Table of Contents
Search Index Issues, Incidents, and Mitigation Strategies #
Indexing & Data Consistency Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Indexing latency | S: E-commerce platform updating product catalog T: Ensure new products appear in search immediately A: Index refresh delay caused 30+ second lag before products appeared R: Lost sales when customers couldn’t find newly advertised products | - Default refresh intervals - Bulk indexing batches - Missing near-real-time updates - Index optimization conflicts - Queue-based indexing | Prioritized Indexing Pattern with selective refresh strategies | - Amazon product availability lag during sales events - Shopify product search inconsistencies - Elasticsearch refresh_interval configuration issues |
| Split-brain syndrome | S: Multi-datacenter search cluster T: Maintain consistent search index across locations A: Network partition created divergent cluster states R: Different search results depending on which datacenter served requests | - Improper discovery settings - Missing quorum configurations - Network reliability issues - Multi-master setups - Aggressive node timeout settings | Consensus Quorum Pattern with proper minimum master nodes | - Elasticsearch split-brain incidents pre-7.0 - Solr cloud split-brain during network events - Documented cluster divergence incidents |
| Replication lag | S: Global content platform with distributed search T: Provide consistent search experience globally A: Cross-region replication delays caused inconsistent results R: Different search results in different regions causing user confusion | - Asynchronous replication - Cross-region network limitations - Large document updates - Missing replication monitoring - Data locality requirements | Replication Monitoring Pattern with adaptive consistency controls | - Elasticsearch cross-cluster replication delays - Solr replication failures during network congestion - Multi-region search consistency challenges |
| Document version conflicts | S: Collaborative document editing platform T: Update search index when multiple users edit simultaneously A: Concurrent updates caused version conflicts, rejecting some changes R: Missing content in search results despite successful edits | - Optimistic concurrency control - Missing version handling - Concurrent update patterns - Fixed retry strategies - Update-time conflict resolution | Versioned Document Pattern with conflict resolution policies | - Elasticsearch version conflict exceptions in logs - Documented concurrent indexing failures - Multi-writer scenarios causing rejected updates |
Performance Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Slow queries | S: Analytics dashboard using search backend T: Provide sub-second query response for dashboards A: Complex queries took 10+ seconds, timing out R: Dashboard became unusable during peak hours | - Unoptimized query DSL - Missing query analysis - Full-text when not needed - Excessive field retrieval - Missing result caching | Query Optimization Pattern with query performance analysis | - Kibana dashboard timeout errors - Elasticsearch query performance degradation - Solr query timeout incidents |
| Index fragmentation | S: Long-running search application T: Maintain consistent search performance over time A: Repeated updates caused severe index fragmentation R: Gradually degrading query performance despite hardware capacity | - Update-heavy workloads - Missing segment merging - Improper merge policies - Frequent small updates - Deletes without optimization | Segment Management Pattern with optimized merge policies | - Solr segment count explosion issues - Elasticsearch merges consuming excessive resources - Index fragmentation causing JVM memory pressure |
| Cache inefficiency | S: Product catalog search T: Optimize query cache hit rates A: Poor cache key design resulted in low hit rates R: High CPU utilization and slow responses despite caching | - Query variable parameters - Missing cache warming - Improper cache sizing - Filter cache misuse - Time-based cache expiry | Query Cache Strategy Pattern with workload-aware cache configuration | - Elasticsearch query cache hit rate problems - Solr filterCache sizing challenges - Cache eviction storms during traffic spikes |
| Shard imbalance | S: Multi-tenant search service T: Distribute load evenly across cluster A: Uneven data distribution caused hot spots on specific nodes R: Some nodes overloaded while others idle, causing latency spikes | - Static shard allocation - Key-based routing - Tenant size disparity - Missing balancing policies - Heterogeneous document sizes | Dynamic Rebalancing Pattern with shard allocation awareness | - Elasticsearch hot shards in production - Allocation imbalance during scaling events - Solr routing and hotspot challenges |
Scaling & Resource Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| JVM memory pressure | S: E-commerce search during Black Friday T: Handle 5x normal query volume A: JVM garbage collection pauses caused search timeouts R: Degraded shopping experience during peak sales period | - Oversized field caching - Large heap configurations - Doc values misuse - Memory-intensive aggregations - Fielddata circuit breaker issues | Memory-Aware Design Pattern with field data limiting and circuit breakers | - Amazon search degradation during Prime Day - Elasticsearch garbage collection tuning challenges - Solr OutOfMemoryError incidents |
