elasticsearch
Elasticsearch search and analytics engine with full-text search. Use for search and logging.
Works with
---
name: elasticsearch
description: Elasticsearch search and analytics engine with full-text search. Use for search and logging.
license: MIT
---
# Elasticsearch
Elasticsearch is a distributed search and analytics engine built on Apache Lucene. It is the heart of the ELK Stack (Elastic, Logstash, Kibana) and a leading Vector Database for AI.
## When to Use
- **Full-Text Search**: "Did you mean?" suggestions, fuzzy search, relevance scoring.
- **Log Analytics**: Storing terabytes of logs (Observability).
- **Vector Search (2025)**: Storing embeddings for Semantic Search / RAG.
## Quick Start
```bash
# REST API - Search for "bike"
GET /products/_search
{
"query": {
"match": {
"description": "bike"
}
}
}
```
## Core Concepts
### Inverted Index
Maps words to documents. "Bike" -> [Doc1, Doc5]. Makes text search nearly instant.
### Shards & Replicas
- **Shard**: A slice of the index. Distributes data across nodes.
- **Replica**: Copy of a shard for High Availability.
### ES|QL (2024+)
Elasticsearch Query Language. A piped language (like SQL/Splunk) to simplify querying.
`FROM logs | WHERE status == 500 | LIMIT 10`
## Best Practices (2025)
**Do**:
- **Use `kNN` Search**: Native vector search support for AI applications.
- **Use ILM (Index Lifecycle Management)**: Move old logs to cheaper cold storage automatically.
- **Use Datastreams**: Optimized abstraction for time-series/logs (append-only).
**Don't**:
- **Don't use as primary source of truth**: It is eventually consistent and partition-tolerant, but relational DBs are safer for "money" data.
- **Don't oversight mapping explosion**: Too many unique fields map crash the cluster.
## References
- [Elasticsearch Guide](https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html)More SEO & Marketing skills
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