Concurrency Patterns in Go: A *Short* Deep Dive Series

Introduction

Concurrency is one of those topics that can make even experienced developers break out in a cold sweat. It’s like trying to juggle flaming chainsaws while riding a unicycle on a tightrope. But here’s the thing – in today’s world of multi-core processors, distributed systems, and high-performance applications, understanding concurrency isn’t just a nice-to-have skill; it’s absolutely essential.

Go, with its goroutines and channels, makes concurrency more approachable than most languages. But just because it’s easier doesn’t mean it’s easy. You still need to understand the patterns, the pitfalls, and the best practices to build robust, scalable systems.

That’s what this series is about. We’re going to dive deep into the concurrency patterns that every Go developer should know. Not just the theory – we’ll look at real, working code examples that you can run, modify, and learn from. This blog post is going to also act as the index to the posts, with today starting with the Pipeline Pattern.

What You’ll Learn

This series covers 12 essential concurrency patterns, each with practical examples and detailed explanations. Here’s what’s coming:

1. Pipeline Pattern

The pipeline pattern is like an assembly line for data processing. We’ll explore how to chain operations together, where each stage processes data and passes it to the next. Perfect for ETL operations, data transformation workflows, and stream processing.

2. Fan-out/Fan-in Pattern

This pattern distributes work across multiple workers and then collects the results. Think of it as having multiple chefs working on different parts of a meal, then combining everything at the end. Essential for parallel processing and load balancing.

3. Worker Pools Pattern

Worker pools maintain a fixed number of workers that process jobs from a shared queue. It’s like having a team of specialists ready to handle whatever comes their way. Great for controlling resource usage and handling bursty workloads.

4. Producer-Consumer Pattern

This pattern decouples data production from consumption using a shared buffer. Producers generate data, consumers process it, and the buffer handles the flow between them. Perfect for handling variable production and consumption rates.

5. Supervisor/Restart Pattern

The supervisor pattern monitors workers and automatically restarts them when they fail. It’s like having a manager who ensures critical tasks keep running, no matter what. Essential for building resilient, fault-tolerant systems.

6. Publish-Subscribe (Pub/Sub) Pattern

Pub/Sub decouples message producers from consumers. Publishers send messages to topics, and all subscribers to that topic receive the messages. Perfect for event-driven architectures and broadcasting messages to multiple consumers.

7. Timeouts and Cancellation Pattern

This pattern uses context and channels to control the lifecycle of concurrent operations. It’s about preventing goroutines from running indefinitely and gracefully shutting down operations. Critical for building responsive applications.

8. Rate Limiting Pattern

Rate limiting controls the frequency of operations to prevent resource exhaustion. We’ll explore both fixed-rate and token bucket approaches. Essential for API throttling, resource protection, and preventing DoS attacks.

9. MapReduce Pattern

MapReduce processes large datasets by breaking work into map and reduce phases. It’s like having a team of workers who each process a piece of data, then combining all the results. Perfect for data analytics and batch processing.

10. Singleflight Pattern

Singleflight ensures that only one execution of a function is in-flight for a given key at a time. Duplicate callers wait for the result instead of executing the function again. Great for preventing duplicate expensive operations and API call deduplication.

11. Event Loop Pattern

The event loop processes events from multiple sources in a single thread. It’s like having a central dispatcher that handles all incoming events in a controlled, sequential manner. Perfect for handling multiple event sources efficiently.

12. Resource Pooling Pattern

Resource pooling manages reusable resources like database connections and HTTP clients. Instead of creating and destroying resources frequently, we maintain a pool of ready-to-use resources. Essential for performance optimization and resource management.

Why These Patterns Matter

These patterns aren’t just academic exercises. They’re the building blocks of real-world systems. Whether you’re building:

• Web services that need to handle thousands of concurrent requests
• Data processing pipelines that need to scale with your data
• Real-time applications that need to respond quickly to events
• Distributed systems that need to be fault-tolerant and resilient

Understanding these patterns will help you make better architectural decisions and build more robust, scalable systems.

What’s Included

For each pattern, you’ll get:

• Working code examples that you can run and experiment with
• Detailed explanations of how the pattern works
• Real-world use cases where the pattern is most effective
• Performance considerations and best practices
• Common pitfalls and how to avoid them

Getting Started

All the code examples are available in the concurrency-model-patterns repository. You can run them individually or use the CLI tool to explore different patterns:

go run main.go --pattern pipeline
go run main.go --pattern fan
# ... and so on

Who This Series Is For

This series is for Go developers who want to:

• Deepen their understanding of concurrency patterns
• Build more robust systems that can handle real-world challenges
• Improve their code with proven patterns and best practices
• Prepare for system design interviews where concurrency is often a key topic

Whether you’re a junior developer looking to level up or a senior developer wanting to refresh your knowledge, there’s something here for you. All of this is written in context of elaborating on the series around maintaining fluency in Go (and other languages) in the face of agentic – AKA vibe – programming.

Let’s Get Started

Ready to dive in? Let’s start with the Pipeline Pattern and work our way through each pattern. By the end of this series, you’ll have a solid understanding of the concurrency patterns that power modern, scalable systems. Maybe you’ll conjure up another one to add? Repo is open sourced over here.

Remember, concurrency is like learning to ride a bike – it might feel wobbly at first, but once you get it, you’ll wonder how you ever lived without it.

Happy thrashing coding!