Terminal.skills
Skills/elasticsearch-search
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elasticsearch-search

Configure and use Elasticsearch for full-text search, custom analyzers, aggregations, and index management. Use when a user needs to design search mappings, write complex queries, build aggregation pipelines, tune relevance scoring, or optimize index performance for search workloads.

#elasticsearch#search#full-text-search#aggregations#analyzers#mappings
terminal-skillsv1.0.0
Works with:claude-codeopenai-codexgemini-clicursor
Source

Usage

$
✓ Installed elasticsearch-search v1.0.0

Getting Started

  1. Install the skill using the command above
  2. Open your AI coding agent (Claude Code, Codex, Gemini CLI, or Cursor)
  3. Reference the skill in your prompt
  4. The AI will use the skill's capabilities automatically

Example Prompts

  • "Deploy the latest build to the staging environment and run smoke tests"
  • "Check the CI pipeline status and summarize any recent failures"

Documentation

Overview

Design and implement Elasticsearch search solutions including index mappings, custom analyzers, full-text queries, aggregations, and relevance tuning. Covers index lifecycle, search templates, and performance optimization.

Instructions

Task A: Create Index with Custom Mappings

bash
# Create an index with explicit mappings and custom analyzers
curl -X PUT "http://localhost:9200/products" \
  -H "Content-Type: application/json" \
  -d '{
  "settings": {
    "number_of_shards": 3,
    "number_of_replicas": 1,
    "analysis": {
      "analyzer": {
        "product_analyzer": {
          "type": "custom",
          "tokenizer": "standard",
          "filter": ["lowercase", "asciifolding", "product_synonyms", "product_stemmer"]
        },
        "autocomplete_analyzer": {
          "type": "custom",
          "tokenizer": "autocomplete_tokenizer",
          "filter": ["lowercase"]
        }
      },
      "tokenizer": {
        "autocomplete_tokenizer": {
          "type": "edge_ngram",
          "min_gram": 2,
          "max_gram": 15,
          "token_chars": ["letter", "digit"]
        }
      },
      "filter": {
        "product_synonyms": {
          "type": "synonym",
          "synonyms": ["laptop,notebook", "phone,mobile,cellphone", "tv,television"]
        },
        "product_stemmer": {
          "type": "stemmer",
          "language": "english"
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "name": {
        "type": "text",
        "analyzer": "product_analyzer",
        "fields": {
          "autocomplete": { "type": "text", "analyzer": "autocomplete_analyzer", "search_analyzer": "standard" },
          "keyword": { "type": "keyword" }
        }
      },
      "description": { "type": "text", "analyzer": "product_analyzer" },
      "category": { "type": "keyword" },
      "price": { "type": "float" },
      "rating": { "type": "float" },
      "tags": { "type": "keyword" },
      "created_at": { "type": "date" },
      "location": { "type": "geo_point" },
      "in_stock": { "type": "boolean" }
    }
  }
}'

Task B: Full-Text Search Queries

bash
# Multi-match search with boosting
curl -X POST "http://localhost:9200/products/_search" \
  -H "Content-Type: application/json" \
  -d '{
  "query": {
    "bool": {
      "must": {
        "multi_match": {
          "query": "wireless noise cancelling headphones",
          "fields": ["name^3", "description", "tags^2"],
          "type": "best_fields",
          "fuzziness": "AUTO"
        }
      },
      "filter": [
        { "term": { "in_stock": true } },
        { "range": { "price": { "gte": 50, "lte": 300 } } }
      ],
      "should": [
        { "range": { "rating": { "gte": 4.0, "boost": 2.0 } } },
        { "term": { "category": { "value": "electronics", "boost": 1.5 } } }
      ]
    }
  },
  "highlight": {
    "fields": {
      "name": {},
      "description": { "fragment_size": 150, "number_of_fragments": 2 }
    }
  },
  "sort": [
    { "_score": "desc" },
    { "rating": "desc" }
  ],
  "size": 20
}'
bash
# Autocomplete / search-as-you-type query
curl -X POST "http://localhost:9200/products/_search" \
  -H "Content-Type: application/json" \
  -d '{
  "query": {
    "bool": {
      "must": {
        "match": {
          "name.autocomplete": { "query": "wire", "operator": "and" }
        }
      },
      "filter": { "term": { "in_stock": true } }
    }
  },
  "size": 10,
  "_source": ["name", "category", "price"]
}'

