Webhooks and Event Processing

Initial Assessment

• Event source (Helius, Dialect, custom listener)?
• Volume and burst expectations?
• Ordering requirements and acceptable lag?
• Downstream actions (alerts, DB writes, bots)?

Core Principles

• Idempotency is mandatory; every event must be safe to replay.
• Separate ingestion from processing with a queue.
• Persist offsets/checkpoints; handle reorgs by slot/signature.
• Apply backpressure; avoid unbounded retries.

Workflow

1. Intake
   • Receive webhook -> verify signature/auth -> enqueue message (include slot, sig, index).
2. Dedupe/idempotency
   • Use composite key (slot+sig+index); store processed marker.
3. Ordering
   • Process by slot then index; allow slight reordering but reconcile with checkpoints.
4. Retries
   • Exponential backoff with DLQ for poison messages; alert on DLQ growth.
5. Backfill + catchup
   • On startup, backfill missing slots; reconcile with queue state.
6. Monitoring
   • Metrics: queue depth, processing latency, failure rate; alerts.

Templates / Playbooks

• Message schema: {slot, signature, index, type, payload, received_at}.
• Dedup key example: slot:signature:index in Redis/DB.
• DLQ policy: max retries 5 -> send to DLQ with reason.

Common Failure Modes + Debugging

• Duplicate webhooks: dedupe with keys.
• Out-of-order slots causing state mismatch: enforce ordering or replay after lag window.
• Burst overload: autoscale workers; drop non-critical events or sample.
• Missing auth verification -> spoofed events; validate signatures.

Quality Bar / Validation

• Idempotency proven by replay test.
• Queue size stable under expected load; DLQ monitored.
• Checkpointing recovers correctly after restart.

Output Format

Examples

• Simple: Low-volume alerts -> webhook to Cloudflare Worker -> queue -> Slack; dedupe by signature.
• Complex: High-volume log stream -> webhook to ingestion service -> Kafka/SQS -> processors updating DB and sending alerts; checkpoints by slot; replay script validated.