or using simpler handlers.

How to implement custom routing logic based on log attributes?

Use the Router pattern with custom predicate functions that inspect record attributes, as illustrated in the region-based routing example. Define conditions using built-in predicates or custom functions to match specific keys and values.

slog-multi — Go Structured Logging Patterns

What is slog-multi?

slog-multi is a Go library that extends the standard library's structured logging (slog) by providing advanced handler composition patterns. It enables developers to build complex logging workflows for production systems, solving problems like log distribution, conditional routing, high availability, and data transformation.

Target Audience

Go developers building production-grade applications who need sophisticated logging strategies beyond basic slog handlers, such as those requiring redundancy, conditional routing, or log transformation pipelines.

Value Proposition

Developers choose slog-multi for its enterprise-grade logging patterns that remain idiomatic to Go's slog ecosystem, offering unique composition strategies like fanout, routing, failover, and pipelining not available in the standard library.

🚨 Design workflows of slog handlers: pipeline, middleware, fanout, routing, failover, load balancing...

Use Cases

Best For

Building high-availability logging systems with automatic failover to backup handlers
Distributing logs to multiple destinations (like Logstash, Datadog, and stderr) in parallel for redundancy
Conditionally routing logs based on criteria like log level, attributes, or business logic (e.g., errors to Slack, info to console)
Transforming and filtering logs through middleware chains for data privacy, GDPR compliance, or enrichment
Load balancing logging traffic across multiple handlers to increase throughput and provide redundancy
Gracefully handling logging failures without crashing the application using error recovery mechanisms

Not Ideal For

Applications with straightforward logging needs that are fully served by slog's standard handlers without composition
Performance-sensitive systems where minimizing logging latency is critical, as additional layers can introduce overhead
Teams using alternative logging frameworks like Zap or Logrus without plans to adopt slog
Projects requiring plug-and-play logging with minimal configuration, as slog-multi demands careful setup of handlers and patterns

Pros & Cons

Pros

Advanced Composition Patterns

Provides unique strategies like fanout for parallel logging, routing based on criteria, and failover for high availability, as shown in the README examples for distributing logs to multiple destinations.

Seamless slog Integration

Idiomatically extends Go's standard slog library, allowing developers to use existing slog handlers and options without friction, evidenced by compatibility with Go >= 1.21 and no breaking changes before v2.0.0.

Error Resilience

Includes built-in error recovery mechanisms like RecoverHandlerError to gracefully handle logging failures, preventing application crashes during downstream issues.

Flexible Middleware Support

Offers inline middleware and custom handler shortcuts, enabling rapid implementation of transformation logic such as data redaction or enrichment, as demonstrated in the pipelining examples.

Cons

Configuration Complexity

Setting up advanced patterns like routers or pipelines requires a deep understanding of slog handlers and can be error-prone for newcomers, with multiple steps shown in the usage examples.

Performance Trade-offs

The library adds processing layers; as warned in the README, excessive logging with multiple handlers can significantly impact application performance, making it costly in high-throughput scenarios.

Ecosystem Dependency

For many practical use cases, it relies on external handler libraries from the samber ecosystem (e.g., slog-datadog, slog-slack), increasing dependency management overhead and potential versioning issues.

Frequently Asked Questions

What is slog-multi?

Target Audience

Value Proposition

Use Cases

Best For

Building high-availability logging systems with automatic failover to backup handlers
Distributing logs to multiple destinations (like Logstash, Datadog, and stderr) in parallel for redundancy
Conditionally routing logs based on criteria like log level, attributes, or business logic (e.g., errors to Slack, info to console)
Transforming and filtering logs through middleware chains for data privacy, GDPR compliance, or enrichment
Load balancing logging traffic across multiple handlers to increase throughput and provide redundancy
Gracefully handling logging failures without crashing the application using error recovery mechanisms

Not Ideal For

Applications with straightforward logging needs that are fully served by slog's standard handlers without composition
Performance-sensitive systems where minimizing logging latency is critical, as additional layers can introduce overhead
Teams using alternative logging frameworks like Zap or Logrus without plans to adopt slog
Projects requiring plug-and-play logging with minimal configuration, as slog-multi demands careful setup of handlers and patterns

Pros & Cons

Pros

Advanced Composition Patterns

Seamless slog Integration

Error Resilience

Includes built-in error recovery mechanisms like RecoverHandlerError to gracefully handle logging failures, preventing application crashes during downstream issues.

Flexible Middleware Support

Offers inline middleware and custom handler shortcuts, enabling rapid implementation of transformation logic such as data redaction or enrichment, as demonstrated in the pipelining examples.

Cons

Configuration Complexity

Setting up advanced patterns like routers or pipelines requires a deep understanding of slog handlers and can be error-prone for newcomers, with multiple steps shown in the usage examples.

Performance Trade-offs

The library adds processing layers; as warned in the README, excessive logging with multiple handlers can significantly impact application performance, making it costly in high-throughput scenarios.

Ecosystem Dependency

Frequently Asked Questions

slog-multi

What is slog-multi?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?

slog-multi

What is slog-multi?

Overview

Use Cases

Best For

Not Ideal For

Pros & Cons

Pros

Cons

Frequently Asked Questions

Related Projects

Found a gem we're missing?