Heldinhow/elasticsearch-search

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

1. Setup: Install and configure Elasticsearch
2. Define: Create index with mappings
3. Index: Add documents with proper field types
4. Query: Build search queries (match, term, bool)
5. Aggregate: Implement aggregations for facets
6. 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