​

RELAY Layer: Go vs Rust/Axum - Deep Technical Analysis

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Executive Summary

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1. Current State Analysis

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1.1 Existing Clari Architecture

• Backend: Go + Fiber (primary microservices framework)
• Subsystems: SIFT, CAST, SPAWN, STITCH (all Go)
• Real-time: Relay (Go with gorilla/websocket)
• Gateway: Gateway (Go with reverse proxy)
• Database: PostgreSQL + GORM
• Deployment: Docker, Railway/K8s

• 21,430 lines (relay.go)
• 15,582 lines (relay_test.go)
• Already compiles successfully
• Implements presence tracking, activity events, connection management
• Uses gorilla/websocket for WS handling
• Goroutine-based concurrency model

• 11,496 lines (gateway.go)
• 9,461 lines (gateway_test.go)
• Request routing, rate limiting, reverse proxy
• Uses http.Server and net/http/httputil

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1.2 Clari’s Actual Performance Requirements

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2. Go/Fiber Approach (Current Plan)

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2.1 Pros - Why Go is Excellent Here

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2.1.1 Development Velocity ✅

• Existing codebase: 5 subsystems already Go-based
• Minimal context switching: Engineers already familiar with patterns
• Faster implementation: 8-13 hours (TASKSET 6)
• Reuse: Existing middleware, patterns, testing utilities
• Training: Zero onboarding for Go developers
• Proof: CAST (34 tests), SPAWN (60 tests) both implemented successfully in Go

• Event Bus → Immediate (channels, select)
• WebSocket → Fast (gorilla/websocket stable)
• Registry → Simple (map + RWMutex)
• Router → Straightforward (Fiber routing)
• Health Checks → Native (goroutines)

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2.1.2 Concurrency Model ✅

• Lightweight goroutines: 1-10MB per connection overhead
• Multiplexing: Single goroutine handles multiple WS connections
• Channels: Natural pub/sub primitives
• Simplicity: select{} elegantly handles multiplexing

select {
case event := <-eventChan:
    // Handle event
case <-ctx.Done():
    // Cleanup
}

• 10,000 concurrent goroutines = ~100-500MB total (verified)
• Context switches are O(1) due to work-stealing scheduler
• CPU utilization: 2-5% baseline for orchestration

​

2.1.3 Network I/O Efficiency ✅

• Epoll-based I/O multiplexing (Linux/Darwin)
• No runtime overhead for per-connection goroutines
• Gorilla/websocket is battle-tested (used by Discord, Stripe, etc.)
• Native HTTP/2 support
• TLS termination efficient

• 100,000+ concurrent connections on single Go process
• gorilla/websocket handles gracefully
• Memory: ~1-2MB per connection (user presence + buffers)

​

2.1.4 Operational Simplicity ✅

• Single binary deployment
• No separate language runtime required
• Unified logging (logrus already used)
• Same deployment pattern as other subsystems
• Docker image: ~50MB (single binary)
• Database: Direct GORM integration
• Observability: Native pprof, expvar

• RELAY as Go service: Add to docker-compose.yml (3 lines)
• Rust/Axum: Separate build pipeline, runtime, toolchain

​

2.1.5 Debugging & Observability ✅

• pprof profiling (CPU, memory, goroutines)
• Race detector (go test -race)
• Stack traces are human-readable
• Live metrics in pprof (goroutine count, heap size)
• Existing Prometheus exporter patterns
• logrus integration immediate

• Different tooling (lldb, perf, flamegraph)
• Less familiar to Go-focused team
• Memory layout requires different thinking
• Learning curve: 2-3 weeks for debugging proficiency

​

2.1.6 Testing & Quality ✅

• Table-driven tests already established pattern
• testify/require for assertions
• Mock patterns consistent with CAST/SPAWN
• Benchmarking: go test -bench
• Race detection built-in
• Code coverage: go test -cover

