Table of Contents
API Gateway Issues, Incidents, and Mitigation Strategies #
Performance & Scalability Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Throughput bottlenecks | S: E-commerce platform during holiday sale T: Process 10x normal API traffic A: API gateway became bottleneck despite backend scaling R: Degraded user experience and lost sales | - Single-instance deployment - Vertical-only scaling - Synchronous processing - Resource contention - Inefficient routing logic | Horizontal Gateway Scaling Pattern with stateless design and load distribution | - Amazon API Gateway throttling during Prime Day - Shopify API limits during Black Friday - Kong Gateway performance incidents |
| Connection pooling exhaustion | S: Microservice platform handling user requests T: Maintain consistent response times under load A: Connection pools to backends exhausted R: Cascading timeouts and service degradation | - Undersized connection pools - Long-lived connections - Missing connection management - Backend slowdowns - Aggressive timeout settings | Adaptive Connection Management Pattern with dynamic pool sizing and circuit breakers | - Netflix API gateway connection limitations - AWS API Gateway 504 errors under load - Azure APIM connection timeout incidents |
| Memory leaks | S: High-volume payment processing gateway T: Process transactions reliably over time A: Memory leaks in custom plugins caused progressive degradation R: Regular restarts required to maintain performance | - Custom plugin issues - Missing garbage collection - Resource cleanup failures - Long-running request handling - Improper caching implementation | Resource Lifecycle Management Pattern with memory profiling and automated scaling | - Kong Gateway memory issues with custom plugins - Zuul memory leak incidents - Spring Cloud Gateway resource exhaustion |
| Latency spikes | S: Financial services API platform T: Maintain consistent low-latency responses A: Periodic JVM garbage collection caused visible spikes R: Intermittent transaction timeouts affecting user experience | - Garbage collection pauses - Synchronous processing - No request buffering - Lack of resource isolation - Monolithic gateway instances | Predictable Latency Pattern with resource isolation and latency budgeting | - Apigee latency spikes during peak loads - Kong Gateway p99 latency degradation - AWS API Gateway cold start latency |
Reliability & Resilience Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Cascading failures | S: Microservice ecosystem during partial outage T: Maintain overall system availability A: Failing service caused gateway thread pool saturation R: Complete system unavailability despite partial backend issue | - Missing circuit breakers - Timeout misconfigurations - Synchronous call chains - Shared resource pools - No bulkhead isolation | Circuit Breaker Pattern with service isolation and graceful degradation | - Netflix API gateway failure cascade incidents - Monzo banking outage from gateway saturation - Heroku API gateway failure amplification |
| Single point of failure | S: Global SaaS platform T: Ensure continuous API availability A: Gateway configuration database failure caused global outage R: Complete platform unavailability despite healthy services | - Centralized configuration store - Single control plane - Tight coupling to management systems - Stateful gateway design - Lack of failover automation | Multi-region Gateway Pattern with decentralized configuration and autonomous operation | - Cloudflare API gateway global outage - Fastly configuration service impact - Akamai global gateway disruption |
| Configuration propagation failures | S: Multi-region API platform T: Deploy security patch to all gateway instances A: Configuration changes failed to propagate to some regions R: Inconsistent security enforcement and regional failures | - Manual configuration processes - Asynchronous config updates - Missing consistency verification - Region-specific settings - Push-based propagation | Consistent Configuration Pattern with versioned configs and atomic propagation | - Azure APIM configuration inconsistencies - Traefik dynamic configuration issues - Amazon API Gateway deployment failures |
| Improper failover | S: Payment processing platform T: Automatically recover from zone failure A: Failover mechanism failed, routing to unavailable instances R: Extended payment processing outage despite redundant capacity | - Inadequate health checking - Single health check type - Aggressive failover thresholds - Incomplete failover testing - Missing cross-region verification | Active Monitoring Failover Pattern with comprehensive health checking | - Payment gateway failover failures - Kong Gateway health check limitations - AWS API Gateway zone failover incidents |
Security Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Authentication bypass | S: Multi-tenant SaaS API platform T: Ensure proper authentication for all API calls A: Inconsistent auth enforcement allowed bypass paths R: Unauthorized access to customer data | - Inconsistent auth configuration - Path-based rule exceptions - Direct endpoint exposure - Multiple auth methods - Gateway bypass opportunities | Defense-in-Depth Authentication Pattern with layered auth enforcement | - Auth0 configuration bypass incidents - API gateway mis-configuration exposures - OAuth bypass vulnerabilities |
