Introduction

Processing large amounts of data efficiently is a common challenge. Pipeline patterns break complex processing into stages, where each stage does one thing well and passes results to the next stage.

What Are Pipeline Patterns?

Think of an assembly line: each worker performs a specific task and passes the work to the next person. Pipeline patterns work similarly:

1. Input Stage: Receives raw data
2. Processing Stages: Transform, filter, or enrich data
3. Output Stage: Sends results somewhere useful

Each stage runs concurrently, so while stage 1 processes item N, stage 2 can process item N-1, and stage 3 can process item N-2.

Why Use Pipelines?

Pipelines solve several problems:

• Throughput: Process multiple items simultaneously
• Modularity: Each stage has a single responsibility
• Scalability: Add more workers to bottleneck stages
• Testability: Test each stage independently
• Backpressure: Handle situations where one stage is slower

Common Use Cases

Pipeline patterns work well for:

• Log Processing: Parse, filter, and route log entries
• Image Processing: Resize, compress, and store images
• Data ETL: Extract, transform, and load data
• Stream Analytics: Process real-time event streams
• API Processing: Handle, validate, and respond to requests

Basic Pipeline Structure

func pipeline() {
    // Stage 1: Generate data
    input := make(chan int)
    
    // Stage 2: Process data
    processed := make(chan int)
    
    // Stage 3: Output results
    output := make(chan int)
    
    // Connect the stages
    go generator(input)
    go processor(input, processed)
    go consumer(processed)
}

This guide covers building robust pipelines that handle errors, backpressure, and graceful shutdown.