elasticsearch-best-practices — Claude Code skill: how to install
Provides rules and code patterns for Elasticsearch index mappings, shard planning, and search query optimization directly to your agent.
npx skills add Mindrally/skills --skill elasticsearch-best-practices -a claude-codeWhat elasticsearch-best-practices does
This skill supplies your coding agent with concrete Elasticsearch patterns. It includes ready-to-use templates for explicit mappings, distinguishing full-text text fields from exact-match keyword types. It also shows how to turn off indexing on raw payload objects with enabled: false.
Cluster planning guidelines set clear boundaries. Shards should stay between 20GB and 40GB, with roughly 20 shards per gigabyte of heap. The reference ILM policy demonstrates hot, warm, and delete transitions. Ingesting large datasets without setting refresh_interval to -1 will bottleneck your cluster on indexing overhead.
Query design guidance focuses on search latency. The skill instructs the agent to route non-scoring terms into cached filter blocks rather than must clauses. For bulk operations, it recommends batch sizes between 5MB and 15MB.
What it helps with
- write explicit index mappings separating searchable text from keyword filters
- build an index lifecycle management policy with hot, warm, and delete phases
- convert non-scoring query constraints into cacheable
filtercontexts - tune batch indexing throughput via the Bulk API with requests sized 5–15MB
Example prompts for the agent
- Generate an explicit Elasticsearch mapping for a product catalog with analyzers and keyword fields
- Refactor this bool query to move exact status and date filters into a filter context
- Write an ILM policy that rolls over at 50GB and deletes indices older than 90 days
How to install elasticsearch-best-practices
1Skills CLI (Vercel)
npx skills add Mindrally/skills --skill elasticsearch-best-practices -a claude-code2Manually: put the files in place
~/.claude/skills/elasticsearch-best-practices/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/elasticsearch-best-practices/
1Skills CLI (Vercel)
npx skills add Mindrally/skills --skill elasticsearch-best-practices -a codex2Manually: put the files in place
.agents/skills/elasticsearch-best-practices/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add Mindrally/skills --skill elasticsearch-best-practices -a cursor2Manually: put the files in place
.cursor/skills/elasticsearch-best-practices/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install Mindrally/skills elasticsearch-best-practices2Manually: put the files in place
.github/skills/elasticsearch-best-practices/SKILL.md1Skills CLI (Vercel)
npx skills add Mindrally/skills --skill elasticsearch-best-practices -a gemini-cli2Platform CLI
gemini skills install https://github.com/Mindrally/skills --path elasticsearch-best-practicesManually: put the files in place
.devin/skills/elasticsearch-best-practices/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add Mindrally/skills --skill elasticsearch-best-practices -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about elasticsearch-best-practices
How do I install elasticsearch-best-practices in Claude Code?
Run npx skills add Mindrally/skills --skill elasticsearch-best-practices -a claude-code.
What does elasticsearch-best-practices do?
It provides guidelines for index design, mapping types, shard sizing, and query performance inside the agent workflow.
What shard sizing targets does the skill recommend?
It targets 20GB to 40GB per shard and roughly 20 shards per gigabyte of JVM heap.