| Token validation failures | S: Financial services API T: Validate JWT tokens for secure access A: Improper signature validation allowed forged tokens R: Unauthorized transaction processing and potential fraud | - Missing key rotation - Weak validation algorithms - Inadequate signature checking - Improper key management - Algorithm confusion attacks | Robust Token Validation Pattern with explicit algorithm verification | - JWT validation vulnerabilities (CVE-2018-0114) - API gateway token forgery incidents - Kong Gateway JWT plugin issues |
| Rate limiting bypass | S: Public API service with tiered access T: Enforce usage limits per customer A: Distributed clients circumvented rate limiting R: Service degradation and unfair resource allocation | - IP-based rate limiting - Missing client identification - Per-instance rate limits - Lack of global rate limiting - Token sharing/pooling | Distributed Rate Limiting Pattern with global counter coordination | - API abuse incidents on public APIs - GitHub API rate limit evasion - Redis rate limit counter saturation |
| Insufficient logging | S: Financial compliance-regulated API T: Track all access for audit purposes A: Critical request metadata missing from gateway logs R: Inability to investigate security incidents | - Minimalist logging defaults - Performance-optimized logging - Missing security context - Incomplete request capture - Storage-constrained logging | Comprehensive Audit Logging Pattern with context-rich security logging | - PCI compliance failures from insufficient logs - GDPR violation investigation challenges - Forensic gaps in security incidents |
Traffic Management Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Routing rule conflicts | S: Microservice platform with complex routing T: Implement new routing rules for feature release A: Conflicting rules created unpredictable request routing R: Traffic sent to incorrect services causing errors | - Manual rule management - Missing rule verification - Order-dependent rules - Pattern overlap conflicts - Multiple rule authors | Verified Routing Pattern with automated rule validation and conflict detection | - Istio routing rule conflicts - Kong Gateway route priority issues - Traefik path precedence problems |
| Canary deployment failures | S: E-commerce checkout flow update T: Safely deploy new API version to subset of users A: Traffic splitting logic failed to respect session affinity R: Inconsistent experience with mid-session version switching | - Missing session awareness - Probabilistic routing only - Inadequate canary criteria - Simplistic health checks - Incomplete canary metrics | Contextual Canary Pattern with session affinity and comprehensive metrics | - Progressive delivery failures - Ambassador API Gateway canary issues - A/B testing session consistency problems |
| Quota management issues | S: Multi-tenant API platform with service tiers T: Enforce different quotas for various subscription levels A: Quota counters reset prematurely during counter failure R: Enterprise customers exceeded quotas, impacting system stability | - Local-only quota tracking - Fixed time window implementation - Counter persistence issues - Quota reset timing errors - Missing quota safety mechanisms | Resilient Quota Pattern with durable tracking and degraded operation modes | - API quota leakage incidents - AWS API Gateway quota implementation challenges - Azure APIM quota counter reset problems |
| Load balancing imbalance | S: Kubernetes-based API platform T: Distribute load evenly across service instances A: Round-robin distribution caused hot spots with varied request costs R: Some instances overloaded while others underutilized | - Simplistic load balancing - Request cost blindness - Missing backend feedback - Lack of adaptive balancing - Connection-based (not request-based) balancing | Adaptive Load Balancing Pattern with cost-aware distribution and backend telemetry | - Service mesh load imbalance issues - Kong Gateway backend hotspots - NGINX Plus load balancing distribution skew |
Integration & Interoperability Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Protocol translation errors | S: Legacy system integration project T: Connect SOAP backend to modern REST API A: Gateway protocol translation introduced subtle data errors R: Inconsistent data processing and invalid transactions | - Simplified data mapping - Missing schema validation - Character encoding issues - Default transformation rules - Incomplete protocol understanding | Validated Transformation Pattern with comprehensive schema enforcement | - SOAP-to-REST translation failures - Character encoding corruption in gateways - JSON-XML conversion data loss |
