channels

Go Concurrency Patterns Series: ← Select Statement | Series Overview | Fan-Out/Fan-In → What is the Pipeline Pattern? The Pipeline pattern is a powerful way to structure concurrent data processing by breaking work into stages connected by channels. Each stage runs in its own goroutine, receives data from an input channel, processes it, and sends results to an output channel. This creates a chain of processing stages that can run concurrently, dramatically improving throughput....

Go Concurrency Patterns Series: ← Channel Fundamentals | Series Overview | Pipeline Pattern → What is the Select Statement? The select statement is Go’s powerful tool for handling multiple channel operations simultaneously. It’s like a switch statement, but for channels - it allows a goroutine to wait on multiple communication operations and proceed with whichever one becomes ready first. Think of select as a traffic controller at an intersection, managing multiple lanes of traffic (channels) and allowing the first available lane to proceed....

Go Concurrency Patterns Series: ← Goroutine Basics | Series Overview | Select Statement → What are Channels? Channels are Go’s primary mechanism for communication between goroutines. They embody Go’s concurrency philosophy: “Don’t communicate by sharing memory; share memory by communicating.” Think of channels as typed pipes that allow goroutines to safely pass data back and forth. Channels provide both communication and synchronization, making them incredibly powerful for building concurrent applications....

Go Concurrency Patterns Series: ← Pipeline Pattern | Series Overview | Pub/Sub Pattern → What is the Fan-Out/Fan-In Pattern? The Fan-Out/Fan-In pattern is a powerful concurrency pattern that distributes work across multiple goroutines (fan-out) and then collects the results back into a single channel (fan-in). This pattern is perfect for parallelizing CPU-intensive tasks or I/O operations that can be processed independently. Fan-Out: Distribute work from one source to multiple workers Fan-In: Collect results from multiple workers into a single destination...

Go Concurrency Patterns Series: ← Fan-Out/Fan-In | Series Overview | Request/Response → What is the Pub/Sub Pattern? The Publisher/Subscriber (Pub/Sub) pattern is a messaging pattern where publishers send messages without knowing who will receive them, and subscribers receive messages without knowing who sent them. This creates a loosely coupled system where components can communicate through events without direct dependencies. Key Components: Publisher: Sends messages/events Subscriber: Receives and processes messages/events Message Broker: Routes messages from publishers to subscribers Topics/Channels: Categories for organizing messages Real-World Use Cases Event-Driven Architecture: Microservices communication Real-Time Notifications: User activity feeds, alerts Data Streaming: Log aggregation, metrics collection UI Updates: React to state changes across components Workflow Orchestration: Trigger actions based on events Cache Invalidation: Notify when data changes Basic Pub/Sub Implementation package main import ( "fmt" "sync" "time" ) // Message represents a pub/sub message type Message struct { Topic string Payload interface{} } // Subscriber represents a message handler type Subscriber func(Message) // PubSub is a simple in-memory pub/sub system type PubSub struct { mu sync....

Go Concurrency Patterns Series: ← Pub/Sub Pattern | Series Overview | Worker Pool → What is the Request/Response Pattern? The Request/Response pattern enables synchronous communication between goroutines, where a sender waits for a response from a receiver. This pattern is essential for RPC-style communication, database queries, API calls, and any scenario where you need to get a result back from an operation. Key Components: Request: Contains data and a response channel Response: Contains result data and/or error information Requester: Sends request and waits for response Responder: Processes request and sends response Real-World Use Cases Database Operations: Query execution with results API Gateways: Forwarding requests to microservices Cache Systems: Get/Set operations with confirmation File Operations: Read/Write with status feedback Validation Services: Input validation with results Authentication: Login requests with tokens Basic Request/Response Implementation package main import ( "fmt" "math/rand" "time" ) // Request represents a request with a response channel type Request struct { ID string Data interface{} Response chan Response } // Response represents the response to a request type Response struct { ID string Result interface{} Error error } // Server processes requests type Server struct { requests chan Request quit chan bool } // NewServer creates a new server func NewServer() *Server { return &Server{ requests: make(chan Request), quit: make(chan bool), } } // Start begins processing requests func (s *Server) Start() { go func() { for { select { case req := <-s....

Go Concurrency Patterns Series: ← Request/Response | Series Overview | Mutex Patterns → What is the Worker Pool Pattern? The Worker Pool pattern manages a fixed number of worker goroutines that process jobs from a shared queue. This pattern is essential for controlling resource usage, preventing system overload, and ensuring predictable performance under varying loads. Key Components: Job Queue: Channel containing work to be processed Worker Pool: Fixed number of worker goroutines Result Channel: Optional channel for collecting results Dispatcher: Coordinates job distribution to workers Real-World Use Cases Image Processing: Resize/compress images with limited CPU cores Database Operations: Limit concurrent database connections API Rate Limiting: Control outbound API call rates File Processing: Process files with bounded I/O operations Web Scraping: Limit concurrent HTTP requests Background Jobs: Process queued tasks with resource limits Basic Worker Pool Implementation package main import ( "fmt" "math/rand" "sync" "time" ) // Job represents work to be processed type Job struct { ID int Data interface{} } // Result represents the outcome of processing a job type Result struct { JobID int Output interface{} Error error } // WorkerPool manages a pool of workers type WorkerPool struct { workerCount int jobQueue chan Job resultQueue chan Result quit chan bool wg sync....