The Integration Reliability Checklist

Integration failures are not just technical annoyances - they cost revenue, damage trust, and create operational chaos. After building dozens of production integrations, we have learned that reliability comes from following a systematic checklist of patterns. Here is what every reliable integration must have.

1. Idempotency Keys

Why it matters: Network requests can fail and retry. Without idempotency, a single customer order could create three invoices in your ERP, or charge a credit card twice.

How to implement: Generate a unique identifier for each operation before making the API call. Pass this as an idempotency key header or in the request body. Store processed keys in your database with a TTL (24-48 hours is common). On retry, check if the key was already processed before executing the operation again.

Real scenario: A Shopify order webhook arrives. Your system processes it and calls your fulfillment API, which times out. Shopify retries the webhook. Without idempotency keys, you would create a duplicate fulfillment request. With them, you detect the retry and return the original response.

2. Retries with Exponential Backoff

Why it matters: External APIs have transient failures: temporary network issues, rate limit resets, brief downtime. Immediate retries often hit the same problem. Too many retries too fast can make things worse.

How to implement: Retry failed requests with increasing delays: 1 second, 2 seconds, 4 seconds, 8 seconds. Add jitter (random variance) to prevent thundering herd problems. Set a maximum retry count (typically 3-5 attempts). Only retry on retriable errors (5xx, timeouts, network errors) - not on 4xx client errors.

Real scenario: Your integration calls Shopify Admin API and hits a rate limit (429 response). With exponential backoff, your first retry waits 2 seconds - by which time the rate limit window has reset. Without it, you would waste retries hitting the same rate limit.

3. Dead-Letter Queues

Why it matters: Some failures are not transient. Invalid data, API schema changes, or business logic errors mean retries will never succeed. You need visibility into these permanent failures.

How to implement: After exhausting retries, move failed events to a dead-letter queue (DLQ). Include the original event payload, error details, timestamps, and retry history. Alert your team when DLQ depth exceeds a threshold. Build tooling to inspect DLQ items and replay them after fixing root causes.

Real scenario: A webhook arrives with a product ID that does not exist in your system. Retries will never fix this - it is a data consistency issue. The DLQ captures it, alerts you, and you investigate: turns out a product was deleted without cleaning up downstream references.

4. Reconciliation Jobs

Why it matters: Webhooks can be missed. Networks fail. Systems have eventual consistency windows. Without periodic reconciliation, small drifts compound into major data discrepancies.

How to implement: Schedule jobs (hourly, daily, or based on data volume) that compare system states. For each entity, compute a checksum or compare critical fields. Flag differences that exceed tolerance thresholds. Queue corrections to the target system, respecting rate limits and idempotency.

Real scenario: Shopify inventory updates via webhooks. One webhook was dropped during a network outage. Without reconciliation, your inventory shows 10 units while Shopify shows 8, leading to oversells. A daily reconciliation job detects the drift and corrects it before customers notice.

5. Comprehensive Monitoring

Why it matters: You cannot fix what you cannot see. Integration failures are often silent until they cause customer-facing issues. Monitoring must be proactive, not reactive.

How to implement: Track key metrics: webhook processing latency, API error rates, DLQ depth, reconciliation drift counts. Set alerts on anomalies: sudden spike in failures, processing lag exceeding SLA, repeated errors for the same entity. Log structured data (JSON) with correlation IDs for easy debugging.

Real scenario: Your Shopify webhook processor latency suddenly jumps from 200ms to 5 seconds. Your alert fires. You investigate and discover a database query is slow due to missing index. You fix it before webhook processing falls behind and starts dropping events.

6. Backfill Strategy

Why it matters: You will need to backfill data - after fixing bugs, onboarding new features, or recovering from outages. Naive backfills can overwhelm APIs, create race conditions, or duplicate data.

How to implement: Design backfill jobs as separate processes from real-time sync. Use batch processing with rate limiting to respect API quotas. Include a dry-run mode that shows what would change without applying it. Make backfills idempotent - use the same idempotency patterns as real-time sync. Include rollback mechanisms for when backfills introduce errors.

Real scenario: You discover a bug where product metafields were not syncing. You need to backfill 10,000 products. Your backfill job processes 100 products per minute, logs progress, and can resume if interrupted. You run it in dry-run mode first to verify the changes. The backfill completes cleanly with zero duplicate operations.

7. Rate Limit Handling

Why it matters: External APIs have rate limits. Exceeding them causes request failures and can trigger throttling that affects all your operations.

How to implement: Track rate limit headers returned by APIs (X-RateLimit-Remaining, Retry-After). Implement client-side rate limiting to stay under thresholds. Use token bucket or leaky bucket algorithms. When approaching limits, queue requests for later processing. Monitor rate limit consumption and alert when consistently hitting limits - it may indicate you need to request higher quotas.

Real scenario: During a product catalog sync, you make 2,000 API calls in one minute, hitting Shopify rate limit. Your client detects this via response headers and pauses requests for 30 seconds. Sync completes successfully without triggering API throttling.

Putting It Together

Reliable integrations are not magic - they are the result of applying these patterns consistently. Start with the basics: idempotency and retries. Add dead-letter queues for visibility. Implement reconciliation to catch drift. Monitor everything. When you follow this checklist, your integrations become predictable and debuggable instead of fragile and mysterious.

Want help building reliable integrations? We have built these patterns into dozens of production systems. Get our 7-day readiness audit to identify reliability gaps in your current integrations.