Event-Driven Architecture Code Review: Patterns and Anti-Patterns

Event driven systems let teams ship independently, yet they introduce new failure modes: out-of-order processing, undelivered messages, and schema drift. A strong review process keeps these systems reliable. Use this guide to evaluate event changes before they surprise you in production.

Start with Event Contracts

Treat event schemas like public APIs. Confirm that the producer change keeps payloads backward compatible. Require schema evolution policies (Avro, Protobuf) and a registry entry with version history. If fields are removed, insist on a deprecation window and a migration plan for consumers, similar to our API contract checklist.

Idempotency and Ordering

Error Handling Patterns

Ask the author to document failure pathways:

• What happens when the consumer cannot deserialize the payload?
• Are poison events quarantined with alerts and manual remediation steps?
• Do retries risk causing service storms or propagate partial updates?

Observability Expectations

Event driven systems require deep visibility. During review, confirm:

• Metrics for throughput, lag, dead letter queue size, and consumer error rates are emitted.
• Tracing links producers to consumers via correlation IDs.
• Logs include event version, tenant identifiers, and retry counts.
• Dashboards exist for on-call engineers. Reference the operational ideas in our performance review guide.

Security and Compliance

Events flow through multiple services, increasing exposure. Validate:

• Data classification: PII encrypted at rest and masked in logs.
• Access controls: producers and consumers use constrained IAM roles.
• Audit trails: retention policies meet regulatory requirements.

The Cloud Native Computing Foundation recommends treating event streams as critical assets (CNCF Event Driven Architecture Report).

Testing Strategies

Reviewers should look for:

• Contract tests using mock brokers or schema validation pipelines.
• Replay harnesses that feed historical events into the new consumer to detect regressions.
• Chaos experiments that drop or reorder messages to confirm resilience.
• Staging or shadow topics where new consumers run alongside production before cutover.

Operational Readiness Review

Require a rollout plan that covers:

• How and when to toggle the new consumer or producer behind a feature flag.
• Fallback steps if error rates spike (pause consumers, reroute traffic).
• Update to on-call runbooks with failure scenarios and remediation checklists.
• Communication to dependent teams about new event types or versions.

By applying disciplined reviews to event driven changes you guard against invisible failures and keep your asynchronous architecture trustworthy. Pair these patterns with the automation ROI insights from our ROI calculator to justify the tooling that keeps event pipelines observable and resilient.