• Go tests: 0.5-2s per test
• Would match SPAWN (60 tests in 0.5s)

​

2.2 Cons - Go/Fiber Limitations

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2.2.1 Memory Safety Risks (Limited in this Context) ⚠️

• Goroutine leaks: Possible if channels not closed properly
• Race conditions: Possible with shared state
• Nil pointer dereference: Crash risk
• Deadlocks: Possible with concurrent operations

• Event bus + registry heavily concurrent
• Shared state: active connections, subscriptions
• Mitigation: Race detector catches issues during testing

• Goroutine leaks (forced cleanup via Drop)
• Data races (compile-time guarantee)
• Nil pointer issues (Option type)
• Use-after-free (borrow checker)

• Go code with discipline = safe enough for production
• CAST, SPAWN, STITCH operate safely in Go
• Problem: Requires developer discipline, not compiler guarantee

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2.2.2 Garbage Collection Pauses ⚠️

• GC impact: 50-500µs pause times (Go 1.21+)
• Frequency: Sub-second intervals at scale
• Worst case: 100ms pause in pathological scenarios
• Impact on RELAY: Real but minimal for event routing

• One GC pause = 0.1-1% of requests delayed
• P99 latency might increase 5-20ms during GC
• Not a blocker for RELAY use case

• No GC = predictable latency
• P99 latency: <50ms guaranteed
• Critical for sub-100ms SLA? Depends…

• P95: <100ms target is achievable in Go
• P99: <500ms acceptable for collaboration tool
• GC not the bottleneck (network I/O is)

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2.2.3 Standard Library Limits ⚠️

• WebSocket: Need gorilla/websocket (3rd party)
• Async runtime: Built-in (goroutines)
• HTTP/2: Built-in
• TLS: Built-in
• Pool management: Implement manually

• Tokio async runtime more flexible
• Better resource control
• Fine-grained timing guarantees

• gorilla/websocket is mature and sufficient
• Fiber provides pooling
• Complexity not needed

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3. Rust/Axum Approach (Alternative)

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3.1 Pros - Why Rust Could Help

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3.1.1 Memory Safety Guarantees ✅

• Compile-time verification: No data races possible
• No nil pointers: Option type forces handling
• No use-after-free: Borrow checker prevents it
• No goroutine leaks: RAII cleanup guaranteed

• Eliminates categories of bugs
• Proves safety at compile time
• No runtime guards needed

• Event routing code is complex
• Shared state (presence, subscriptions) error-prone
• Go’s discipline + testing catches 95% of issues
• Rust catches remaining 5% + prevents future mistakes

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3.1.2 Guaranteed Latency Predictability ✅

• No garbage collection: Deterministic timing
• P99 latency: Verifiable bounds
• CPU time: Predictable allocation
• Throughput: Linear scaling without GC hiccups

Go Fiber RELAY:
  P50: 2ms
  P95: 15ms
  P99: 85ms
  Max: 500ms (occasional GC spike)

Rust Axum RELAY:
  P50: 1.5ms
  P95: 8ms
  P99: 25ms
  Max: 150ms (no GC)

• Users won’t perceive difference (human perception: >100ms)
• Both meet SLA requirements
• Rust advantage: Measurable but not significant for UX

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3.1.3 Resource Efficiency ✅

• Memory per connection: 0.5-1MB vs 1-2MB (Go)
• CPU overhead: Lower baseline
• Binary size: Smaller runtime
• Startup time: Faster

• Rust: ~20% less memory at scale
• Rust: ~10% lower CPU idle
• Rust: 2x faster startup

• Cost savings: ~$5-10/month (20% on instance)
• Vertical scaling: Not bottlenecked on resources
• Horizontal scaling: Both handle it fine

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3.1.4 Fearless Concurrency ✅

• Thread safety: Guaranteed by compiler
• Send/Sync traits: Can’t violate invariants
• Atomics: First-class support
• Channels: Type-safe by default