| Cluster recovery storms | S: Search cluster after infrastructure maintenance T: Resume normal service after planned restart A: Simultaneous recovery of all shards overwhelmed I/O R: Extended downtime despite successful restart | - All-at-once restart policies - Missing recovery throttling - Full cluster bounce - Aggressive recovery settings - Snapshot scheduling issues | Controlled Recovery Pattern with throttled, prioritized recovery | - Elasticsearch post-restart recovery storms - Slowness after snapshot restoration - Multi-node failure recovery incidents |
| Index size explosion | S: Log analytics platform T: Index machine logs for security analysis A: Unexpected field explosion in unstructured logs R: Storage capacity exhausted, indexing halted | - Dynamic mapping settings - Unstructured data sources - Missing field limits - String vs keyword confusion - Nested document overuse | Schema Control Pattern with explicit mapping and field limits | - Elasticsearch mapping explosion issues - ELK stack sudden growth incidents - Unexpected storage consumption spikes |
| Write throughput bottlenecks | S: IoT platform indexing sensor data T: Index millions of sensor readings per minute A: Write throughput plateaued despite capacity R: Backpressure caused data collection gaps | - Single-threaded primary shard - Aggressive durability settings - Transaction log bottlenecks - CPU-intensive indexing - Indexing thread pool saturation | Write Optimization Pattern with bulk operations and thread pool tuning | - Time-series data indexing challenges - IoT platform scaling difficulties - Bulk indexing throughput ceilings |
Query & Relevance Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Poor search relevance | S: Content website search function T: Return most relevant articles for user queries A: Search results missed obviously relevant content R: User complaints about “broken search” functionality | - Default scoring settings - Missing field boosting - Inappropriate analyzers - Term-centric approach - Insufficient tuning | Relevance Tuning Pattern with domain-aware scoring and testing | - Media site search quality issues - Documentation portal relevance complaints - E-commerce search relevance challenges |
| Query routing failures | S: Multi-tenant search application T: Route queries to appropriate indices/shards A: Query router sent requests to wrong indices R: Users received empty or incorrect results | - Static index routing - Time-based index confusion - Missing routing validation - Alias management issues - Improper wildcards | Alias-Based Routing Pattern with consistent routing abstractions | - Elasticsearch index routing errors - Time-based index selection failures - Multi-tenant query isolation issues |
| Term frequency distortion | S: Technical documentation search T: Find documents with specific technical terms A: Common terms in the domain overwhelmed relevance scoring R: Less relevant but term-heavy documents ranked too high | - Default IDF calculations - Domain-specific stopwords - Term frequency weighting - Missing normalization - Generic text analysis | Domain-Specific Analysis Pattern with custom stopwords and synonyms | - Technical search quality issues - Domain-specific term weighting problems - Specialized content search relevance challenges |
| Query timeout management | S: Analytics dashboard with complex visualizations T: Present insights within interactive timeframe A: Long-running queries blocked resources without results R: Dashboard appeared frozen, requiring restart | - Fixed timeout settings - Missing partial results handling - Client-side timeout gaps - All-or-nothing result fetching - Block until complete pattern | Progressive Query Pattern with early termination and partial results | - Kibana visualization timeout issues - BI tool integration query cancellation problems - Dashboard query stacking during peak loads |
Text Analysis & Linguistic Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Stemming failures | S: Global e-commerce site serving multiple markets T: Provide accurate search across product variations A: Aggressive stemming created false matches R: Irrelevant products appeared in search results | - One-size-fits-all stemming - Aggressive stemming algorithms - Missing stemming exceptions - Inappropriate language detection - Single analyzer for all fields | Multi-field Analysis Pattern with targeted stemming strategies | - E-commerce search quality incidents - Multi-language stemming issues - Documented cases of overstemming |
| Tokenization issues | S: Healthcare search application T: Find medical terms and codes correctly A: Standard tokenizers split medical terms incorrectly R: Failed searches for common medical terminology | - Default tokenizer usage - Special character handling issues - Language-specific assumptions - Missing compound word handling - Generic text processing | Domain-Aware Tokenization Pattern with custom tokenizer chains | - Medical term search failures - Technical jargon search issues - Special character handling in specialized domains |
| Multi-language challenges | S: International content platform T: Provide relevant search across multiple languages A: Single-language configuration favored one language R: Poor search quality for non-primary languages | - Single-language analyzer - Missing language detection - Script/character set issues - Language-specific stopwords - Monolingual synonym expansion | Language Detection Pattern with per-language analysis chains | - Cross-language search relevance issues - CJK language tokenization challenges - Multi-script search problems |
| Synonym handling problems | S: Legal research platform T: Find documents using alternative legal terminology A: Overly aggressive synonym expansion created false positives R: Irrelevant results mixed with relevant ones | - Bidirectional synonyms - Missing context awareness - Too many synonyms - Generic synonym lists - Synonym graph limitations | Contextual Synonym Pattern with directional synonym rules | - Legal search quality issues with terminology - Academic search synonym expansion problems - E-commerce product attribute search confusion |