Source text
SKILL.md · Apache-2.0
---
name: elasticsearch-best-practices
description: Elasticsearch development best practices for indexing, querying, and search optimization
metadata:
maintainer: Mindrally
source: https://github.com/Mindrally/skills
---
# Elasticsearch Best Practices
## Core Principles
- Design indices and mappings based on query patterns
- Optimize for search performance with proper analysis and indexing
- Use appropriate shard sizing and cluster configuration
- Implement proper security and access control
- Monitor cluster health and optimize queries
## Index Design
### Mapping Best Practices
- Define explicit mappings instead of relying on dynamic mapping
- Use appropriate data types for each field
- Disable indexing for fields you do not search on
- Use keyword type for exact matches, text for full-text search
```json
{
"mappings": {
"properties": {
"product_id": {
"type": "keyword"
},
"name": {
"type": "text",
"analyzer": "standard",
"fields": {
"keyword": {
"type": "keyword",
"ignore_above": 256
}
}
},
"description": {
"type": "text",
"analyzer": "english"
},
"price": {
"type": "scaled_float",
"scaling_factor": 100
},
"category": {
"type": "keyword"
},
"tags": {
"type": "keyword"
},
"created_at": {
"type": "date"
},
"metadata": {
"type": "object",
"enabled": false
},
"location": {
"type": "geo_point"
}
}
}
}
```
### Field Types
- `keyword`: Exact values, filtering, aggregations, sorting
- `text`: Full-text search with analysis
- `date`: Date/time values with format specification
- `numeric types`: long, integer, short, byte, double, float, scaled_float
- `boolean`: True/false values
- `geo_point`: Latitude/longitude pairs
- `nested`: Arrays of objects that need independent querying
### Index Settings
```json
{
"settings": {
"number_of_shards": 3,
"number_of_replicas": 1,
"refresh_interval": "30s",
"analysis": {
"analyzer": {
"custom_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": ["lowercase", "asciifolding", "synonym_filter"]
}
},
"filter": {
"synonym_filter": {
"type": "synonym",
"synonyms": ["laptop, notebook", "phone, mobile, smartphone"]
}
}
}
}
}
```
## Shard Sizing
### Guidelines
- Target 20-40GB per shard
- Aim for ~20 shards per GB of heap
- Avoid oversharding (too many small shards)
- Consider time-based indices for time-series data
```json
{
"settings": {
"number_of_shards": 3,
"number_of_replicas": 1
}
}
```
### Index Lifecycle Management (ILM)
```json
{
"policy": {
"phases": {
"hot": {
"min_age": "0ms",
"actions": {
"rollover": {
"max_size": "50gb",
"max_age": "7d"
}
}
},
"warm": {
"min_age": "30d",
"actions": {
"shrink": {
"number_of_shards": 1
},
"forcemerge": {
"max_num_segments": 1
}
}
},
"delete": {
"min_age": "90d",
"actions": {
"delete": {}
}
}
}
}
}
```
## Query Optimization
### Query Types
#### Match Query (Full-text search)
```json
{
"query": {
"match": {
"description": {
"query": "wireless bluetooth headphones",
"operator": "and",
"fuzziness": "AUTO"
}
}
}
}
```
#### Term Query (Exact match)
```json
{
"query": {
"term": {
"status": "active"
}
}
}
```
#### Bool Query (Combining queries)
```json
{
"query": {
"bool": {
"must": [
{ "match": { "name": "laptop" } }
],
"filter": [
{ "term": { "category": "electronics" } },
{ "range": { "price": { "gte": 500, "lte": 2000 } } }
],
"should": [
{ "term": { "brand": "apple" } }
],
"must_not": [
{ "term": { "status": "discontinued" } }
]
}
}
}
```
### Query Best Practices
- Use `filter` context for non-scoring queries (cacheable)
- Use `must` only when scoring is needed
- Avoid wildcards at the beginning of terms
- Use `keyword` fields for exact matches
- Limit result size with `size` parameter
```json
{
"query": {
"bool": {
"must": {
"multi_match": {
"query": "search terms",
"fields": ["name^3", "description", "tags^2"],
"type": "best_fields"
}
},
"filter": [
{ "term": { "active": true } },
{ "range": { "created_at": { "gte": "now-30d" } } }
]
}
},
"size": 20,
"from": 0,
"_source": ["name", "price", "category"]
}
```
## Aggregations
### Common Aggregation Patterns
```json
{
"size": 0,
"aggs": {
"categories": {
"terms": {
"field": "category",
"size": 10
},
"aggs": {
"avg_price": {
"avg": { "field": "price" }
}
}
},
"price_ranges": {
"range": {
"field": "price",
"ranges": [
{ "to": 100 },
{ "from": 100, "to": 500 },
{ "from": 500 }