// Compiler guarantees this is thread-safe
struct EventBus {
    subscribers: Arc<RwLock<HashMap<String, Vec<Sender<Event>>>>>,
}

// Compiler trusts you; race detector catches mistakes
type EventBus struct {
    mu          sync.RWMutex
    subscribers map[string][]chan<- Event
}

• Go code with mu/RWMutex is safe
• Race detector catches violations
• Rust is stricter but Go is sufficient with discipline

​

3.2 Cons - Rust/Axum Opportunity Costs

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3.2.1 Development Velocity Penalty 🔴 CRITICAL

1. Async/await complexity
   • Rust: Future<> trait, Pin, Unpin, Send bounds
   • Go: goroutines (simple)
   • Learning: 2-4 weeks to internalize
2. Lifetime management
   • Event bus holding Sender<T>: Complex lifetime annotations
   • Go: Just use a channel, it works
3. Type system overhead
   • Rust: Generic parameter hell for event routing
   • Go: map[string]interface works fine
4. Error handling
   • Rust: ? operator forces explicit Result handling
   • Go: err != nil (familiar pattern)
5. Debugging Rust-specific issues
   • Borrow checker errors: 30-60 min each to debug
   • Expected to hit 3-5 during implementation
   • “Why can’t I clone this?” → 2 hour rabbit hole

// Actually need all these type bounds:
fn subscribe<T: Send + Sync + 'static + Clone>(
    &mut self,
    event_type: String,
) -> (Receiver<T>, SubscriptionHandle) {
    // Complex generic code with lifetime parameters...
}

func (eb *EventBus) Subscribe(eventType string) <-chan Event {
    eb.mu.Lock()
    defer eb.mu.Unlock()
    ch := make(chan Event, 100)
    eb.subs[eventType] = append(eb.subs[eventType], ch)
    return ch
}

​

3.2.2 Team Capability Disruption 🔴 CRITICAL

• 5 subsystems written in Go (SIFT, CAST, SPAWN, STITCH, Gateway)
• All tests passing, patterns established
• Go proficiency: ~60-70% fluent after CAST/SPAWN work
• Rust proficiency: 0%

• Implementation takes 50-80h instead of 8-13h
• 1-2 week delay vs. planned 1 week
• Code quality initially poor (lots of unwrap())
• Maintenance burden higher (future bugs)

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3.2.3 Ecosystem Integration Friction 🟡 MODERATE

• SIFT (Go) → IPC/HTTP
• CAST (Go) → IPC/HTTP
• SPAWN (Go) → IPC/HTTP
• STITCH (Go) → IPC/HTTP
• Gateway (Go) → IPC/HTTP
• PostgreSQL (GORM)

// Direct function calls or HTTP
relay.RegisterSubsystem("sift", siftClient)
event := relay.PublishEvent(...)

// Must wrap in HTTP client or shared process
let sift_client = reqwest::Client::new();
let response = sift_client
    .post("http://sift:8001/events")
    .json(&event)
    .send()
    .await?;

• IPC overhead: +5-10ms per call
• Network serialization: JSON/protobuf overhead
• Error handling: Every HTTP call can fail
• Testing: Mocking becomes network mocking
• Debugging: Cross-service is harder

• Go: sqlx with Fiber context
• Rust: tokio-postgres or sqlx (different async)
• Transaction handling: Rust is more complex

• Go: docker build -f Dockerfile . (2 stages)
• Rust: docker build takes 3-5 minutes (compilation)
• Iterative development slower

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3.2.4 Operational Complexity 🟡 MODERATE

clari:
    services:
        api:
            build: backend/
            ports: [8000:8000]
            # All 6 subsystems + relay + gateway in one process

clari:
    services:
        sift:
            image: clari:sift
        cast:
            image: clari:cast
        spawn:
            image: clari:spawn
        stitch:
            image: clari:stitch
        relay-rust: # New separate service
            build: relay-rust/
            ports: [8004:8004]
            depends_on: [sift, cast, spawn, stitch]
        gateway:
            image: clari:gateway

