Does Parquet Support Date Data Type?


Larry Thompson

Does Parquet Support Date Data Type?

If you are working with big data and need an efficient file format for storing and processing it, you have probably come across Parquet. Parquet is a columnar storage file format that is optimized for performance and compression. It is widely used in big data frameworks like Apache Hadoop, Apache Spark, and Apache Impala.

When working with date values in your big data applications, it’s important to understand whether Parquet supports the date data type. Let’s dive into this topic and explore the capabilities of Parquet in handling dates.

Parquet Data Types

Parquet supports a variety of data types, including primitive types like integers, floating-point numbers, booleans, and strings. Additionally, it provides support for complex types like arrays, maps, and structs. However, when it comes to dates specifically, Parquet offers a dedicated data type called Date.

The Date Data Type in Parquet

The Date data type in Parquet represents a date value without any time component. It uses 32 bits to store the number of days since January 1st, 1970 (also known as the Unix epoch). This format allows for efficient storage and manipulation of date values.

By using the Date data type in Parquet files, you can benefit from various optimizations provided by Parquet-compatible processing engines. These optimizations include predicate pushdowns (filtering rows based on date conditions), efficient compression techniques specific to date values, and more.

Working with Dates in Parquet

To work with dates in Parquet files effectively, you need to ensure that your processing engine has built-in support for the Date data type. Most popular big data frameworks like Apache Spark and Apache Impala have native support for Parquet and its date data type.

When reading Parquet files containing date columns in Spark, you can use the from_unixtime function to convert the integer representation of dates into a more human-readable format. Similarly, when writing Parquet files in Spark, you can use the unix_timestamp function to convert dates from their string representation to the Parquet-compatible format.

In Apache Impala, you can directly query and manipulate date columns stored in Parquet files using built-in functions like CAST, DATEDIFF, and DATE_ADD. These functions provide powerful capabilities for working with date values efficiently.


In summary, Parquet supports the Date data type, making it a suitable choice for storing and processing date values in big data applications. Its efficient storage format and compatibility with popular big data frameworks make it an excellent option for handling large datasets efficiently.

If you are working with dates in your big data projects, consider leveraging Parquet’s support for the Date data type to optimize storage, processing, and analysis of your date-based information.

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