Microservices

Introduction Building robust distributed systems requires understanding fundamental patterns that solve common challenges like consensus, fault tolerance, request distribution, and asynchronous communication. This comprehensive guide uses visual diagrams to illustrate how these patterns work, making complex distributed systems concepts easier to understand and implement. We’ll explore: Raft Consensus Algorithm: How distributed systems agree on shared state Circuit Breaker Pattern: Preventing cascading failures in microservices Load Balancing Algorithms: Distributing traffic efficiently across servers Message Queue Patterns: Asynchronous communication strategies Part 1: Raft Consensus Algorithm The Raft consensus algorithm ensures that a cluster of servers maintains a consistent, replicated log even in the face of failures. It’s designed to be more understandable than Paxos while providing the same guarantees. ...

The RPC Evolution: From gRPC to ConnectRPC While gRPC has been the go-to choice for high-performance RPC communication, ConnectRPC is emerging as a compelling alternative that addresses many of gRPC’s pain points while maintaining compatibility with existing ecosystems. What is ConnectRPC? ConnectRPC (formerly known as Connect) is a protocol that provides a simpler, more flexible approach to RPC. It’s designed to be: HTTP/1.1 and HTTP/2 compatible - Works with existing infrastructure Browser-friendly - No need for special proxies gRPC compatible - Can interoperate with gRPC services Developer-friendly - Simpler tooling and better debugging Why Teams Choose ConnectRPC Over gRPC 1. Browser Compatibility Without Proxies gRPC requires special proxies (like grpc-web) to work with browsers because it relies on HTTP/2-specific features. ConnectRPC works directly in browsers using standard fetch APIs. ...

The API Dilemma: REST vs RPC? For years, teams have debated REST vs RPC as if they were mutually exclusive choices. The truth? You need both. Modern applications benefit from a two-tier API strategy that leverages REST for external clients and RPC for internal services. This isn’t about choosing sides-it’s about using the right tool for each job. Understanding the Two Tiers Tier 1: REST for External APIs (The Public Face) Use REST when: ...

Go Concurrency Patterns Series: ← Go Generics Patterns | Series Overview What is Distributed Tracing? Distributed tracing tracks requests as they flow through microservices, providing visibility into performance bottlenecks, service dependencies, and error propagation in distributed systems. Key Concepts: Trace: End-to-end journey of a request across services Span: Single unit of work within a trace Context Propagation: Carrying trace information across boundaries Sampling: Controlling which traces to collect Why OpenTelemetry? OpenTelemetry (OTel) is the industry standard for observability: ...

Go Concurrency Patterns Series: ← Circuit Breaker | Series Overview | Memory Model → What is Context Propagation? Context propagation is the practice of threading context.Context through your application to carry cancellation signals, deadlines, and request-scoped values across API boundaries, goroutines, and service boundaries. This is critical for building observable, responsive distributed systems. Key Capabilities: Distributed Tracing: Propagate trace IDs across services Cancellation Cascades: Cancel entire request trees Deadline Enforcement: Ensure requests complete within time budgets Request-Scoped Values: Carry metadata without polluting function signatures Real-World Use Cases Microservices: Trace requests across multiple services API Gateways: Propagate timeouts and user context Database Layers: Cancel queries when requests are abandoned Message Queues: Propagate processing deadlines HTTP Middleware: Extract and inject trace headers gRPC Services: Automatic context propagation Basic Context Propagation Propagating Through Function Calls package main import ( "context" "fmt" "time" ) // ServiceA calls ServiceB which calls ServiceC // Context propagates through all layers func ServiceA(ctx context.Context, userID string) error { // Add request-scoped value ctx = context.WithValue(ctx, "user_id", userID) ctx = context.WithValue(ctx, "request_id", generateRequestID()) fmt.Printf("[ServiceA] Processing request for user: %s\n", userID) // Propagate context to next service return ServiceB(ctx) } func ServiceB(ctx context.Context) error { // Retrieve values from context userID := ctx.Value("user_id").(string) requestID := ctx.Value("request_id").(string) fmt.Printf("[ServiceB] User: %s, Request: %s\n", userID, requestID) // Add timeout for downstream call ctx, cancel := context.WithTimeout(ctx, 2*time.Second) defer cancel() return ServiceC(ctx) } func ServiceC(ctx context.Context) error { userID := ctx.Value("user_id").(string) requestID := ctx.Value("request_id").(string) fmt.Printf("[ServiceC] Processing for User: %s, Request: %s\n", userID, requestID) // Simulate work select { case <-time.After(1 * time.Second): fmt.Println("[ServiceC] Work completed") return nil case <-ctx.Done(): fmt.Printf("[ServiceC] Cancelled: %v\n", ctx.Err()) return ctx.Err() } } func generateRequestID() string { return fmt.Sprintf("req-%d", time.Now().UnixNano()) } func main() { ctx := context.Background() err := ServiceA(ctx, "user-123") if err != nil { fmt.Printf("Error: %v\n", err) } } Output: ...