Go Concurrency Patterns

Production patterns for Go concurrency including goroutines, channels, synchronization primitives, and context management.

When to Use This Skill

• Building concurrent Go applications
• Implementing worker pools and pipelines
• Managing goroutine lifecycles
• Using channels for communication
• Debugging race conditions
• Implementing graceful shutdown

Core Concepts

1. Go Concurrency Primitives

2. Go Concurrency Mantra

Don't communicate by sharing memory;
share memory by communicating.

Quick Start

package main

import (
    "context"
    "fmt"
    "sync"
    "time"
)

func main() {
    ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
    defer cancel()

    results := make(chan string, 10)
    var wg sync.WaitGroup

    // Spawn workers
    for i := 0; i < 3; i++ {
        wg.Add(1)
        go worker(ctx, i, results, &wg)
    }

    // Close results when done
    go func() {
        wg.Wait()
        close(results)
    }()

    // Collect results
    for result := range results {
        fmt.Println(result)
    }
}

func worker(ctx context.Context, id int, results chan<- string, wg *sync.WaitGroup) {
    defer wg.Done()

    select {
    case <-ctx.Done():
        return
    case results <- fmt.Sprintf("Worker %d done", id):
    }
}

Patterns

Pattern 1: Worker Pool

package main

import (
    "context"
    "fmt"
    "sync"
)

type Job struct {
    ID   int
    Data string
}

type Result struct {
    JobID int
    Output string
    Err   error
}

func WorkerPool(ctx context.Context, numWorkers int, jobs <-chan Job) <-chan Result {
    results := make(chan Result, len(jobs))

    var wg sync.WaitGroup
    for i := 0; i < numWorkers; i++ {
        wg.Add(1)
        go func(workerID int) {
            defer wg.Done()
            for job := range jobs {
                select {
                case <-ctx.Done():
                    return
                default:
                    result := processJob(job)
                    results <- result
                }
            }
        }(i)
    }

    go func() {
        wg.Wait()
        close(results)
    }()

    return results
}

func processJob(job Job) Result {
    // Simulate work
    return Result{
        JobID:  job.ID,
        Output: fmt.Sprintf("Processed: %s", job.Data),
    }
}

// Usage
func main() {
    ctx, cancel := context.WithCancel(context.Background())
    defer cancel()

    jobs := make(chan Job, 100)

    // Send jobs
    go func() {
        for i := 0; i < 50; i++ {
            jobs <- Job{ID: i, Data: fmt.Sprintf("job-%d", i)}
        }
        close(jobs)
    }()

    // Process with 5 workers
    results := WorkerPool(ctx, 5, jobs)

    for result := range results {
        fmt.Printf("Result: %+v\n", result)
    }
}

Pattern 2: Fan-Out/Fan-In Pipeline

package main

import (
    "context"
    "sync"
)

// Stage 1: Generate numbers
func generate(ctx context.Context, nums ...int) <-chan int {
    out := make(chan int)
    go func() {
        defer close(out)
        for _, n := range nums {
            select {
            case <-ctx.Done():
                return
            case out <- n:
            }
        }
    }()
    return out
}
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