Data types in Elasticsearch

What are Data Types in Elasticsearch?

Data types in Elasticsearch are a vital component of its core functionality as a distributed, open-source search and analytics engine. They define the type of data that can be stored in fields within an index, including text, date, nest, boolean, and others. Elasticsearch uses these data types to effectively index, search, and analyze data across various business applications.

Functionality and Features

Elasticsearch supports numerous data types divided into core types, complex types, and specialized types. Core types include text, keyword, date, long, double, etc., representing the fundamental data types. Complex types, such as object and nested, allow for structured JSON data to be stored. Specialized types, including IP, geo_point, and geo_shape, cater to specific use-cases.


In Elasticsearch's architecture, data types are used to shape the fields within an index. An index can be compared to a 'database' in a relational database, with the type being a 'table. Each field in Elasticsearch has a dedicated data type, which defines the kind of operations that can be performed on the field.

Benefits and Use Cases

Defining appropriate data types in Elasticsearch enhances its search and analytics capabilities. It supports full-text search, making it useful for log or event data analysis, real-time application monitoring, and more. For example, the geo_point data type allows for geographic location-based data searches.

Challenges and Limitations

While Elasticsearch offers many benefits, it isn't devoid of challenges. Deciding the right mapping and data types at the start is crucial because changing them after data ingestion can be challenging. Also, managing large volumes of data might require hardware with high computational capabilities.

Comparison to Other Technologies

Elasticsearch stands out with its real-time data ingestion, full-text search capabilities, and scalability compared to traditional databases. However, compared to schema-less NoSQL databases, Elasticsearch requires predefined data types, which can impact its flexibility.

Integration with Data Lakehouse

Elasticsearch's data types can be effectively used in a Data Lakehouse environment for organizing and optimizing search and analytics operations. Data Lakehouse combines the benefits of a data lake and a data warehouse, providing both raw and structured data storage and analytics capabilities.

Security Aspects

Elasticsearch ensures data security through features like role-based access control, field- and document-level security, audit logging, and more. These features control who can access the data and what operations they can perform.


Appropriate use of data types in Elasticsearch can significantly impact its performance. For instance, using the right numeric data type based on the size of the data can optimize storage and enhance search speed.


Can data types in Elasticsearch be changed after data ingestion? Altering data types after data ingestion is difficult, as it requires reindexing, which can be resource-intensive.

What role do data types play in Elasticsearch's performance? Correct usage of data types can optimize storage space and improve search speed.

What are some specialized data types in Elasticsearch? Examples of specialized data types include geo_point and geo_shape for geographical data, IP for IP addresses, and range types for range values.


Index: An 'Index' in Elasticsearch is like a database in a relational database system, where the actual data is stored.

Mapping: 'Mapping' is the process of defining how a document and its fields are stored and indexed in Elasticsearch.

Data Type: 'Data Type' in Elasticsearch specifies the type of data a field can store, including text, keyword, date, long, double, among others.

Data Lakehouse: 'Data Lakehouse' is a new architecture that combines the benefits of data lakes and data warehouses, providing both raw and structured data storage and analytics capabilities.

Reindexing: 'Reindexing' is the process of creating a new index with a different structure or settings and copying the data from the existing index to the new one.

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