Mapping api

As explained in Elasticsearch in Action, Second Edition

A mapping API is an Elasticsearch API for defining how index fields are mapped and analyzed.

A mapping API defines how Elasticsearch fields are stored and analyzed before indexing. Explicit mappings prevent dynamic mapping from inferring an unsuitable schema and let structured fields support sorting, filtering, and aggregations. section 4.0 section 4.3

Use the two explicit-mapping approaches for different schema changes:

• Create the index and its initial mapping together by sending a mappings object to the create index API. section 4.3 section 4.3.1
• Add new fields to an existing or empty index through its _mapping endpoint. section 4.3 section 4.3.2
• Keep the properties object at the root of an _mapping request; initial index creation wraps it inside mappings. section 4.3.2
• Treat existing field types as fixed: adding fields is supported, but changing an existing type is not. section 4.3.2

Define and extend an employee index

Create the initial schema with an HTTP PUT request: PUT employees { "mappings": { "properties": { "name": { "type": "text" }, "age": { "type": "integer" }, "email": { "type": "keyword" }, "address": { "properties": { "street": { "type": "text" }, "country": { "type": "text" } } } } } } Later, add fields to the live index: PUT employees/_mapping { "properties": { "joining_date": { "type": "date", "format": "dd-MM-yyyy" }, "phone_number": { "type": "keyword" } } } The date mapping supports date operations, while keyword preserves the phone number without tokenization. Elasticsearch infers the inner address object from its nested properties. section 4.3.1 section 4.3.2

Figure 4.5 Creating and updating a schema using the indexing and mapping APIs

Compare index creation with later schema updates through the mapping and indexing API flow. section 4.3

How Elasticsearch in Action, Second Edition covers it

How deep it goes

Works through initial mappings, live additive updates, request shapes, inferred objects, and immutable existing field types.

Examples

Uses employee fields including text, integer, keyword, date, and object properties in create and update requests.

What it assumes

Assumes basic Elasticsearch indexing and documents while teaching schema control, field types, and mapping request structure.

What you can do after

Operate/configure Elasticsearch indexes with explicit initial mappings and additive field updates through HTTP PUT requests.

How it connects

Connects dynamic mapping with indexing APIs, data types, schema design, analysis, sorting, filtering, and aggregations.

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Related concepts

• schemaconfigures · A data schema is a formal specification of the structure, fields, types, relationships, and constraints of data.
• endpointuses · An endpoint is a network-accessible address or interface through which a service exposes a resource or operation and handles requests and responses.
• similarity scoringconfigured by · Similarity scoring is the computation of a numerical measure indicating how closely two or more items resemble or match one another.
• HTTP PUT methoduses · The HTTP PUT method requests creation or complete replacement of a resource representation at a specified target URI.
• mappinguses · An Elasticsearch mapping is configuration that defines indexed document fields, their data types, and their indexing and handling rules.

This explainer was generated by Manning's knowledge graph from the text of Elasticsearch in Action, Second Edition. The section links point to the parts of the book it draws on.