What are common Go concurrency patterns?

Question

Accepted Answer

Go's goroutines and channels enable a set of well-established **concurrency patterns** that solve common problems — distributing work, fanning out/in, pipelines, and rate limiting. Knowing these idiomatic patterns lets you build correct, efficient concurrent systems.

## Worker pool — bounded concurrency

```go
func workerPool(jobs <-chan int, results chan<- int, numWorkers int) {
    var wg sync.WaitGroup
    for w := 0; w < numWorkers; w++ {     // a FIXED number of workers
        wg.Add(1)
        go func() {
            defer wg.Done()
            for job := range jobs {        // each worker pulls jobs until the channel closes
                results <- process(job)
            }
        }()
    }
    wg.Wait()
    close(results)
}
```

A **worker pool** runs a fixed number of goroutines consuming from a shared jobs channel — bounding concurrency (e.g. don't open 10,000 DB connections; use 10 workers). This is the most important pattern for controlling resource usage.

## Fan-out / fan-in

```text
Fan-out: distribute work across MULTIPLE goroutines (parallelize)
Fan-in:  MERGE results from multiple goroutines back into one channel
```

```go
// fan-in: merge several channels into one
func merge(channels ...<-chan int) <-chan int {
    out := make(chan int)
    var wg sync.WaitGroup
    for _, ch := range channels {
        wg.Add(1)
        go func(c <-chan int) {
            defer wg.Done()
            for v := range c { out <- v }   // forward each channel's values to out
        }(ch)
    }
    go func() { wg.Wait(); close(out) }()    // close out when all sources are drained
    return out
}
```

## Pipeline — stages connected by channels

```go
// each stage is a goroutine: receives from an input channel, sends to an output channel
func generate(nums ...int) <-chan int {
    out := make(chan int)
    go func() { for _, n := range nums { out <- n }; close(out) }()
    return out
}
func square(in <-chan int) <-chan int {
    out := make(chan int)
    go func() { for n := range in { out <- n * n }; close(out) }()
    return out
}
// compose: square(generate(1,2,3)) → a streaming pipeline
for result := range square(generate(1, 2, 3)) { fmt.Println(result) }
```

A **pipeline** chains processing stages via channels — data streams through, each stage transforming it concurrently.

## Rate limiting

```go
limiter := time.Tick(200 * time.Millisecond)   // a tick every 200ms
for req := range requests {
    <-limiter                                    // wait for the next tick before proceeding
    go handle(req)                                // → at most 5 requests/second
}
```

## Cancellation with context (cross-cutting)

```go
select {
case result := <-work:
case <-ctx.Done():    return   // stop on cancellation — prevents leaks
}
```

## Why it matters

These concurrency patterns are the practical toolkit for building real concurrent Go systems, turning raw goroutines and channels into structured, correct solutions to recurring problems.

The **worker pool** is especially important — it bounds concurrency to protect limited resources (database connections, API rate limits, memory), preventing the common mistake of spawning unbounded goroutines that exhaust the system. **Fan-out/fan-in** parallelizes work and merges results; **pipelines** model streaming data transformations cleanly; **rate limiting** controls throughput.

Combined with **context-based cancellation** (to avoid goroutine leaks), these patterns are how production Go services handle concurrency safely and efficiently.

Understanding them — and especially knowing to bound concurrency with worker pools rather than launching unlimited goroutines — is a hallmark of senior Go competence and a frequent advanced interview topic, since naive concurrency (unbounded goroutines, leaks, races) is a common source of production failures.