All About Parquet Part 03 - Parquet File Structure | Pages, Row Groups, and Columns

In the previous post, we explored the benefits of Parquet’s columnar storage model. Now, let’s delve deeper into the internal structure of a Parquet file. Understanding how Parquet organizes data into pages, row groups, and columns will give you valuable insights into how Parquet achieves its efficiency in storage and query execution. This knowledge will also help you make informed decisions when working with Parquet files in your data pipelines.

The Hierarchical Structure of Parquet

Parquet uses a hierarchical structure to store data, consisting of three key components:

Row Groups
Columns
Pages

These components work together to enable Parquet’s ability to store large datasets while optimizing for efficient read and write operations.

1. Row Groups

A row group is a horizontal partition of data in a Parquet file. It contains all the column data for a subset of rows. Think of a row group as a container that holds the data for a chunk of rows. Each row group can be processed independently, allowing Parquet to perform parallel processing and read specific sections of the data without needing to load the entire dataset into memory.

Why Row Groups Matter

Row groups are crucial for performance. When querying data, especially in distributed systems like Apache Spark or Dremio, the ability to read only the row groups relevant to a query greatly improves efficiency. By splitting the dataset into row groups, Parquet minimizes the amount of data scanned during query execution, reducing both I/O and compute costs.

Row Group Size: A typical row group size is set based on the expected query pattern and memory limitations of your processing engine. A smaller row group size allows for more parallelism, but increases the number of read operations. A larger row group size reduces the number of I/O operations but may increase memory usage during query execution.

2. Columns Within Row Groups

Within each row group, the data is stored column-wise. Each column in a row group is called a column chunk. These column chunks hold the actual data values for each column in that row group.

The columnar organization of data within row groups allows Parquet to take advantage of columnar compression and query optimization techniques. As we mentioned in the previous blog, Parquet can skip reading entire columns that aren’t relevant to a query, further improving performance.

Column Compression: Since similar data types are stored together in a column chunk, Parquet can apply compression techniques such as dictionary encoding or run-length encoding, which work particularly well on columns with repeated values or patterns.

3. Pages: The Smallest Unit of Data

Within each column chunk, data is further divided into pages, which are the smallest unit of data storage in Parquet. Pages help break down column chunks into more manageable sizes, making data more accessible and enabling better compression.

There are two types of pages in Parquet:

Data Pages: These contain the actual values for a column.
Index Pages: These store metadata such as min and max values for a range of data, which can be used for filtering during query execution. By storing statistics about the data, Parquet can skip reading entire pages that don’t match the query, speeding up execution.

Page Size and Its Impact

The page size in a Parquet file plays an important role in balancing read and write performance. Larger pages reduce the overhead of managing metadata but may lead to slower reads if the page contains irrelevant data. Smaller pages provide better granularity for skipping irrelevant data during queries, but they come with higher metadata overhead.

By default, Parquet sets the page size to a few megabytes, but this can be configured based on the specific needs of your workload.

The Role of Metadata in Parquet Files

Parquet files also store extensive metadata at multiple levels (file, row group, and page). This metadata contains useful information, such as:

Column statistics: Min, max, and null counts for each column.
Compression schemes: The compression algorithm used for each column chunk.
Schema: The structure of the data, including data types and field names.

This metadata plays a crucial role in query optimization. For example, the column statistics allow query engines to skip row groups or pages that don’t contain data relevant to the query, significantly improving query performance.

File Metadata

At the file level, Parquet stores global metadata that describes the overall structure of the file, such as the number of row groups, the file schema, and encoding information for each column.

Footer: Parquet stores this file-level metadata in the footer of the file, which allows data processing engines to quickly read the structure of the file without scanning the entire dataset. This structure ensures that the metadata is accessible without having to read the entire file first, enabling fast schema discovery and data exploration.

Row Group Metadata

Each row group also has its own metadata, which describes the columns it contains, the number of rows, and statistics for each column chunk. This enables efficient querying by allowing Parquet readers to filter out row groups that don’t meet the query conditions.

Optimizing Parquet File Structure

When working with Parquet files, optimizing the structure of your files based on the expected query patterns can lead to better performance. Here are some tips:

Row Group Size: Adjust the row group size based on the memory capacity of your processing engine. If your engine has limited memory, smaller row groups might help avoid memory issues. Larger row groups can be beneficial when you need to minimize I/O operations.
Page Size: Tuning the page size can improve compression and query performance. Smaller page sizes are better for queries that involve filters, as they allow more granular data skipping.
Compression and Encoding: Selecting the right compression algorithm and encoding scheme for your data type can make a significant difference in file size and query speed. For example, dictionary encoding is a good choice for columns with many repeated values.

Conclusion

The hierarchical structure of Parquet files—organized into row groups, columns, and pages—enables efficient storage and fast data access. By organizing data this way, Parquet minimizes unnecessary reads and maximizes the potential for parallel processing and compression.

Understanding how these components interact helps you optimize your data storage and querying processes, ensuring that your data pipelines run as efficiently as possible.

In the next blog post, we’ll explore schema evolution in Parquet, diving into how Parquet handles changes in data structures over time and why this flexibility is key in dynamic data environments.

Stay tuned for part 4: Schema Evolution in Parquet.