Task C: Aggregations

bash
# Multi-level aggregations for faceted search
curl -X POST "http://localhost:9200/products/_search" \
  -H "Content-Type: application/json" \
  -d '{
  "size": 0,
  "query": { "match": { "description": "wireless headphones" } },
  "aggs": {
    "categories": {
      "terms": { "field": "category", "size": 20 },
      "aggs": {
        "avg_price": { "avg": { "field": "price" } },
        "avg_rating": { "avg": { "field": "rating" } }
      }
    },
    "price_ranges": {
      "range": {
        "field": "price",
        "ranges": [
          { "key": "budget", "to": 50 },
          { "key": "mid-range", "from": 50, "to": 150 },
          { "key": "premium", "from": 150, "to": 300 },
          { "key": "luxury", "from": 300 }
        ]
      }
    },
    "price_stats": { "extended_stats": { "field": "price" } },
    "top_rated": {
      "top_hits": {
        "sort": [{ "rating": "desc" }],
        "size": 3,
        "_source": ["name", "rating", "price"]
      }
    }
  }
}'
bash
# Date histogram aggregation for time-series analysis
curl -X POST "http://localhost:9200/logs-*/_search" \
  -H "Content-Type: application/json" \
  -d '{
  "size": 0,
  "query": { "range": { "@timestamp": { "gte": "now-7d" } } },
  "aggs": {
    "errors_over_time": {
      "date_histogram": {
        "field": "@timestamp",
        "calendar_interval": "1h"
      },
      "aggs": {
        "error_count": {
          "filter": { "term": { "level": "error" } }
        },
        "error_rate": {
          "bucket_script": {
            "buckets_path": { "errors": "error_count._count", "total": "_count" },
            "script": "params.total > 0 ? (params.errors / params.total) * 100 : 0"
          }
        }
      }
    }
  }
}'

Task D: Index Lifecycle Management

bash
# Create an ILM policy for log indices
curl -X PUT "http://localhost:9200/_ilm/policy/logs-lifecycle" \
  -H "Content-Type: application/json" \
  -d '{
  "policy": {
    "phases": {
      "hot": {
        "min_age": "0ms",
        "actions": {
          "rollover": { "max_age": "1d", "max_primary_shard_size": "50gb" },
          "set_priority": { "priority": 100 }
        }
      },
      "warm": {
        "min_age": "3d",
        "actions": {
          "shrink": { "number_of_shards": 1 },
          "forcemerge": { "max_num_segments": 1 },
          "set_priority": { "priority": 50 }
        }
      },
      "cold": {
        "min_age": "30d",
        "actions": {
          "searchable_snapshot": { "snapshot_repository": "backups" },
          "set_priority": { "priority": 0 }
        }
      },
      "delete": {
        "min_age": "90d",
        "actions": { "delete": {} }
      }
    }
  }
}'

Task E: Search Templates

bash
# Create a reusable search template
curl -X PUT "http://localhost:9200/_scripts/product-search" \
  -H "Content-Type: application/json" \
  -d '{
  "script": {
    "lang": "mustache",
    "source": {
      "query": {
        "bool": {
          "must": { "multi_match": { "query": "{{query}}", "fields": ["name^3", "description"] } },
          "filter": [
            {{#category}}{ "term": { "category": "{{category}}" } },{{/category}}
            { "term": { "in_stock": true } },
            { "range": { "price": { "gte": "{{min_price}}{{^min_price}}0{{/min_price}}", "lte": "{{max_price}}{{^max_price}}99999{{/max_price}}" } } }
          ]
        }
      },
      "size": "{{size}}{{^size}}20{{/size}}"
    }
  }
}'
bash
# Use the search template
curl -X POST "http://localhost:9200/products/_search/template" \
  -H "Content-Type: application/json" \
  -d '{ "id": "product-search", "params": { "query": "headphones", "category": "electronics", "max_price": 200, "size": 10 } }'

Best Practices

  • Use keyword sub-fields on text fields for exact match filtering and aggregations
  • Set fuzziness: "AUTO" for user-facing search to handle typos gracefully
  • Use filter context for non-scoring clauses (dates, booleans, categories) to leverage caching
  • Design ILM policies to move data through hot/warm/cold tiers based on access patterns
  • Use search templates to keep query logic server-side and simplify client code
  • Monitor slow queries with index.search.slowlog.threshold.query.warn: 2s

Information

Version
1.0.0
Author
terminal-skills
Category
DevOps
License
Apache-2.0

Use Cases

→Centralize Logs with the ELK Stack for a Distributed System