The basic structure of Pipeline is a processing chain composed of multiple stages. Each stage communicates through channel and data flows in turn. The first stage generates data; the second stage processes data; the third stage outputs results. For example: generate numbers → calculate squares → output results. Three points to be noted when designing a robust pipeline: 1. Control the number of goroutines to avoid resource exhaustion, such as using a fixed number of workers to perform tasks concurrently; 2. Correctly close the channel to avoid deadlocks, the sender closes the channel, the receiver does not close, and when more senders are sent, you need to coordinate the closing time; 3. Use context to control the life cycle, which is used to exit all goroutines by error or timeout. Common problems include missing channel to cause goroutine leakage, uncontrolled concurrency to cause OOM, repeated channel to cause panic, and sequential dependence to damage logic. Applicable scenarios include data conversion pipelines, batch network requests, and background task queues. Mastering the coordination of goroutine, channel and context can build an efficient and stable pipeline.
In Go language, the pipeline pattern is a common programming pattern that uses goroutine and channel to implement concurrent task pipeline processing. It achieves an efficient concurrency process by splitting the task into stages, each phase being processed in parallel by one or more goroutines and passing the data to the next stage in turn through the channel.
This model is particularly suitable for scenarios where large amounts of data are processed in stages, such as file processing, network request flow, data conversion, etc.
What is the basic structure of pipeline?
Pipeline is essentially a processing chain consisting of multiple stages:
- Communication between stages through channel
- There can be multiple goroutines per stage to execute concurrently
- Data flows from one stage to another like water flow
A simplest example is: Generate data -> Process data -> Output results
// Stage 1: Generate the number func gen(nums ...int) <-chan int {
out := make(chan int)
go func() {
for _, n := range nums {
out <- n
}
close(out)
}()
return out
}
// Stage 2: Calculate the square func square(in <-chan int) <-chan int {
out := make(chan int)
go func() {
for n := range in {
out <- n * n
}
close(out)
}()
return out
}You can use it in series like this:
c := gen(2, 3)
squared := square(c)
for result := range squared {
fmt.Println(result)
}How to design a robust pipeline?
Building a practical pipeline is not just as simple as writing a few functions, but also the following points need to be considered:
1. Control the number of goroutines to avoid resource exhaustion
Do not start a goroutine for each task. Limit the number of concurrency, otherwise it may cause memory explosions or system stuttering.
func worker(id int, jobs <-chan int, results chan<- int) {
for j := range jobs {
fmt.Println("worker", id, "processing", j)
time.Sleep(time.Second)
results <- j * j
}
}Then use a fixed number of goroutines to consume jobs:
jobs := make(chan int, 100)
results := make(chan int, 100)
for w := 1; w <= 3; w {
go worker(w, jobs, results)
}2. Close the channel correctly to avoid deadlocks
- Only the sender is responsible for closing the channel
- The receiver should not try to close the channel
- When multiple senders, the shutdown time needs to be coordinated
3. Use context to control life cycle
If your pipeline is running in an HTTP request or background task, it is recommended to introduce context.Context to exit all goroutines in advance if an error or timeout occurs.
ctx, cancel := context.WithCancel(context.Background())
defer cancel()
// Listen to ctx.Done() at a certain stage
select {
case <-ctx.Done():
Return
case ch <- data:
}Frequently Asked Questions and Precautions
Missing channel causes goroutine to leak
If you forget to close the channel, the receiver will wait, causing the program to fail to end normally.No control over concurrency results in resource exhaustion
Especially when processing large amounts of data, OOM may be triggered without limiting the number of goroutines.Repeat the channel to close repeatedly incorrectly
Repeated closing of the channel is not allowed in Go, otherwise it will be panic.The issue of order dependency
If a certain stage of the pipeline must be executed in sequence, it cannot be parallelized at will, otherwise it will destroy the logic.
What scenarios are applicable to Pipeline mode?
- Data conversion pipeline (such as log analysis, image processing)
- Batch network requests (concurrent pull API data)
- Background task queue (task processing in stages)
Basically that's it. Master the cooperation of goroutine channel and the context controls the life cycle, you can write an efficient and stable pipeline.
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