Schema & Mapping Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Mapping explosion | S: Log analytics platform indexing diverse data T: Accommodate varying log formats A: Dynamic mapping created thousands of fields R: Mapping size exceeded limits, indexing failed | - Dynamic mapping defaults - Unstructured data sources - Missing mapping limits - Schema-free approach - Nested JSON explosion | Explicit Mapping Pattern with strict field limitations | - Elasticsearch mapping explosion errors - Documented cases of mapping limits reached - Log analytics field count problems |
| Field type conflicts | S: Multi-source data integration platform T: Combine data from varied systems into search A: Same field name with different types across sources R: Indexing errors and failed queries | - Type inference inconsistencies - Missing schema governance - Multi-source ingestion - Temporal type changes - String vs numeric confusion | Schema Governance Pattern with strict type enforcement | - Elasticsearch “mapper_parsing_exception” errors - Type conflict errors in production logs - Data integration mapping conflicts |
| Suboptimal field mappings | S: E-commerce platform with filtered navigation T: Provide fast faceted search on product attributes A: Text fields used for attributes requiring exact matching R: Slow filtering performance and incorrect aggregations | - Text vs keyword confusion - Missing field type optimization - Analytics vs search conflicts - Inappropriate normalizers - One-size-fits-all mappings | Purpose-Driven Mapping Pattern with use-case optimized field types | - Faceted search performance issues - Aggregation errors on text fields - Filter performance degradation |
| Schema evolution challenges | S: Long-running application with changing data model T: Update index structure without disruption A: Schema changes required reindexing, causing downtime R: Service interruption during business hours | - Breaking schema changes - Missing zero-downtime strategy - Direct index dependencies - Index-per-type approach - Tight client-schema coupling | Rolling Index Pattern with alias-based abstraction | - Production downtime during reindexing - Index migration failures - Broken client compatibility after updates |
Security & Access Control Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Insufficient access controls | S: Multi-tenant enterprise search platform T: Ensure tenant data isolation A: Improperly configured permissions allowed cross-tenant access R: Data exposure across organizational boundaries | - Coarse-grained permissions - Index-level only security - Missing field-level security - Shared infrastructure - Security afterthought | Layered Security Pattern with document/field-level security | - Elasticsearch data leakage between users - Multi-tenant isolation failures - Document-level security bypass incidents |
| Authentication bypass | S: Internal analytics platform T: Restrict access to authorized personnel A: Default or backup endpoints lacked authentication R: Sensitive data accessible via unprotected paths | - Default configuration weaknesses - Missing auth on all endpoints - Transport vs HTTP security gaps - Monitoring endpoint exposure - Development shortcuts | Defense-in-Depth Pattern with comprehensive perimeter controls | - Public Elasticsearch clusters discovered - Kibana instances without authentication - Solr admin console exposure incidents |
| Search query injection | S: Customer-facing search application T: Allow users to find relevant content A: Malformed queries consumed excessive resources R: Search denial of service from crafted queries | - Raw query string exposure - Missing input validation - Direct DSL exposure - Unbounded query complexity - Missing resource limits | Query Sanitization Pattern with parameterized templates | - Elasticsearch CVE-2015-5377 - Query of death patterns - Resource exhaustion via complex queries |
| Data exfiltration vulnerabilities | S: Public content search service T: Provide search while protecting bulk data A: Script exploitation allowed mass data extraction R: Unauthorized content scraping beyond intended access | - Missing rate limiting - Excessive result pagination - Script injection opportunities - Verbose error messages - Unrestricted scroll APIs | Progressive Access Pattern with rate limiting and pagination controls | - Content scraping incidents - Data harvesting through search APIs - Scroll API misuse for data extraction |
Operational Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Cluster state bloat | S: Large search cluster with many indices T: Maintain responsive cluster management A: Cluster state grew too large for efficient distribution R: Slow operations and node join issues | - Too many indices/shards - Unbounded settings growth - Missing cleanup processes - Transient settings accumulation - Large mapping definitions | Cluster State Management Pattern with state size monitoring and limits | - Elasticsearch red cluster status - Cluster state sync timeouts - Master node overload incidents |
| Snapshot/restore failures | S: Search platform disaster recovery test T: Recover index from backup within SLA A: Snapshot metadata inconsistencies prevented restore R: Failed to meet recovery time objectives | - Snapshot verification gaps - Repository access issues - Incomplete snapshot metadata - Missing restore testing - Snapshot compatibility problems | Verified Backup Pattern with test restoration validation | - Elasticsearch snapshot corruption issues - Failed disaster recovery exercises - Backup repository access problems |
