Elasticsearch Data Types

Elasticsearch is a powerful search engine that can handle a wide variety of data types. Understanding how data types work in Elasticsearch is important when designing your index and mapping your fields. In this article, we’ll explore the different data types available in Elasticsearch and when to use them.

Overview of Elasticsearch Data Types

Elasticsearch has several built-in data types that can be used to define fields in an index. Each data type is designed to store a specific type of data and has its own set of properties and methods for searching and aggregating data.

Here are some of the most common data types in Elasticsearch:

• Text: Used to store long-form text, such as article content, blog posts, or product descriptions. Text fields are analyzed at index time and are broken down into individual terms, which can then be used for full-text search.
• Keyword: Used to store short-form text, such as tags, usernames, or product names. Keyword fields are not analyzed and are stored as-is, so they can be used for exact matches.
• Numeric: Used to store numeric data, such as prices, ratings, or quantities. Numeric fields can be stored as integers, floats, or doubles and can be used for sorting, filtering, and aggregations.
• Date: Used to store date and time information, such as created or updated timestamps. Date fields can be stored in several different formats and can be used for range queries and date-based aggregations.
• Boolean: Used to store true/false values, such as whether a product is in stock or not. Boolean fields can be used for filtering and aggregations.
• Object: Used to store nested objects or arrays of values. Object fields can contain any other data type and can be used for complex queries and aggregations.
• Geo: Used to store geographic data, such as latitude and longitude coordinates. Geo fields can be used for location-based queries and aggregations.
• IP: Used to store IPv4 and IPv6 addresses. IP fields can be used for filtering and aggregations.
• Binary: Binary fields store binary data, such as images or files. Elasticsearch can index binary data and retrieve it when needed.
• Nested and Object: These data types allow you to work with structured and nested data within your documents. They are particularly useful when dealing with complex, hierarchical data structures.

Mapping Data Types in Elasticsearch

When you create an index in Elasticsearch, you define the data types for each field in the index mapping. The mapping tells Elasticsearch how to interpret the data in each field, including how to index, analyze, and search the data.

Here’s an example of how to define a mapping for an index with two fields, “title” and “body”:

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

In this example, both “title” and “body” are defined as “text” data types, which means they will be analyzed at index time and broken down into individual terms for searching.

You can also define additional properties for each field, such as whether the field is required, whether it can be updated, and how the field should be analyzed. Here’s an example of how to add a “keyword” field with additional properties:

PUT /my_index
{
  "mappings": {
    "properties": {
      "title": {
        "type": "text"
      },
      "tags": {
        "type": "keyword",
        "ignore_above": 20
      }
    }
  }
}

In this example, “tags” is defined as a “keyword” data type with an “ignore_above” property of 20, which means any value longer than 20 characters will be truncated. I will tell more about mapping from practice side at separate article “what is mapping and why it is so important”. Please, check “Elasticsearch basics” topic regular. Current article will appear soon. Or simply subscribe to my newsletter .

Choosing the Right Data Type

Selecting the appropriate data type for each field in your Elasticsearch index is crucial for efficient data management and effective querying. Here are some key considerations:

1. Data Accuracy: Ensure that the data type accurately represents the nature of the data in the field. This prevents issues like incorrect sorting or unexpected query results.
2. Storage Efficiency: Choose data types that minimize storage space without compromising data quality. For example, use keyword fields for exact matches instead of text fields.
3. Query Performance: The choice of data type can significantly impact query performance. Numeric fields, for instance, allow for faster range queries and aggregations.
4. Analyzers: If working with text data, consider the use of custom analyzers to tokenize and index the content effectively.

Conclusion

In Elasticsearch, data types play a vital role in shaping how your data is stored, indexed, and queried. By selecting the appropriate data types for your fields, you can optimize storage, improve query performance, and ensure accurate search results. Understanding and leveraging Elasticsearch data types is a fundamental step towards harnessing the full power of this versatile search and analytics engine for your data management needs. If you want to know more about how to choose datatype properly from practice – then welcome to my course “Elasticsearch as you have never known it before”

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