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Elasticsearch Index Design

ElasticOfficialCreated by elastic

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  • Data and AI
  • Apache-2.0
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From the author

Design and review Elasticsearch index mappings for stated access patterns: correct field types, text+keyword multi-fields, doc_values tuning, mapping-explosion avoidance, and explicit shard settings. Use when creating a new index, reviewing a mapping for storage or query performance, fixing wrong field types, or when the user asks which type to use for search, filter, sort, or aggregation on a field.
— the description by elastic

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured process for designing and reviewing Elasticsearch index mappings. It emphasizes aligning field types with actual access patterns—such as full-text search, filtering, sorting, or aggregation—to optimize storage and query performance. The skill guides users through defining explicit mappings, implementing multi-field patterns, and applying storage optimizations like doc_values tuning and flattened types. It also provides a clear path for remediating existing indices by creating new, optimized mappings and performing a reindex operation, as Elasticsearch does not support in-place type changes.

What it does

  • Analyzes field usage patterns to determine optimal Elasticsearch data types.
  • Provides templates for explicit index mappings to avoid dynamic mapping pitfalls.
  • Identifies and mitigates risks like mapping explosion and storage bloat.
  • Recommends storage optimizations such as disabling doc_values for retrieve-only fields.
  • Outlines the procedure for migrating data to a new index when mapping changes are required.
  • Maps high-level design requirements to specific Elasticsearch CLI operations.

When to use it

  • When creating a new Elasticsearch index and defining its initial schema.
  • When reviewing an existing index to improve query performance or reduce storage costs.
  • When encountering mapping explosion or performance issues due to incorrect field types.
  • When you need to change a field type on a live index that requires a reindex operation.
  • When you are unsure whether to use text, keyword, or other types for specific search or aggregation needs.

Advantages

  • Prevents common performance anti-patterns like fielddata usage or mapping explosion.
  • Reduces disk storage footprint through targeted type selection and doc_values tuning.
  • Ensures search relevance and aggregation accuracy by using correct field types.
  • Provides a safe, repeatable workflow for updating production index schemas.
  • Promotes the use of explicit mappings over dynamic ones for production stability.

Usage examples

“I have a product index. Users search by name, but I also need to sort and aggregate by name. How should I map this?”
The agent will recommend using a text field with a keyword multi-field, providing the JSON structure to map 'name' as text and 'name.keyword' as keyword.
“My index is growing too large and I have a field for labels that contains many different keys. How do I fix this?”
The agent will suggest replacing the dynamic object mapping for 'labels' with the 'flattened' type to prevent mapping explosion.
“I need to change a field from text to keyword to fix my aggregations. How do I do this?”
The agent will explain that in-place changes are not possible, then guide you through creating a new index with the correct mapping and using the reindex command.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/elastic/elasticsearch-index-design/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip elasticsearch-index-design.zip -d .agents/skills

Documentation by elastic

Elasticsearch Index Design

Design explicit index mappings from access patterns, review existing mappings for type and storage mistakes, and apply corrections through a new index plus reindex when field types must change.

<!-- begin-partial: preamble -->

Environment Configuration

This skill executes Elasticsearch operations through the elastic CLI. If the elastic CLI is not installed, tell the user what it is needed for. Do not guess credentials, call the HTTP API directly, or attempt other workarounds.

This skill references operations in HTTP-shorthand form (e.g., GET /, GET /_cat/indices, GET /{index}/_mapping, GET /{index}/_settings/index.mode, POST /_query). The Operations table at the end of this document maps each shorthand to the equivalent elastic CLI command — always use the CLI rather than calling the HTTP API directly.

<!-- end-partial: preamble -->

Process

  1. Gather access patterns per field. Before choosing types, list how each field is used. For every field capture:

    • Search — full-text match, phrase, relevance scoring?

    • Filter — exact term, terms set, prefix?

    • Aggregate — terms, cardinality, histogram, stats?