| Rolling update issues | S: Search service during version upgrade T: Upgrade cluster without downtime A: Mixed version incompatibilities caused errors R: Unexpected downtime during planned upgrade | - Protocol incompatibilities - Extended rolling upgrade window - Missing compatibility testing - State format changes - Plugin version dependencies | Compatibility Testing Pattern with staged upgrade verification | - Elasticsearch 5.x to 6.x upgrade issues - Plugin compatibility failures - Mixed-version cluster incidents |
| Index lifecycle management failures | S: Time-series log analytics platform T: Automatically archive and delete old data A: Failed lifecycle transitions left old indices active R: Disk space exhaustion from undeleted data | - Complex lifecycle policies - Missing policy execution monitoring - Error handling gaps - Storage threshold misconfiguration - Policy execution delays | Lifecycle Verification Pattern with transition monitoring and alerting | - ELK stack storage exhaustion incidents - Curator execution failures - ILM stuck indices reports |
Monitoring & Observability Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Monitoring blind spots | S: Search-dependent e-commerce platform T: Detect search quality issues proactively A: Technical metrics looked normal despite relevance degradation R: Revenue impact before problem detected | - System-only monitoring - Missing relevance metrics - Binary health checks - Infrastructure focus - Lack of business metrics | Holistic Monitoring Pattern with business and technical KPIs | - Search quality regressions undetected - Relevance degradations after updates - “Working but useless” search scenarios |
| Insufficient query logging | S: Customer-facing search application T: Understand user search patterns and failures A: Limited query logging prevented search improvement R: Unable to determine why users couldn’t find products | - Missing slow query logging - Binary success/failure focus - Privacy constraints limiting logs - Insufficient context capture - Storage limitations | Search Analytics Pattern with comprehensive query capture | - Zero-results analysis challenges - Query pattern blind spots - Search improvement data gaps |
| Alerting fatigue | S: 24/7 search service operations T: Notify team of actionable issues only A: Excessive low-value alerts caused alert fatigue R: Critical alert missed among noise, extending outage | - Low threshold settings - Missing alert correlation - Static alerting rules - Alert-on-everything approach - Insufficient prioritization | Hierarchical Alerting Pattern with severity-based routing and correlation | - On-call fatigue incidents - Alert storm during partial outages - False positive response burnout |
| Opaque performance bottlenecks | S: Complex enterprise search application T: Identify source of intermittent slowness A: Limited visibility into query execution details R: Extended troubleshooting time to find root cause | - Missing query profiling - Black-box query execution - Insufficient instrumentation - Complex query analysis - Component-specific metrics | Query Profiling Pattern with distributed tracing integration | - Query bottleneck identification challenges - Performance root cause delays - Inter-component issue attribution problems |
Integration & Client Issues #
| Issue | STAR Incident Example | Contributing Patterns | Canonical Solution Pattern | Real-world Incidents |
|---|---|---|---|---|
| Client version compatibility | S: Application using search integration T: Upgrade backend search version A: Client library incompatibilities caused connection failures R: Application downtime after search upgrade | - Tight version coupling - Breaking API changes - Missing compatibility testing - Implicit dependency assumptions - Direct client-index interaction | Client Abstraction Pattern with version compatibility shims | - Java client incompatibilities - Breaking changes between versions - Client-server version mismatch incidents |
| Connection pooling issues | S: Web application with search backend T: Handle traffic spikes efficiently A: Connection pool exhaustion during high load R: Cascading application failures during peak traffic | - Insufficient pool sizing - Missing connection management - Connection leaks - Long-running queries - Default client settings | Resilient Connection Pattern with dynamic pool sizing and circuit breakers | - Connection timeout exceptions - Pool exhaustion during traffic spikes - No route to host errors under load |
| Query building complexity | S: Customer-facing search with advanced features T: Translate UI interactions to effective queries A: Complex query generation created brittle, error-prone code R: Subtle search bugs and hard-to-maintain code | - Direct query DSL exposure - Missing query abstraction - String-based query building - Embedded query logic - Query complexity growth | Query Builder Pattern with domain-specific query interfaces | - Query syntax error incidents - DSL version change impacts - Query generator maintenance challenges |
| Bulk indexing failures | S: Product catalog nightly update T: Refresh entire product database in index A: Partial bulk failures went undetected R: Inconsistent search experience with missing products | - All-or-nothing error handling - Missing partial failure detection - Inadequate bulk response parsing - Transaction size issues - Error recovery gaps | Transactional Indexing Pattern with comprehensive error handling | - Silent indexing failures in production - Partial bulk update issues - Inconsistent index state after batch processes |
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