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elasticsearch

elasticsearch

github.com/partme-aiData & databases71874 installsUpdated 3w ago
Repository

About elasticsearch

This skill provides guidance for designing, querying, and operating Elasticsearch indexes and clusters. It is for people working with search systems, with coverage spanning Query DSL, REST API document operations, and cluster maintenance.

Key features

  1. 01Design mappings, analyzers, and index settings
  2. 02Write Query DSL searches and aggregations
  3. 03Index, update, and bulk-load documents
  4. 04Manage shards, replicas, and snapshots
  5. 05Monitor cluster health and tune performance

Use cases

  1. 01Creating a product index and searching it
  2. 02Bulk-loading or updating documents through the REST API
  3. 03Planning shard allocation and snapshot backups
  4. 04Connecting Elasticsearch workflows with Kibana or Logstash

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/partme-ai/full-stack-skills --skill elasticsearch

After installing, type/elasticsearchin Claude Code to confirm it loaded.

Bundled filesLICENSE.txt

Details

From the project README

When to use this skill

Use this skill whenever the user wants to:

  • Design index mappings with analyzers and field types
  • Write search queries (match, term, bool, multi_match, nested, aggregations)
  • Index, update, or bulk-load documents via the REST API
  • Manage clusters (shards, replicas, snapshots, upgrades)
  • Integrate Elasticsearch with Kibana or Logstash (ELK stack)

How to use this skill

Workflow
  1. Design the mapping - Define field types, analyzers, and index settings
  2. Index documents - Use PUT/POST or bulk API
  3. Write queries - Use Query DSL with filters for caching
  4. Monitor and tune - Check cluster health, slow logs, and shard balance
Quick-Start Example: Create Index and Search
// Create index with mapping
PUT /products
{
  "mappings": {
    "properties": {
      "name":        { "type": "text", "analyzer": "standard" },
      "description": { "type": "text" },
      "price":       { "type": "float" },
      "category":    { "type": "keyword" },
      "created_at":  { "type": "date" }
    }
  }
}

// Index a document
POST /products/_doc
{
  "name": "Wireless Mouse",
  "description": "Ergonomic wireless mouse with USB-C receiver",
  "price": 29.99,
  "category": "electronics",
  "created_at": "2025-01-15"
}

// Search with bool query and aggregation
GET /products/_search
{
  "query": {
    "bool": {
      "must": [{ "match": { "description": "wireless" } }],
      "filter": [{ "range": { "price": { "lte": 50 } } }]
    }
  },
  "aggs": {
    "by_category": { "terms": { "field": "category" } }
  }
}

Best Practices

  1. Define explicit mappings - Avoid dynamic mapping in production; set dynamic: strict to catch errors
  2. Use filters for exact matches - Filters are cached and faster than queries for keyword/range conditions
  3. Paginate with search_after - Avoid deep from/size pagination; use search_after for large result sets
  4. Plan shards carefully - Target 20-40 GB per shard; avoid too many small shards
  5. Snapshot regularly - Use snapshot/restore for backups; test restore procedures

Keywords

elasticsearch, search, index, mapping, query DSL, aggregation, 搜索引擎, 全文检索, 聚合, ELK, Kibana, bulk API, cluster

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