event

name	event
description	Event-driven architecture. SQS, NATS, schema versioning, DLQ, retry policies, idempotency.

Event -- Event-Driven Architecture

Activate When

• User invokes /godmode:event
• User says "event sourcing", "CQRS", "message broker"
• User says "Kafka", "RabbitMQ", "dead letter queue"
• When building loosely coupled async systems

Workflow

Step 1: Event Architecture Assessment

# Detect message broker infrastructure
ls kafka/ docker-compose*.yml 2>/dev/null \
  | head -5
grep -rl "kafkajs\|amqplib\|@aws-sdk/client-sqs" \
  package.json pyproject.toml 2>/dev/null

# Check for event schemas
find . -name "*.avsc" -o -name "*.proto" \
  -o -path "*/events/*" | head -10

EVENT ARCHITECTURE CONTEXT:
Current State: No events | Basic pub/sub | Full CQRS/ES
Throughput: <events per second>
Ordering: None | Per-entity | Global
Retention: <how long to store events>

IF throughput > 10K/s: recommend Kafka
IF ordering per-entity only: Kafka partitions by key
IF need replay: Kafka or NATS JetStream (not RabbitMQ)
IF simple fan-out: SNS/SQS or RabbitMQ

Step 2: Broker Selection

MESSAGE BROKER SELECTION:
| Feature    | Kafka  | RabbitMQ | SQS/SNS | NATS  |
|-----------|--------|----------|---------|-------|
| Throughput| V.High | High     | High    | V.High|
| Latency   | ~5ms   | ~1ms     | ~50ms   | ~0.1ms|
| Ordering  | Per-pt | Per-q    | FIFO opt| Per-sb|
| Replay    | Yes    | No       | No      | Yes*  |

THRESHOLDS:
  Kafka: use when > 10K events/sec or need replay
  RabbitMQ: use when < 10K/sec and need routing
  SQS/SNS: use when AWS-native and < 50K/sec
  NATS: use when need sub-ms latency

Step 3: Event Schema Design

EVENT ENVELOPE (required fields):
  event_id: UUID
  event_type: "order.placed" (past tense)
  event_version: "1.2"
  source: "order-service"
  timestamp: ISO 8601
  correlation_id: UUID (propagated across services)
  data: { ... payload ... }

VERSIONING:
  Backward compatible: new schema reads old data
  Forward compatible: old schema reads new data
  RULE: never modify existing fields, only add
  RULE: new fields must have defaults
  RULE: reserve removed field numbers (protobuf)

Step 4: DLQ & Retry Policies

RETRY POLICY:
| Attempt | Delay      | Action                  |
|---------|-----------|-------------------------|
| 1       | Immediate | Process message         |
| 2       | 1 second  | Retry (transient)       |
| 3       | 5 seconds | Retry (backoff)         |
| 4       | 30 seconds| Retry (extended backoff)|
| 5       | → DLQ     | Move to dead letter     |

DLQ RULES:
  IF DLQ depth > 0: alert team within 5 minutes
  IF DLQ depth > 100: page on-call
  IF message age in DLQ > 24h: escalate to P1
  Every consumer MUST have a DLQ configured

Step 5: Idempotency Patterns

| Pattern           | How It Works         |
|-------------------|---------------------|
| Idempotency key   | Store processed IDs |
| Natural idempotent| Upserts, SET ops    |
| Optimistic locking| Version check       |
| Dedup table       | event_id in DB      |

RULE: Every consumer must be idempotent.
At-least-once delivery means duplicates WILL occur.

Step 6: Event Sourcing (if needed)

Store state as immutable event sequence. Rebuild aggregate state by replaying events. Use snapshots every 100 events for performance.

Step 7: CQRS (if needed)

Separate write model (commands → event store) from read model (projections → query-optimized DB). Projection lag target: < 500ms for user-facing reads.

Step 8: Validation

EVENT ARCHITECTURE VALIDATION:
| Check                              | Status |
|------------------------------------|--------|
| Event envelope follows standard    | ?      |
| All events have correlation IDs    | ?      |
| Schema versioning strategy defined | ?      |
| DLQ on all consumers               | ?      |
| Idempotent consumer verified       | ?      |
| Retry with exponential backoff     | ?      |

Commit: "event: <system> -- <N> event types, <broker>, <pattern>"

Key Behaviors

Never ask to continue. Loop autonomously until done.

1. Events are facts, not commands. Past tense: "OrderPlaced", not "PlaceOrder".
2. Events are immutable. Publish corrective events.
3. Schema evolution is mandatory. Backward and forward compatibility from day one.
4. Every consumer is idempotent.

Quality Targets

• Publish-to-consume: <100ms latency
• Event delivery: >99% success rate
• Max payload: <1MB per event

HARD RULES

1. Never use events as remote procedure calls.
2. Never mutate historical events.
3. Never deploy consumers without idempotency.
4. Never skip the dead letter queue.
5. Never publish without schema registry check.

Auto-Detection

1. Broker: kafka, rabbitmq, SQS/SNS, NATS configs
2. Schemas: *.avsc, *.proto, events/ directory
3. DLQ: dead-letter config, maxReceiveCount
4. Event sourcing: event_store table, Axon framework

Loop Protocol

FOR each event domain:
  1. Design schema with envelope standard
  2. Register in schema registry
  3. Implement producer + consumer
  4. Configure DLQ + retry policy
  5. Verify idempotency with duplicate test
  6. IF schema breaks compat: add field, don't modify
  7. IF DLQ growing: check handler, fix root cause

Output Format

Print: Event: {pattern}, {broker}, {N} event types, DLQ: {configured}. Verdict: {verdict}.

TSV Logging

timestamp	broker	event_types	dlq_configured	idempotency	status

Keep/Discard Discipline

KEEP if: schema registered AND DLQ configured
  AND idempotent consumer verified
DISCARD if: schema breaks compat OR no DLQ
  OR consumer not idempotent

Stop Conditions

STOP when ANY of:
  - All events have schemas with compat checks
  - DLQ on every consumer with backoff retry
  - Idempotency verified for all consumers
  - User requests stop

Error Recovery

• Schema compat fails: add fields with defaults only.
• Consumer lag growing: scale instances, add partitions.
• DLQ growing: check handler, fix deserialization.
• Ordering broken: verify partition key strategy.