    • Sort — ascending/d descending in result sets?

    • Retrieve only — returned in _source but never queried?

    The decision: classify each field into one primary access pattern (search, exact, numeric metric, date, boolean, structured object, or retrieve-only). Missing access-pattern data is a blocker — ask the user rather than guessing. Call GET / to confirm connectivity; when reviewing an existing index, call GET /{index}/_mapping to ground the discussion in the current mapping.

  2. Choose field types from access patterns. Map each field to the minimal type set that satisfies its pattern. Read Field Type Decisions and Multi-Field Patterns before proposing mappings.

    Key judgments:

    PatternMapping
    Full-text search onlytext (no keyword sub-field)
    Filter / agg / sort onlykeyword (not text)
    Full-text search and sort or aggregationtext with fields.keyword multi-field
    Decimal price or metricdouble, float, or scaled_float — not text or integer
    Timestampdate
    True/false flagboolean
    Free-form key/value map with many distinct keysflattened — not dynamic object

    Multi-field rule: When a field must be searchable and sortable/aggregatable (e.g. product name), map it as text with a keyword sub-field — search on name, sort and aggregate on name.keyword. Mapping as only text or only keyword is wrong for that combined pattern.

    Explicit mapping rule: For new indices, always define mappings explicitly with PUT /{index}. Do not rely on dynamic mapping for production indices — the first document can lock in wrong types (strings as text, ambiguous numbers as keyword).

    Index settings: Set deliberate number_of_shards and number_of_replicas in the same PUT /{index} request when the deployment allows it (Self-Managed / Elastic Cloud Hosted). On Serverless, omit shard and replica counts (Elastic manages them); still supply explicit mappings. State chosen values or document that defaults apply.

    Example — products index optimized for search plus sort/agg on name:

    {
      "settings": {
        "number_of_shards": 1,
        "number_of_replicas": 1
      },
      "mappings": {
        "properties": {
          "name": {
            "type": "text",
            "fields": {
              "keyword": { "type": "keyword", "ignore_above": 256 }
            }
          },
          "price": { "type": "double" },
          "created": { "type": "date" },
          "in_stock": { "type": "boolean" }
        }
      }
    }

    Create with PUT /products passing the settings and mappings blocks. Verify with GET /products/_mapping.

  3. Guard against mapping explosion and storage bloat. On high-volume indices, type mistakes multiply cost. Read Mapping Explosion and Storage Bloat and apply these review checks:

    • Analyzed-but-not-searched fields — Fields used only for filter and aggregation (url, HTTP status_code, tags, IDs) must be keyword, not text. text wastes space; aggregations on text require fielddata or a .keyword sub-field that should not exist if the field is not searched.

    • message.keyword without ignore_above — A keyword sub-field on a large full-text body indexes the entire raw string as one term. Flag this anti-pattern; remove the sub-field when only full-text search is needed, or add ignore_above when a bounded exact-match sub-field is truly required.

    • Dynamic free-form objects — object with "dynamic": true on user-supplied key/value data with thousands of distinct keys causes mapping explosion. Recommend flattened (or strict dynamic / allowlist strategy).

    • doc_values: false — On fields retrieved in hits but never sorted, aggregated, or filtered (e.g. display-only session_id), set "doc_values": false on keyword to save disk at scale.

    • scaled_float — For metrics with bounded precision (e.g. response_time_ms), prefer scaled_float with an appropriate scaling_factor over plain float/double when storage dominates.

    Prefer "dynamic": "strict" on the root mapping unless unknown fields are an explicit requirement.

  4. Apply design: create new index and reindex when types change. Elasticsearch cannot change an existing field's type in place. When review finds wrong types (text→keyword, object→flattened, float→scaled_float, doc_values changes on existing fields), state clearly that fixes require a new index and reindex — not a mapping update on the live index.

