Does Hive Support JSON Data Type?


Angela Bailey

Does Hive Support JSON Data Type?

In the world of big data processing, Hive has emerged as a popular choice due to its simplicity and compatibility with Apache Hadoop. However, one question that often arises is whether Hive supports the JSON data type. Let’s dive into this topic and explore what Hive has to offer when it comes to handling JSON data.

Understanding Hive Data Types

Before discussing the support for JSON data in Hive, let’s take a quick look at the data types supported by Hive. By default, Hive supports various primitive data types such as integers, floats, strings, and more. Additionally, it provides complex data types like arrays, maps, and structs.

The Need for JSON Support

JSON (JavaScript Object Notation) has become an increasingly popular format for exchanging data due to its simplicity and flexibility. Many modern applications generate or consume JSON-formatted data, making it crucial for big data platforms like Hive to support this format.

Hive’s Approach to JSON Data

Hive does not have a dedicated built-in JSON data type like some other databases or programming languages. However, that doesn’t mean you can’t work with JSON in Hive.

Hive provides several functions and features that allow you to handle JSON effectively:

  • SerDe (Serializer/Deserializer): A SerDe is a module in Hive that helps with the serialization and deserialization of various file formats, including JSON. There are several third-party SerDes available that can be used with Hive to process JSON files.
  • JSON Functions: Hive offers a set of built-in functions specifically designed to handle JSON data.

    These functions allow you to extract, manipulate, and transform JSON data within Hive queries.

  • Structs and Maps: Hive’s support for complex data types like structs and maps allows you to store JSON-like structures in Hive tables. By leveraging these data types, you can effectively work with JSON-like data in Hive.

Working with JSON in Hive

To work with JSON in Hive, you need to follow these steps:

Step 1: Create a Table

Create a table in Hive that represents the structure of your JSON data. You can use the appropriate column types like strings, integers, arrays, or maps based on your JSON schema.

Step 2: Define the SerDe

Specify the SerDe (Serializer/Deserializer) for your table. This tells Hive how to interpret the JSON format. There are various third-party SerDes available that support JSON.

Step 3: Load Data

Load your JSON data into the table using common loading techniques such as LOAD DATA or INSERT INTO statements. Hive will use the specified SerDe to parse and store the JSON data accordingly.

Step 4: Querying and Manipulating

You can now query and manipulate your JSON data using standard Hive SQL queries. Utilize the built-in functions provided by Hive to extract specific fields, perform aggregations, or apply transformations on your JSON-like structures.

The Limitations

While Hive provides ways to work with JSON data, it’s important to note some limitations:

  • Hive’s JSON support relies on third-party SerDes, which may have their own limitations or compatibility issues.
  • Processing JSON data in Hive can be slower compared to dedicated JSON databases due to Hive’s batch-oriented nature.
  • Complex nested JSON structures may require additional effort and custom logic to extract and manipulate the desired data.


Although Hive does not have native support for the JSON data type, it offers several features and functions that allow you to work with JSON effectively. By leveraging SerDes, built-in functions, and complex data types like structs and maps, you can process, query, and transform JSON-like data within Hive. However, it’s important to consider the limitations and trade-offs when working with JSON in a batch-oriented system like Hive.

So, if you’re working with JSON data in your big data environment that utilizes Hive, rest assured that there are ways to handle it efficiently with the right tools and techniques.

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