Elasticsearch Mappings Tutorial

Introduction to Mappings

In Elasticsearch, mappings are used to define how documents and their fields are stored and indexed. They are similar to the schema in a relational database. A mapping defines:

• The data types for fields (e.g., string, integer, date).
• The configurations for various field properties (e.g., indexing options, analyzers).

Creating a Mapping

To create a mapping in Elasticsearch, you typically define it when creating an index. Here is an example of how to create an index with a mapping:

PUT /my_index
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text"
      },
      "date": {
        "type": "date"
      },
      "views": {
        "type": "integer"
      }
    }
  }
}

This command creates an index named my_index with fields title, date, and views.

Understanding Field Data Types

Elasticsearch supports various field data types. Some common ones include:

• text: Used for full-text search.
• keyword: Used for structured search, exact values.
• integer: Numeric data without a fraction.
• float: Numeric data with a fraction.
• date: Date values.
• boolean: True or false values.

Updating Mappings

Once an index is created, you cannot change the mapping of existing fields. However, you can add new fields. Here is how you can add a new field to an existing index:

PUT /my_index/_mapping
{
  "properties": {
    "author": {
      "type": "keyword"
    }
  }
}

This command adds a new field author to the my_index index.

Analyzers

Analyzers are used to process text fields during indexing and searching. They break down text into tokens (terms) and optionally apply filters. Here's an example of how to define an analyzer in a mapping:

PUT /my_index
{
  "settings": {
    "analysis": {
      "analyzer": {
        "my_analyzer": {
          "type": "standard",
          "stopwords": "_english_"
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "content": {
        "type": "text",
        "analyzer": "my_analyzer"
      }
    }
  }
}

In this example, we create a custom analyzer named my_analyzer and apply it to the content field.

Dynamic vs. Explicit Mappings

Elasticsearch supports both dynamic and explicit mappings:

• Dynamic Mappings: Elasticsearch automatically detects and adds new fields to the mapping.
• Explicit Mappings: You define the mapping for each field explicitly.

By default, dynamic mapping is enabled. You can control or disable it using the dynamic parameter:

PUT /my_index
{
  "mappings": {
    "dynamic": "strict",
    "properties": {
      "title": {
        "type": "text"
      }
    }
  }
}

Setting dynamic to strict will prevent Elasticsearch from dynamically adding new fields.

Example: Complete Mapping

Here is a complete example of an index mapping in Elasticsearch:

PUT /blog
{
  "settings": {
    "number_of_shards": 1,
    "number_of_replicas": 1,
    "analysis": {
      "analyzer": {
        "english_analyzer": {
          "type": "standard",
          "stopwords": "_english_"
        }
      }
    }
  },
  "mappings": {
    "properties": {
      "title": {
        "type": "text",
        "analyzer": "english_analyzer"
      },
      "author": {
        "type": "keyword"
      },
      "content": {
        "type": "text",
        "analyzer": "english_analyzer"
      },
      "publish_date": {
        "type": "date"
      },
      "views": {
        "type": "integer"
      }
    }
  }
}

This example creates an index named blog with various field mappings and custom analyzer settings.

Conclusion

Mappings in Elasticsearch are a powerful way to control how your data is indexed and searched. Understanding and configuring mappings appropriately can greatly enhance your search capabilities and performance.

We hope this tutorial has provided a comprehensive understanding of mappings in Elasticsearch. Happy indexing!