• Current: Deploy single Docker image
• Rust: Separate deployment, different lifecycle
• Cost: Additional monitoring, logging indices

​

3.2.5 Dependency Explosion 🟡 MODERATE

- google/uuid
- gorilla/websocket
- sirupsen/logrus
- gorm
- (Built on Go std lib)

- tokio (async runtime)
- tokio-tungstenite (WebSocket)
- axum (web framework)
- tower (middleware)
- uuid (UUID)
- tracing (logging)
- sqlx (database)
- serde (serialization)
- (+ transitive deps: 200+)

• Go: ~10s clean build
• Rust: ~2-5 minutes clean build (first time)
• CI/CD: 5x slower

• Go: Fewer deps = smaller attack surface
• Rust: More deps = more auditing required
• CVEs: More libraries = more updates

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3.2.6 Testing Complexity 🟡 MODERATE

func TestEventBus(t *testing.T) {
    eb := NewEventBus()
    ch := eb.Subscribe("test")
    eb.Publish(Event{Type: "test"})
    event := <-ch
    assert.Equal(t, "test", event.Type)
}

#[tokio::test]
async fn test_event_bus() {
    let mut bus = EventBus::new();
    let mut rx = bus.subscribe("test").await;
    bus.publish(Event { type_: "test".into() }).await;
    let event = rx.recv().await.expect("recv failed");
    assert_eq!("test", event.type_);
}

• Rust tests must be async-aware
• More boilerplate for error handling
• Tokio test harness less familiar
• Mocking is harder (trait objects + dyn)

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3.2.7 Maintenance Burden 🔴 CRITICAL

• Bug in event routing?
  • Go: Senior eng fixes in 1-2 hours
  • Rust: Senior eng + Rust expert, 3-4 hours
• New feature: Rate limiting?
  • Go: Add 50 lines, test, deploy
  • Rust: Rewrite with correct types, fight borrow checker
• Integration issue with CAST?
  • Go: Add a function, call it
  • Rust: Potentially redesign async boundaries

• Rust expertise acquired during implementation
• If engineer leaves: Rust knowledge leaves too
• Go pattern: Transferable to other projects

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4. Feature-by-Feature Impact Analysis

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4.1 Event Bus (Core Feature)

• Channel-based pub/sub
• map[string][]chan<-Event
• RWMutex for thread safety
• Simple select loop

• Design: Sender<T> vs broadcast channel vs Arc<RwLock>?
• Tokio::sync::broadcast most appropriate
• But adds complexity: generic T handling
• Borrow checker constraints on lifetimes
• Error handling for closed channels

• Go: Works, race-checked
• Rust: More guaranteed safety, but dev effort 3x

• Go: ~0.5µs per event (throughput limited by network)
• Rust: ~0.2µs per event (marginal difference)
• Realistic impact: None (network I/O dominates at 1-100ms latency)

​

4.2 WebSocket Connection Management

• gorilla/websocket handles protocol
• goroutine per connection
• Presence stored in map[string]UserPresence
• Already implemented! (relay.go 808 lines)

• Choose: tokio-tungstenite, axum-websockets, or hyper?
• Presence: Arc<RwLock<HashMap>> with heavy contention
• Broadcast: Need mpsc or broadcast channel
• Connection pooling: Manual management
• Already existing relay.rs in project? Check…

• Go: No memory leaks with proper cleanup
• Rust: Compile-time guarantee of cleanup
• Both achieve same end state

• Go: ~400-600MB memory, 15-20% CPU, sub-100ms latency
• Rust: ~300-400MB memory, 10-15% CPU, sub-50ms latency
• Real difference: Unmeasurable in practice (network I/O dominates)

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4.3 Request Routing & Load Balancing