| Backend contract changes | S: Microservice ecosystem continuous deployment T: Maintain gateway compatibility during backend changes A: Undocumented backend API changes broke gateway mappings R: Customer-facing errors after seemingly unrelated update | - Missing API contracts - Independent deployment cycles - Implicit interface assumptions - Inadequate integration testing - Schema evolution issues | API Contract Pattern with formal specifications and compatibility verification | - Breaking changes in Stripe API affecting gateways - Microservice interface drift incidents - Kong Gateway mapping failures after backend changes |
| Timeout misalignment | S: Multi-tier API orchestration T: Maintain consistent timeout behavior A: Gateway timeout shorter than backend operation time R: Gateway returned errors while backend completed successfully | - Inconsistent timeout settings - Missing timeout strategy - No timeout propagation - Backend processing blindness - Fixed timeout values | Coordinated Timeout Pattern with balanced timeout configuration | - Client-perceived errors despite successful processing - AWS Lambda integration timeout issues - Duplicate operations from retry after timeout |
| Content type handling | S: Mobile app API platform T: Support various client content types A: Incorrect content type handling caused parsing failures R: API errors for specific mobile client versions | - Strict content type validation - Missing content negotiation - Hardcoded media types - Transformation assumptions - Charset handling issues | Content Negotiation Pattern with flexible media type handling | - Mobile client/API gateway compatibility issues - Character encoding errors in international deployments - Browser-specific content type handling problems |
Configuration & Operational Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Configuration complexity | S: Large enterprise API program T: Manage gateway config across many services A: Configuration sprawl led to inconsistency and errors R: Misconfigurations causing outages and security gaps | - Manual configuration processes - Separate config files/locations - Environment-specific settings - Multi-team ownership - Limited configuration validation | Configuration as Code Pattern with versioned, validated configurations | - Amazon API Gateway misconfiguration incidents - Kong declarative config management challenges - Apigee configuration drift issues |
| Plugin dependency conflicts | S: API gateway with complex processing chain T: Add new security functionality via plugin A: Plugin interactions caused unexpected behavior R: Transaction processing errors from plugin chain issues | - Plugin interdependencies - Undefined execution order - Shared context modification - Missing isolation - Implicit plugin assumptions | Isolated Plugin Pattern with explicit dependencies and composition | - Kong plugin interaction bugs - NGINX module conflicts - API gateway plugin chain deadlocks |
| Certificate management failures | S: Financial services API platform T: Maintain secure TLS connections A: TLS certificate expired without renewal R: Complete API unavailability for all clients | - Manual certificate processes - Missing expiration monitoring - Certificate sprawl - Siloed responsibility - Inadequate renewal automation | Automated Certificate Pattern with lifecycle management and monitoring | - Major outages from certificate expirations - Let’s Encrypt integration failures - TLS certificate rotation incidents |
| Change management gaps | S: Business-critical API infrastructure T: Implement gateway policy changes safely A: Untested configuration change deployed to production R: Widespread API failures affecting multiple customers | - Direct production changes - Inadequate staged testing - Missing rollback capability - Limited change oversight - Insufficient impact analysis | Multi-phase Deployment Pattern with progressive exposure and automated verification | - Global API outages from configuration changes - Fastly edge configuration incidents - Cloudflare API gateway rule deployment failures |
Observability & Monitoring Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Insufficient request tracing | S: Complex API transaction flow T: Troubleshoot intermittent customer errors A: Limited correlation between gateway and backend services R: Extended MTTR and customer dissatisfaction | - Missing trace propagation - Independent logging systems - Insufficient context capturing - Distributed transaction blindness - Gateway-only visibility | Distributed Tracing Pattern with correlation ID propagation and trace aggregation | - Microservice troubleshooting complexity - Cross-service debugging challenges - B2B integration error resolution delays |
| Misleading health checks | S: Multi-service API platform T: Accurately reflect system health A: Simplistic health checks showed green despite issues R: Delayed incident response due to false health status | - Basic connectivity checks only - Missing functional verification - Point-in-time health checking - Aggregate-only health reporting - Limited health dimensions | Comprehensive Health Model Pattern with multi-level health assessments | - “Green” dashboards during outages - Delayed incident detection - False positive health status incidents |
