concurrency

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: Series Overview | Channel Fundamentals → What are Goroutines? Goroutines are lightweight threads managed by the Go runtime. They’re one of Go’s most powerful features, allowing you to write concurrent programs that can handle thousands of simultaneous operations with minimal overhead. Think of goroutines as extremely efficient workers that can run independently while sharing the same memory space. Unlike traditional threads that typically consume 1-2MB of memory each, goroutines start with just 2KB of stack space and grow as needed....

Go Concurrency Patterns Series: ← Semaphore Pattern | Series Overview What is the Actor Model? The Actor Model is a conceptual model for concurrent computation where “actors” are the fundamental units of computation. Each actor has its own isolated state, processes messages sequentially, and can create other actors, send messages, or change its behavior in response to messages. Key Principles: Isolated State: Each actor maintains its own private state Message Passing: Actors communicate only through asynchronous messages Sequential Processing: Each actor processes one message at a time Location Transparency: Actors can be local or remote Fault Tolerance: Actor failures are isolated and recoverable Real-World Use Cases Distributed Systems: Microservices communication Game Servers: Player state management IoT Systems: Device state and communication Financial Systems: Transaction processing Chat Applications: User session management Workflow Engines: Task orchestration Basic Actor Implementation package main import ( "context" "fmt" "sync" "time" ) // Message represents a message sent to an actor type Message interface{} // Actor represents the basic actor interface type Actor interface { Receive(message Message) Start(ctx context....

Go Concurrency Patterns Series: ← Context Pattern | Series Overview | Rate Limiter → What is the Circuit Breaker Pattern? The Circuit Breaker pattern prevents cascading failures in distributed systems by monitoring for failures and temporarily stopping calls to failing services. Like an electrical circuit breaker, it “trips” when failures exceed a threshold, giving the failing service time to recover. States: Closed: Normal operation, requests pass through Open: Failing fast, requests are rejected immediately Half-Open: Testing if service has recovered Real-World Use Cases Microservices: Prevent cascade failures between services Database Connections: Handle database outages gracefully External APIs: Deal with third-party service failures Payment Processing: Handle payment gateway issues File Systems: Manage disk I/O failures Network Operations: Handle network partitions Basic Circuit Breaker Implementation package main import ( "context" "errors" "fmt" "sync" "time" ) // State represents the circuit breaker state type State int const ( StateClosed State = iota StateOpen StateHalfOpen ) func (s State) String() string { switch s { case StateClosed: return "CLOSED" case StateOpen: return "OPEN" case StateHalfOpen: return "HALF_OPEN" default: return "UNKNOWN" } } // CircuitBreaker implements the circuit breaker pattern type CircuitBreaker struct { mu sync....

Go Concurrency Patterns Series: ← Once Pattern | Series Overview | Circuit Breaker → What is the Context Pattern? The Context pattern uses Go’s context package to carry cancellation signals, deadlines, timeouts, and request-scoped values across API boundaries and between goroutines. It’s essential for building responsive, cancellable operations and managing request lifecycles. Key Features: Cancellation: Signal when operations should stop Timeouts: Automatically cancel after a duration Deadlines: Cancel at a specific time Values: Carry request-scoped data Real-World Use Cases HTTP Servers: Request cancellation and timeouts Database Operations: Query timeouts and cancellation API Calls: External service timeouts Background Jobs: Graceful shutdown Microservices: Request tracing and correlation IDs File Operations: Long-running I/O with cancellation Basic Context Usage package main import ( "context" "fmt" "math/rand" "time" ) // simulateWork simulates a long-running operation func simulateWork(ctx context....

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: ← Worker Pool | Series Overview | WaitGroup Pattern → What are Mutex Patterns? Mutex (mutual exclusion) patterns are essential for protecting shared resources in concurrent programs. Go provides sync.Mutex and sync.RWMutex for controlling access to critical sections, ensuring data consistency and preventing race conditions. Key Types: Mutex: Exclusive access (one goroutine at a time) RWMutex: Reader-writer locks (multiple readers OR one writer) Lock-free: Atomic operations without explicit locks Real-World Use Cases Shared Counters: Statistics, metrics, rate limiting Cache Systems: Thread-safe caching with read/write operations Configuration Management: Safe updates to application config Connection Pools: Managing database/HTTP connection pools Resource Allocation: Tracking and managing limited resources State Machines: Protecting state transitions Basic Mutex Usage package main import ( "fmt" "sync" "time" ) // Counter demonstrates basic mutex usage type Counter struct { mu sync....

Go Concurrency Patterns Series: ← WaitGroup Pattern | Series Overview | Context Pattern → What is the Once Pattern? The Once pattern uses sync.Once to ensure that a piece of code executes exactly once, regardless of how many goroutines call it. This is essential for thread-safe initialization, singleton patterns, and one-time setup operations in concurrent programs. Key Characteristics: Thread-safe: Multiple goroutines can call it safely Exactly once: Code executes only on the first call Blocking: Subsequent calls wait for the first execution to complete No return values: The function passed to Do() cannot return values Real-World Use Cases Singleton Initialization: Create single instances of objects Configuration Loading: Load config files once at startup Database Connections: Initialize connection pools Logger Setup: Configure logging systems Resource Initialization: Set up expensive resources Feature Flags: Initialize feature flag systems Basic Once Usage package main import ( "fmt" "sync" "time" ) var ( instance *Database once sync....