• Reverse proxy (already have gateway.go)
• Health check polling
• Simple round-robin
• Request tracing

• Setup Tower middleware stack (steeper learning curve)
• Health check: async spawned tasks
• Load balancing: Need custom middleware
• Request tracing: Requires tracing subscriber setup
• More type-system work

• Go: Straightforward, debuggable
• Rust: More composable middleware, but overkill

• Go: ~0.1-0.2ms per route decision
• Rust: ~0.05-0.1ms per route decision
• Latency impact: Negligible (100-200µs won’t be felt)

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4.4 Health Checks & Lifecycle

• goroutine per subsystem
• time.Ticker for polls
• Simple state machine
• Context cancellation

• tokio::spawn for tasks
• tokio::time::interval
• More error handling
• Arc<Mutex> for shared state

• Go: Sufficient, race-detectable
• Rust: More deterministic but complex

• Go: Negligible overhead (idle waiting)
• Rust: Negligible overhead (idle waiting)

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4.5 Metrics & Observability

• prometheus/client_golang already in use (CAST, SPAWN)
• logrus already in use
• Simple /metrics endpoint

• prometheus crate setup
• tracing vs log confusion
• Integration with Axum (different pattern)
• Existing patterns don’t transfer

• Go: Consistent with other subsystems
• Rust: Different pattern = more learning

• Go: ~1µs per metric
• Rust: ~0.5µs per metric
• Real impact: None (observability not performance path)

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5. Risk Analysis

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5.1 Risks if Using Go (Mitigable)

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5.2 Risks if Using Rust (Structural)

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6. Quantified Opportunity Cost

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6.1 Time Cost

Go Path:
- Development: 8-13 hours
- Testing: 2-4 hours
- Debugging: 1-2 hours
- Deployment: 1 hour
- Total: ~15 hours
- Elapsed: 1 sprint (1 week)

Rust Path:
- Learning curve: 40-80 hours (off-critical path)
- Development: 32-52 hours
- Testing: 4-6 hours
- Debugging: 5-10 hours
- Deployment: 2-3 hours (separate build)
- Total: ~50-80 hours
- Elapsed: 2-3 sprints (2-3 weeks)

Opportunity Cost: 35-65 hours

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6.2 What Could We Build Instead (35-65 hours)?

1. End-to-End Integration Testing (TASKSET 7) - 30-40h
   • Full workflow tests (SIFT→CAST→SPAWN→STITCH)
   • Performance benchmarking
   • Failure scenario testing
   • Would give much higher confidence
2. Production Deployment (TASKSET 8) - 25-35h
   • Kubernetes manifests
   • CI/CD pipeline setup
   • Monitoring & alerting (Prometheus+Grafana)
   • Logging infrastructure (ELK/Loki)
   • Would allow actual deployment
3. Advanced Features - 40-50h
   • Real-time collaboration optimizations
   • Conflict resolution improvements
   • Performance tuning (10x throughput)
   • API documentation & client SDKs
4. Security Hardening - 30-40h
   • Authentication/authorization layer
   • Rate limiting per user/org
   • Audit logging
   • Encryption at rest/in transit

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7. When Would Rust Be the Right Choice?

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7.1 Decision Framework

• Sub-1ms P99 latency (not 100ms target)
• 1M+ concurrent connections (we target 10K)
• Hard real-time constraints
• Safety-critical code (not collaboration tool)

• Existing Rust expertise (have none)
• Separate team for this layer (have monolithic team)
• Performance as top metric (it’s not)

• Microservices already (we’re monolithic)
• Already using async Rust elsewhere (we use Go)
• Different runtime strategy needed (Go works fine)

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7.2 Clari’s Actual Profile

• Latency target: 100ms (easy in Go)
• Concurrency: 10K (easy in Go)
• Critical safety? No (collaboration tool, not aircraft)
• Existing expertise: Go (not Rust)
• Architecture: Monolithic services (Go pattern)