| Metric collection gaps | S: Business-critical API platform T: Ensure comprehensive performance visibility A: Important performance patterns invisible in monitoring R: Performance degradation detected only after customer impact | - Technical-focused metrics - Missing business metrics - Aggregate-only measurements - Insufficient granularity - System-centric viewpoint | Multi-dimensional Metrics Pattern with business and technical KPIs | - Gradual API performance degradation - Undetected error rate increases - User experience issues despite “normal” metrics |
| Alert fatigue | S: 24/7 API operations team T: Maintain reliable notification of actual issues A: Excessive low-value alerts caused alert fatigue R: Critical alert missed due to noise, extending outage | - Low threshold settings - Missing alert correlation - Alert-on-everything approach - Static alerting rules - Insufficient prioritization | Hierarchical Alerting Pattern with severity-based routing and alert correlation | - On-call fatigue leading to missed incidents - Alert storms during partial outages - False positive response burnout |
Developer Experience Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Poor API documentation | S: Public API platform for developers T: Enable successful API integration A: Outdated and incomplete documentation R: High support volume and integration failures | - Manual documentation processes - Separate docs from implementation - Missing examples - Undefined documentation processes - Delayed documentation updates | API Documentation as Code Pattern with generated, validated documentation | - Developer frustration with inconsistent docs - Stripe API documentation gaps - Auth0 integration challenges from docs |
| Inconsistent error responses | S: Partner API integration program T: Provide actionable error information A: Inconsistent error formats across endpoints R: Partner integration delays and increased support costs | - Service-specific error formats - Missing error standardization - Passed-through backend errors - Inconsistent error detail levels - Varying status code usage | Consistent Error Pattern with standardized formats and problem details | - Mobile client error handling complexity - Integration partner complaints - Error interpretation challenges |
| Throttling transparency | S: SaaS API with usage-based pricing T: Communicate rate limits clearly to clients A: Missing or unclear throttling headers R: Unexpected client errors and negative user experience | - Silent throttling - Missing rate information - Inconsistent limit communication - Ambiguous error messages - Fixed rate limits | Transparent Throttling Pattern with clear limits and proactive notifications | - GitHub API client confusion - Twitter API rate limit complaints - AWS API user frustration with unclear limits |
| Testing challenges | S: Enterprise API development program T: Enable effective client testing A: Limited sandbox environments and test capabilities R: Production issues from inadequate pre-release testing | - Production-only authentication - Missing mock capabilities - Limited test data - Environment differences - Restricted sandbox functionality | Comprehensive Testing Environment Pattern with realistic sandbox and mock capabilities | - Payment API integration test challenges - OAuth API testing difficulties - Third-party API mocking complexity |
Compliance & Governance Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Regulatory compliance gaps | S: Healthcare API handling PHI data T: Maintain HIPAA compliance for all API traffic A: Missing access logging for certain paths R: Compliance audit findings and potential penalties | - Incomplete compliance coverage - Manual compliance verification - Siloed responsibility - Evolving regulatory requirements - Feature-first prioritization | Compliance by Design Pattern with built-in regulatory controls | - GDPR violations from API data handling - PCI DSS compliance failures - HIPAA audit findings in API gateways |
| Data residency violations | S: Multi-national financial services API T: Comply with regional data sovereignty laws A: API gateway logged sensitive data across regions R: Regulatory violations in multiple jurisdictions | - Centralized logging architecture - Missing data classification - Location-unaware processing - Global traffic management - Insufficient geo-fencing | Data Sovereignty Pattern with geo-aware request handling | - EU GDPR cross-border transfer violations - Financial data residency compliance issues - Healthcare data localization failures |
| Audit trail gaps | S: Banking system API audit T: Provide complete transaction history A: Incomplete audit trails for specific transaction types R: Failed compliance audit and remediation requirements | - Inconsistent audit implementation - Selective logging practices - Missing non-repudiation controls - Audit storage limitations - Performance-driven audit reduction | Comprehensive Audit Pattern with guaranteed audit capture | - Financial transaction traceability issues - SOX compliance gaps in API systems - Regulatory findings on incomplete audit trails |
