Go Chi v5 Distributed Context Cancellation & Goroutine Leak Quarantine

Q: How does adopting this rule prevent AI hallucinations?

This rule explicitly declares what the AI must avoid (Orphaned background goroutines and decoupled contexts causing silent resource exhaustion and zombie microservice queries.) and prescribes verified positive examples. Grounding the LLM with negative and positive pairs eliminates guesswork.

Q: Which AI coding environments are supported?

This specification is multi-platform compatible: Cursor (.cursor/rules/*.mdc & .cursorrules), Anthropic Claude Code (CLAUDE.md), Windsurf (.windsurfrules), Codex / Cline (AGENTS.md), and GitHub Copilot (.github/copilot-instructions.md).

Q: Can I combine this rule with other technology stacks?

Yes. Use the Interactive Stack Builder or download an Agent Pack to merge this rule with other frontend, backend, database, security, and DevOps rules without syntax collision.

Go Chi v5 Distributed Context Cancellation & Goroutine Leak Quarantine

--- description: Go Chi v5 Distributed Context Cancellation & Goroutine Leak Quarantine - Enforces strict propagation of HTTP request contexts across Chi v5 routers, downstream gRPC calls, and asynchronous worker goroutines. Eliminates goroutine leaks and cascading connection starvation by mandating context-derived channels, timeout inheritance, and structured concurrency patterns like errgroup.Group with derived context boundaries. globs: **/*.go alwaysApply: true verified_by: "agentrules.org" license: "CC-BY-4.0" source: "https://agentrules.org/rules/go-chi-distributed-context-cancellation-leak-quarantine" --- # Go Chi v5 Distributed Context Cancellation & Goroutine Leak Quarantine > **Severity**: CRITICAL | **Category**: backend > **Anti-Pattern Warning**: Orphaned background goroutines and decoupled contexts causing silent resource exhaustion and zombie microservice queries. ## System Constraints You are a Principal Go Architect enforcing strict context lifecycle management and goroutine leak prevention under Go Chi v5. You must verify that r.Context() is never replaced with context.Background() or context.TODO() in HTTP handlers, that all spawned goroutines participate in structured concurrency using errgroup.WithContext or context-aware worker pools, and that outbound HTTP/gRPC multiplexed requests cleanly inherit deadline bounds and handle client disconnects gracefully. ## Strict Guidelines - Engineers MUST ALWAYS derive outbound network contexts directly from r.Context() when initiating downstream HTTP, gRPC, or SQL operations within Chi handlers. - Engineers MUST NOT spawn detached goroutines inside Chi HTTP handlers using `go func(ctx context.Context)` unless wrapped in structured concurrency (e.g., errgroup.WithContext) or passed to a bounded, decoupled queue worker. - Code SHOULD enforce chi middleware.Timeout or custom cascading context timeouts to establish hard deterministic ceilings on fan-out execution trees. - Services MUST monitor and react to ctx.Done() in all blocking channel operations, select statements, and long-running loops to prevent permanent goroutine deadlocks. - Components NEVER use context.Background() inside an active HTTP request boundary to bypass client disconnect signals, unless explicitly designated as an asynchronous detached task via context.WithoutCancel (Go 1.21+). ## Bad vs Good Implementations ### Fan-out Multiplexing in Chi v5 Handler #### Bad Antipattern (AI Common Hallucination) > **Why it fails**: The goroutine sends to an unbuffered channel without monitoring context cancellation, leading to a permanent goroutine leak if the HTTP handler exits early or times out. Replacing r.Context() with context.Background() leaves downstream queries running as zombies. ```go package handlers import ( "context" "encoding/json" "net/http" "time" ) func MultiplexBad(w http.ResponseWriter, r *http.Request) { // BAD: Spawning raw, unbounded goroutines detached from r.Context() results := make(chan string) go func() { // Leaks if client disconnects before downstream completes time.Sleep(2 * time.Second) results <- "downstream-data" }() // BAD: context.Background() discards client cancellation & Chi deadlines ctx := context.Background() select { case res := <-results: _ = json.NewEncoder(w).Encode(map[string]string{"result": res}) case <-ctx.Done(): w.WriteHeader(http.StatusGatewayTimeout) } } ``` #### Production Standard (Correct) > **Standard**: errgroup.WithContext binds all spawned goroutines to the request context lifecycle. Downstream operations strictly abort upon client disconnect or timeout, preventing resource exhaustion and goroutine leaks. ```go package handlers import ( "encoding/json" "net/http" "time" "golang.org/x/sync/errgroup" ) func MultiplexGood(w http.ResponseWriter, r *http.Request) { // GOOD: Inherit Chi request context and set strict downstream ceiling ctx, cancel := context.WithTimeout(r.Context(), 2500*time.Millisecond) defer cancel() g, ctx := errgroup.WithContext(ctx) var downstreamData string g.Go(func() error { // Pass ctx to simulated downstream network call select { case <-time.After(2 * time.Second): downstreamData = "downstream-data" return nil case <-ctx.Done(): return ctx.Err() } }) if err := g.Wait(); err != nil { http.Error(w, http.StatusText(http.StatusGatewayTimeout), http.StatusGatewayTimeout) return } w.Header().Set("Content-Type", "application/json") _ = json.NewEncoder(w).Encode(map[string]string{"result": downstreamData}) } ```