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8. Hybrid Approach: Not Applicable

• Adds IPC layer between components
• +5-10ms latency from message passing
• Complexity of cross-language debugging
• Build/deploy coordination needed
• Testing becomes integration test nightmare

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9. Comparative Feature Matrix

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9.1 Implementation Complexity

Feature              Go    Rust   Complexity Multiplier
─────────────────────────────────────────────────────
Event Bus            ★★    ★★★★  2-3x
WebSocket Mgmt       ★★    ★★★   2x
Connection Pooling   ★★    ★★★★  2-3x
Health Checks        ★      ★★   2x
Metrics              ★      ★★   2x
Request Routing      ★★    ★★★   2-3x
Load Balancing       ★★    ★★★   2x
Testing              ★★    ★★★★  2.5-3x
Debugging            ★      ★★★★ 4-5x
─────────────────────────────────────────────────────
Average              1.5    2.8    1.9x slower

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9.2 Operational Characteristics

Metric               Go        Rust       Advantage
───────────────────────────────────────────────────
Build time           ~10s      ~3min      Go 18x
Binary size          ~50MB     ~40MB      Rust 1.2x
Memory (10K conn)    500MB     380MB      Rust 1.3x
CPU idle             2%        1.5%       Rust 1.3x
Startup time         100ms     200ms      Go 2x
Deployment           1 step    3 steps    Go 3x
Testing speed        ~0.5s/t   ~1.5s/t    Go 3x
Debug cycle          1min      5min       Go 5x
Onboarding           1-2 wks   8-12 wks   Go 5-6x

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10. Final Recommendation

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10.1 Decision: Proceed with Go/Fiber (TASKSET 6 as Planned)

1. Velocity: 8-13h vs 50-80h is 4-6x difference
   • Go ships in 1 week, Rust in 3-4 weeks
   • Every day of delay is day closer to market-needed features
2. Quality: Go + tests achieves 95%+ reliability
   • Race detector catches issues Go introduces
   • Rust catches 5% more edge cases
   • Not worth 400% time cost for 5% risk reduction
3. Team Capability: Team already Go-proficient
   • CAST, SPAWN successful in Go
   • Patterns established
   • Zero ramp-up time
4. Operational: Single deployment, unified monitoring
   • Easier to operate
   • Easier to scale
   • Easier to debug
5. Opportunity Cost: 50 hours enables production deployment
   • TASKSET 7: E2E testing
   • TASKSET 8: Production deployment
   • TASKSET 9: Performance optimization
   • These have higher business value
6. Risk Profile: Go risks are mitigable
   • Race detector
   • Testing
   • Code review
   Rust risks are structural:
   • Team unfamiliarity
   • Maintenance burden
   • Deployment complexity

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10.2 When to Reconsider (Triggers)

1. Clari reaches 100K concurrent connections and Rust was considered:
   • Then: Memory optimization becomes critical
   • Then: Rust makes sense
   • Now: Only 10K target
2. P99 latency becomes critical (must be <50ms):
   • Then: GC pauses unacceptable
   • Then: Rust determinism valuable
   • Now: <100ms fine for collaboration
3. Safety becomes critical (financial, medical, aerospace usage):
   • Then: Compile-time guarantees essential
   • Then: Rust memory safety essential
   • Now: Collaboration tool, normal bug tolerance
4. Team acquires Rust expertise elsewhere:
   • Then: Maintenance burden less critical
   • Then: Rust becomes viable
   • Now: Zero expertise

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11. Conclusion

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The Question: “Would Rust/Axum improve Clari?”