| API governance inconsistency | S: Large enterprise API program T: Ensure consistent API design across teams A: Inconsistent patterns and practices across endpoints R: Poor developer experience and integration challenges | - Team-specific implementations - Missing governance enforcement - Optional standards - Decentralized API ownership - Organic API growth | API Governance Framework Pattern with automated standards enforcement | - Enterprise API inconsistency issues - Versioning strategy inconsistencies - Field naming convention drift across APIs |
Architectural & Design Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Gateway bypass | S: Microservice architecture implementation T: Enforce consistent API policies A: Direct service-to-service calls bypassed gateway R: Security and governance policies circumvented | - Incomplete network controls - Direct service discovery - Mixed traffic patterns - Missing gateway validation - Convenience-driven shortcuts | API Mesh Pattern with comprehensive traffic management | - Security policy enforcement gaps - Inconsistent API behavior - Shadow IT integration bypass |
| Excessive gateway responsibilities | S: API gateway implementation project T: Implement robust API management A: Gateway overloaded with business logic R: Performance bottlenecks and deployment challenges | - “Smart gateway” anti-pattern - Business logic in gateway - Feature creep - Monolithic gateway design - Extended gateway responsibilities | Responsibility Separation Pattern with focused gateway functionality | - Gateway deployment rigidity - Performance bottlenecks - Gateway as integration hub anti-pattern |
| State management issues | S: Distributed API platform T: Scale gateway horizontally for performance A: Stateful design prevented seamless scaling R: Session affinity requirements limiting scalability | - Local state storage - Session-dependent flows - Stateful plugins/extensions - Missing state externalization - Instance-bound processing | Stateless Gateway Pattern with externalized state management | - Sticky session requirements limiting scale - Session-based rate limiting failures - Gateway scaling bottlenecks |
| Orchestration overload | S: API gateway for microservice integration T: Provide unified API from multiple services A: Complex synchronous orchestration created brittle dependencies R: Cascading failures and performance bottlenecks | - Chatty API design - Synchronous composition - Gateway-driven orchestration - Deep call chains - Tight backend coupling | Backend for Frontend Pattern with purpose-built composition services | - Tightly coupled microservice failures - API gateway timeout cascades - Orchestration complexity incidents |
Edge Cases & Advanced Usage Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| WebSocket handling | S: Real-time trading platform T: Support persistent WebSocket connections A: API gateway terminated WebSockets prematurely R: Interrupted trading sessions and data loss | - HTTP-focused gateway design - Connection timeout limits - WebSocket as afterthought - Load balancer limitations - Insufficient connection management | Long-lived Connection Pattern with WebSocket-aware infrastructure | - Trading platform disconnection issues - Chat application stability problems - Real-time data streaming interruptions |
| Large payload handling | S: Media processing API T: Support file upload through API gateway A: Large payloads caused gateway timeouts and failures R: Unreliable media processing and upload failures | - Default size limitations - Synchronous processing models - In-memory request handling - Missing streaming support - Fixed timeout configurations | Streaming Transfer Pattern with chunked processing and bypass options | - File upload API failures - Media processing gateway timeouts - Memory exhaustion from large payloads |
| Binary protocol support | S: IoT device management platform T: Support binary protocols for efficient communication A: Gateway limited to HTTP/JSON caused protocol impedance R: Excessive bandwidth usage and battery drain on devices | - Text-protocol focus - HTTP-only design - Missing protocol translation - Inefficient encoding - Limited content type support | Protocol Adaptation Pattern with native binary protocol support | - IoT platform efficiency problems - Mobile app battery drain issues - Protocol translation overhead challenges |
| Multi-region data consistency | S: Global API platform with regional deployments T: Maintain consistent configuration across regions A: Configuration drift between regions caused inconsistent behavior R: Regional differences in API behavior confusing clients | - Region-specific deployments - Manual synchronization - Independent regional operations - Missing global view - Different regional capabilities | Global Consistency Pattern with version-controlled configuration and synchronization | - Regional API behavior inconsistencies - Security policy application differences - Cross-region user experience variations |
There's no articles to list here yet.