Heldinhow Elasticsearch Search
Overview
Elasticsearch provides powerful full-text search with ranking, filtering, and aggregations. This skill should be invoked when building search engines, implementing faceted search, or needing scalable full-text search capabilities.
Core Principles
- Index Design: Plan mappings and analyzers for search effectiveness
- Query DSL: Use Elasticsearch's powerful query language
- Relevance: Leverage scoring for ranked results
- Aggregations: Build faceted search and analytics
Preparation Checklist
- Set up Elasticsearch cluster (local or cloud)
- Plan index structure and mappings
- Choose appropriate analyzers
- Design query patterns
Step-by-Step Process
- Setup: Install and configure Elasticsearch
- Define: Create index with mappings
- Index: Add documents with proper field types
- Query: Build search queries (match, term, bool)
- Aggregate: Implement aggregations for facets
- Optimize: Tune for performance
Do's and Don'ts
- ✅ Do design mappings before indexing
- ✅ Do use appropriate analyzers for text fields
- ✅ Do leverage aggregations for faceted search
- ❌ Don't index everything - only searchable fields
- ❌ Don't skip index refresh intervals
- ❌ Don't ignore shard/replica planning
Anti-Patterns
- No Mapping: Letting Elasticsearch infer types
- Over-Indexing: Indexing unnecessary data
- Missing Aliases: Not using aliases for zero-downtime
- No Backups: Not planning for data recovery