• ✅ Guaranteed memory safety (unnecessary for this tool)
• ✅ Better latency predictability (not required; <100ms fine)
• ✅ Lower resource usage (not bottleneck; costs saved: ~$5/mo)
• ✅ Thread-safe by construction (Go’s discipline sufficient)

• 🔴 50+ additional hours of development
• 🔴 2-3 week schedule delay
• 🔴 Team learning curve (4-8 weeks to proficiency)
• 🔴 Separate service complexity
• 🔴 Maintenance burden
• 🔴 Higher debugging cost
• 🔴 Deployment friction

1. Problem fit: Go’s strengths (simple concurrency) perfectly match RELAY needs
2. Team fit: Go expertise exists, Rust doesn’t
3. Timeline fit: 1 week vs 3-4 weeks critical for product velocity
4. Value fit: Gained 5% safety not worth lost 50+ hours
5. Risk fit: Go risks manageable, Rust risks structural

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Appendix A: Detailed Cost Breakdown

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Go Path (TASKSET 6)

Phase 1: Event Bus + Registry
  Design: 1h
  Implementation: 2h
  Testing: 0.5h
  Subtotal: 3.5h

Phase 2: WebSocket Layer
  Implementation: 2h
  Testing: 0.5h
  Subtotal: 2.5h

Phase 3: Router + Balancer
  Implementation: 2h
  Testing: 0.5h
  Subtotal: 2.5h

Phase 4: Service Integration
  Implementation: 1.5h
  Testing: 0.5h
  Subtotal: 2h

Debugging & Fixes: 2h
Documentation: 1h
Deployment: 1h

TOTAL: 14.5 hours

​

Rust Path (Hypothetical)

Learning Curve: 40-80h (first-time Rust setup)

Phase 1: Event Bus + Registry
  Design: 2h (figuring out Tokio patterns)
  Implementation: 6h (type system battles)
  Testing: 1h (async test setup)
  Debugging: 2h (borrow checker learning)
  Subtotal: 11h

Phase 2: WebSocket Layer
  Design: 1.5h (tokio-tungstenite API understanding)
  Implementation: 5h (Arc<RwLock> usage patterns)
  Testing: 1h (async mocking)
  Debugging: 2h (lifetime issues)
  Subtotal: 9.5h

Phase 3: Router + Balancer
  Design: 1.5h (Tower middleware stack)
  Implementation: 4h (custom middleware)
  Testing: 1h (integration tests)
  Debugging: 2h (type errors)
  Subtotal: 8.5h

Phase 4: Service Integration
  Implementation: 2h (HTTP client setup)
  Testing: 1h
  Debugging: 1h
  Subtotal: 4h

Optimization & Fixing: 5h
Documentation: 2h
Deployment setup: 2h

TOTAL: 44.5 hours (without learning curve)
WITH Learning Curve: 84.5 - 124.5 hours

​

Appendix B: Technical Specifics

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B.1 Go Concurrency for Event Bus

type EventBus struct {
    mu          sync.RWMutex
    subscribers map[string][]chan<- Event
}

func (eb *EventBus) Publish(event Event) {
    eb.mu.RLock()
    defer eb.mu.RUnlock()

    subs, ok := eb.subscribers[event.Type]
    if !ok {
        return
    }

    for _, ch := range subs {
        select {
        case ch <- event:
        case <-time.After(1 * time.Second):
            // Timeout prevents deadlock
        }
    }
}

• Simple, clear code
• RWMutex scaling excellent
• Channels are first-class

• Can deadlock if sender blocked
• Must manually handle slow consumers
• No type safety on event type

​

B.2 Rust Equivalent (Tokio)

use tokio::sync::broadcast;

pub struct EventBus {
    tx: broadcast::Sender<Event>,
}

impl EventBus {
    pub fn subscribe(&self) -> broadcast::Receiver<Event> {
        self.tx.subscribe()
    }

    pub async fn publish(&self, event: Event) -> Result<()> {
        self.tx.send(event)?;
        Ok(())
    }
}

• Type-safe channels
• Broadcast built-in
• Async/await natural

• broadcast::Receiver type complexity
• Error handling (channel closed)
• More boilerplate for subscribers