How does this skill help with Go race conditions?

The skill provides implementation patterns for sync primitives and channel-based communication that prevent shared memory conflicts, which are the primary cause of race conditions.

Can I use this for microservices development?

Absolutely. It covers context management and graceful shutdown patterns, which are essential for building resilient and cloud-native microservices in Go.

Does it support the 'sync.Map' and custom sharding?

Yes, the skill provides templates for both 'sync.Map' for read-heavy workloads and sharded map implementations for write-heavy concurrent applications.

Does it include patterns for high-volume data processing?

Yes, it specifically includes Fan-Out/Fan-In and Worker Pool patterns designed to handle massive throughput by distributing tasks across multiple goroutines.

Go Concurrency Patterns

Name: Go Concurrency Patterns
Author: Tahir-yamin

byTahir-yamin

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API 开发

Implements production-grade Go concurrency patterns including worker pools, pipelines, and graceful shutdowns.

关于

This skill provides a comprehensive toolkit for building high-performance, concurrent Go applications. It guides users through implementing robust concurrency primitives like goroutines and channels while offering battle-tested templates for advanced patterns such as fan-out/fan-in pipelines, bounded concurrency with semaphores, and thread-safe data structures. Whether you are debugging race conditions or architecting scalable backend services, this skill ensures your Go code follows the 'share memory by communicating' philosophy for maximum reliability and performance.

主要功能

Expert guidance on avoiding deadlocks and race conditions
Thread-safe data structure patterns including sharded maps
Bounded concurrency patterns using semaphores and errgroups
Production-ready worker pool and pipeline implementations
Standardized graceful shutdown and context management logic
3 GitHub stars

使用场景

Implementing reliable server-side resource cleanup and signal handling
Scaling backend services with efficient asynchronous worker pools
Processing high-throughput data streams via fan-out/fan-in architectures

关于

主要功能

Expert guidance on avoiding deadlocks and race conditions
Thread-safe data structure patterns including sharded maps
Bounded concurrency patterns using semaphores and errgroups
Production-ready worker pool and pipeline implementations
Standardized graceful shutdown and context management logic
3 GitHub stars

使用场景

Implementing reliable server-side resource cleanup and signal handling
Scaling backend services with efficient asynchronous worker pools
Processing high-throughput data streams via fan-out/fan-in architectures