]
}
},
"date_histogram": {
"date_histogram": {
"field": "created_at",
"calendar_interval": "month"
}
}
}
}
```
### Aggregation Best Practices
- Use `size: 0` when you only need aggregations
- Set appropriate `shard_size` for terms aggregations
- Use composite aggregations for pagination
- Consider using `aggs` filters to narrow scope
## Indexing Best Practices
### Bulk Indexing
```json
POST _bulk
{ "index": { "_index": "products", "_id": "1" } }
{ "name": "Product 1", "price": 99.99 }
{ "index": { "_index": "products", "_id": "2" } }
{ "name": "Product 2", "price": 149.99 }
```
### Bulk API Guidelines
- Use bulk API for batch operations
- Optimal bulk size: 5-15MB per request
- Monitor for rejected requests (thread pool queue full)
- Disable refresh during bulk indexing for better performance
```json
PUT /products/_settings
{
"refresh_interval": "-1"
}
// After bulk indexing:
PUT /products/_settings
{
"refresh_interval": "1s"
}
POST /products/_refresh
```
### Document Updates
```json
POST /products/_update/1
{
"doc": {
"price": 89.99,
"updated_at": "2024-01-15T10:30:00Z"
}
}
// Update by query
POST /products/_update_by_query
{
"query": {
"term": { "category": "electronics" }
},
"script": {
"source": "ctx._source.on_sale = true"
}
}
```
## Analysis and Tokenization
### Custom Analyzers
```json
{
"settings": {
"analysis": {
"analyzer": {
"product_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"asciifolding",
"english_stop",
"english_stemmer"
]
},
"autocomplete_analyzer": {
"type": "custom",
"tokenizer": "standard",
"filter": [
"lowercase",
"edge_ngram_filter"
]
}
},
"filter": {
"english_stop": {
"type": "stop",
"stopwords": "_english_"
},
"english_stemmer": {
"type": "stemmer",
"language": "english"
},
"edge_ngram_filter": {
"type": "edge_ngram",
"min_gram": 2,
"max_gram": 15
}
}
}
}
}
```
### Test Analyzer
```json
POST /products/_analyze
{
"analyzer": "product_analyzer",
"text": "Wireless Bluetooth Headphones"
}
```
## Search Features
### Autocomplete/Suggestions
```json
{
"mappings": {
"properties": {
"name": {
"type": "text",
"fields": {
"suggest": {
"type": "completion"
}
}
}
}
}
}
// Query suggestions
{
"suggest": {
"product-suggest": {
"prefix": "wire",
"completion": {
"field": "name.suggest",
"size": 5
}
}
}
}
```
### Highlighting
```json
{
"query": {
"match": { "description": "wireless" }
},
"highlight": {
"fields": {
"description": {
"pre_tags": ["<em>"],
"post_tags": ["</em>"],
"fragment_size": 150
}
}
}
}
```
## Performance Optimization
### Query Caching
- Filter queries are cached automatically
- Use `filter` context for frequently repeated conditions
- Monitor cache hit rates
### Search Performance
- Avoid deep pagination (use `search_after` instead)
- Limit `_source` fields returned
- Use `doc_values` for sorting and aggregations
- Pre-sort index for common sort orders
```json
{
"query": { "match_all": {} },
"size": 20,
"search_after": [1705329600000, "product_123"],
"sort": [
{ "created_at": "desc" },
{ "_id": "asc" }
]
}
```
## Monitoring and Maintenance
### Cluster Health
```
GET _cluster/health
GET _cat/indices?v
GET _cat/shards?v
GET _nodes/stats
```
### Index Maintenance
```
POST /products/_forcemerge?max_num_segments=1
POST /products/_cache/clear
POST /products/_refresh
```
### Slow Query Log
```json
PUT /products/_settings
{
"index.search.slowlog.threshold.query.warn": "10s",
"index.search.slowlog.threshold.query.info": "5s",
"index.search.slowlog.threshold.fetch.warn": "1s"
}
```
## Security
### Index-Level Security
```json
PUT _security/role/products_reader
{
"indices": [
{
"names": ["products*"],
"privileges": ["read"]
}
]
}
```
### Field-Level Security
```json
PUT _security/role/limited_access
{
"indices": [
{
"names": ["users"],
"privileges": ["read"],
"field_security": {
"grant": ["name", "email", "created_at"]
}
}
]
}
```
## Aliases and Reindexing
### Index Aliases
```json
POST _aliases
{
"actions": [
{ "add": { "index": "products_v2", "alias": "products" } },
{ "remove": { "index": "products_v1", "alias": "products" } }
]
}
```
### Reindex with Transformation
```json
POST _reindex
{
"source": {
"index": "products_v1"
},
"dest": {
"index": "products_v2"
},
"script": {
"source": "ctx._source.migrated_at = new Date().toString()"
}
}
```
© Mindrally · Apache-2.0 · Mindrally/skills