    Workflow for correcting an existing high-volume index such as events:

    1. Design the corrected mapping on a new index name (e.g. events-v2) incorporating all fixes from steps 2–3.

    2. Create the destination with PUT /events-v2 and the full corrected mappings (and settings where applicable).

    3. Copy documents with POST /_reindex — for large indices use wait_for_completion=false and track the task. Source: { "index": "events" }, destination: { "index": "events-v2" }.

    4. Verify with GET /events-v2/_count (compare to source count) and GET /events-v2/_mapping (confirm types).

    5. Cut over reads and writes (index alias swap or application config) after validation.

    Example corrected excerpt for the events review pattern:

    {
      "mappings": {
        "properties": {
          "@timestamp": { "type": "date" },
          "event_id": { "type": "keyword" },
          "session_id": { "type": "keyword", "doc_values": false },
          "url": { "type": "keyword" },
          "status_code": { "type": "keyword" },
          "response_time_ms": { "type": "scaled_float", "scaling_factor": 100 },
          "tags": { "type": "keyword" },
          "message": { "type": "text" },
          "labels": { "type": "flattened" }
        }
      }
    }

    Do not attempt in-place mapping fixes for these type changes — they are rejected or leave data inconsistent. For greenfield indices, a single PUT /{index} before first ingest avoids reindex entirely.

Review checklist

When the user supplies a mapping JSON and usage notes, walk this checklist in order:

  1. Match each field's type to its stated access pattern (see step 2).

  2. Flag text on filter/agg-only fields; flag missing multi-fields where search and sort/agg share one logical field.

  3. Flag message.keyword (or similar) without ignore_above on large analyzed text.

  4. Flag dynamic object on high-cardinality free-form maps; recommend flattened.

  5. Propose retrieve-only and numeric storage optimizations (doc_values: false, scaled_float).

  6. State that type changes require a new index and POST /_reindex, then show the corrected mapping and reindex plan.

Examples

"Users search product names and also sort and aggregate on them" — one logical field, two access patterns, so use a text field with a keyword multi-field:

{
  "mappings": {
    "properties": {
      "product_name": { "type": "text", "fields": { "keyword": { "type": "keyword", "ignore_above": 256 } } }
    }
  }
}

"A status field is only ever filtered and aggregated, never full-text searched" — use keyword, not text:

{ "mappings": { "properties": { "status": { "type": "keyword" } } } }

"Free-form labels object with unbounded keys" — avoid mapping explosion with flattened:

{ "mappings": { "properties": { "labels": { "type": "flattened" } } } }

Guidelines

  • Minimal mapping — Map only what access patterns require; every sub-field and analyzed form adds indexed data.

  • Never guess access patterns — Wrong type choice is expensive to fix at scale.

  • Verify after create — Always confirm with GET /{index}/_mapping; use GET /{index}/_count after reindex.

  • Cross-skill boundary — Copying documents between indices is POST /_reindex (see the reindex skill for slicing, throttling, and task tracking). Loading files into a new index is bulk ingest, not index design.

Reference material

Operations

HTTP API (shorthand)elastic CLI command
GET /elastic es info
GET /{index}/_mappingelastic es indices get-mapping --index '<index>'
PUT /{index}elastic es indices create --index '<index>' --mappings '<json>' --settings '<json>'
POST /_reindexelastic es reindex --source '<json>' --dest '<json>'
POST /_reindex?wait_for_completion=falseelastic es reindex --wait-for-completion false --source '<json>' --dest '<json>'
GET /{index}/_countelastic es count --index '<index>'

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

The author says: Elasticsearch 8.x or 9.x, self-managed, Elastic Cloud Hosted, or Elastic Cloud Serverless; explicit shard and replica settings apply to self-managed and Elastic Cloud Hosted only (managed internally on Serverless). Requires the `elastic` CLI ≥ 0.2 with `stack es` support.

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Attribution and licence

Created by
elastic
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Elastic (Official)
Source
elastic/agent-skills · skills/elasticsearch/elasticsearch-index-design · commit 09d879d
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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