Architectural Specification & Invariant Rationale

Architectural Invariant Design: Go Chi v5 Distributed Context Cancellation & Goroutine Leak Quarantine

Go Chi v5 Distributed Context Cancellation & Goroutine Leak Quarantine defines an industrial-grade behavioral standard for backend engineering. Enforces strict propagation of HTTP request contexts across Chi v5 routers, downstream gRPC calls, and asynchronous worker goroutines. Eliminates goroutine leaks and cascading connection starvation by mandating context-derived channels, timeout inheritance, and structured concurrency patterns like errgroup.Group with derived context boundaries. Modern software architectures require strict adherence to deterministic patterns to guarantee high availability, maintainability, and zero security vulnerabilities.

Framework Paradigm Shift & Drift MechanicsLLMs are trained on millions of open-source repositories containing outdated code, legacy idioms, and unpatched antipatterns. When coding assistants generate implementations for this stack, they default to common statistical tokens: Orphaned background goroutines and decoupled contexts causing silent resource exhaustion and zombie microservice queries. Without an explicit system instruction file, developers spend hours debugging silent failures.

Failure Modes & Production Blast Radius

Hazard Analysis: What Breaks Without This Rule

The following failure modes have been cataloged from hundreds of hours of production code reviews where AI assistants produced non-compliant implementations:

Failure Mode	AI Symptom (The Anti-Pattern)	Severity	Production Impact & Remedy
AI Hallucination & Deprecation Drift	Orphaned background goroutines and decoupled contexts causing silent resource exhaustion and zombie microservice queries.	Critical	Violating this industrial standard introduces regression bugs, security risks, and unexpected runtime failures in production. Enforce explicit behavioral system directives across all AI coding assistants in your repository.
Silent Architecture Inconsistency	AI assistants produce mixed paradigms across files, creating fragmented codebase conventions.	Medium	Increases cognitive load during code reviews and leads to maintenance debt across engineering teams. Pin project conventions in .cursor/rules, CLAUDE.md, and AGENTS.md so all team agents follow identical guardrails.

Architectural Directives in Detail

Step-by-Step Production Directives

Each rule constraint below is injected into the AI agent's system prompt to establish deterministic boundaries:

Engineers MUST ALWAYS derive outbound network contexts directly from r.Context() when initiating downstream HTTP, gRPC, or SQL operations within Chi handlers.

Rationale:AI models frequently generate unconstrained code here because statistical language distributions lack project-specific architectural context. Mandating this directive ensures that generated code adheres to verified industrial production requirements.