All About Parquet Part 05 - Compression Techniques in Parquet

One of the key benefits of using the Parquet file format is its ability to compress data efficiently, reducing storage costs while maintaining fast query performance. Parquet’s columnar storage model enables highly effective compression, as data of the same type is stored together, allowing compression algorithms to work more effectively. In this post, we’ll explore the various compression techniques supported by Parquet, how they work, and how to choose the right one for your data.

Why Compression Matters

Compression is crucial for managing large datasets. By reducing the size of the data on disk, compression not only saves storage space but also improves query performance by reducing the amount of data that needs to be read from disk and transferred over networks.

Parquet’s columnar storage format further enhances the efficiency of compression by storing similar data together, which often results in higher compression ratios than row-based formats. But not all compression algorithms are created equal—different techniques have varying impacts on file size, read/write performance, and CPU usage.

Compression Algorithms Supported by Parquet

Parquet supports several widely-used compression algorithms, each with its own strengths and weaknesses. Here are the main compression options you can use when writing Parquet files:

1. Snappy

Snappy is one of the most popular compression algorithms used in Parquet due to its speed and reasonable compression ratio. It was developed by Google to provide a fast and lightweight compression method that is optimized for both speed and efficiency.

Pros: Fast compression and decompression, making it ideal for real-time queries and analytics workloads.
Cons: Provides a moderate compression ratio compared to other algorithms, meaning that it may not reduce file sizes as much as more aggressive compression methods.
Use Case: Snappy is a good choice when you prioritize performance and need to process data quickly, especially for interactive queries where speed is more important than achieving the smallest file size.

2. Gzip

Gzip is a compression algorithm known for providing a high compression ratio, but it is slower than Snappy when it comes to both compressing and decompressing data. It is widely used in systems where saving storage space is a priority.

Pros: Provides better compression ratios compared to Snappy, resulting in smaller file sizes.
Cons: Slower to compress and decompress data, making it less suitable for time-sensitive or interactive queries.
Use Case: Gzip is a good option when you need to reduce storage costs significantly and query performance is less of a concern, such as for archiving data or when working with large, infrequently accessed datasets.

3. Brotli

Brotli is a newer compression algorithm developed by Google that offers even higher compression ratios than Gzip, with better performance. It is increasingly used in scenarios where both file size and decompression speed are important.

Pros: Higher compression ratios than Gzip and better decompression speed, making it a good balance between file size reduction and read performance.
Cons: Slower to compress data compared to Snappy or Gzip, but faster to decompress than Gzip.
Use Case: Brotli is an excellent choice for compressing large datasets where both read performance and storage efficiency are important, such as in data lakes or cloud storage systems.

4. Zstandard (ZSTD)

Zstandard (ZSTD) is a modern compression algorithm that provides high compression ratios with fast decompression speeds. ZSTD has gained popularity in recent years due to its versatility and ability to be tuned for both speed and compression ratio.

Pros: Provides a very good balance between compression speed, decompression speed, and file size reduction. ZSTD can be adjusted to favor either speed or compression ratio based on specific requirements.
Cons: Requires more configuration compared to simpler algorithms like Snappy or Gzip.
Use Case: ZSTD is ideal for scenarios where you need high compression ratios and fast decompression, such as for optimizing storage in data lakes while maintaining fast query performance.

5. LZO

LZO is another lightweight compression algorithm that focuses on fast decompression and is often used in real-time processing systems. However, it generally provides lower compression ratios compared to other algorithms like Gzip or Brotli.

Pros: Very fast decompression, making it suitable for real-time analytics and streaming data processing.
Cons: Lower compression ratios, which can result in larger file sizes compared to other algorithms.
Use Case: LZO is a good choice when you need extremely fast data access and compression is less of a concern, such as in streaming applications or low-latency analytics.

Choosing the Right Compression Algorithm

Selecting the right compression algorithm for your Parquet files depends on your specific use case and the balance you want to achieve between compression efficiency and performance. Here are some considerations to help guide your decision:

Query Speed vs. File Size: If your workload requires fast query performance, prioritize algorithms like Snappy or ZSTD that decompress quickly, even if they provide slightly larger file sizes. If storage space is more important, algorithms like Gzip or Brotli may be better suited due to their higher compression ratios.
Data Type and Repetition: Some compression algorithms work better on certain data types. For example, dictionary encoding combined with Gzip or Brotli works well on columns with many repeated values. Snappy or LZO might be better for columns with highly variable data.
Storage Costs: For workloads where storage costs are a primary concern (e.g., archiving large datasets), Gzip and Brotli will provide the smallest file sizes, which can lead to significant cost savings in cloud storage environments.
Real-Time Processing: For real-time analytics or systems where low-latency access to data is critical, Snappy or LZO should be the preferred options due to their fast decompression speeds.

Combining Compression with Encoding

In addition to choosing a compression algorithm, Parquet allows you to pair compression with various encoding techniques, such as dictionary encoding or run-length encoding (RLE). This combination can further optimize storage efficiency, especially for columns with repetitive values.

For example:

Dictionary Encoding: Works well with columns that contain many repeated values, like categorical data. Pairing dictionary encoding with Gzip or ZSTD can lead to significant reductions in file size.
Run-Length Encoding (RLE): This encoding is particularly useful for columns with consecutive repeated values, such as timestamps or sequences. Combining RLE with a high-compression algorithm like Brotli can achieve very high compression ratios.

Conclusion

Compression is a critical aspect of managing large datasets, and Parquet’s support for multiple compression algorithms allows you to optimize your data storage and processing based on the specific needs of your workload. Whether you prioritize query performance with Snappy or aim for maximum storage efficiency with Gzip or Brotli, Parquet’s flexibility ensures that you can strike the right balance between speed and file size.

In the next post, we’ll explore encoding techniques in Parquet, diving deeper into how encoding works and how it complements compression for efficient data storage.

Stay tuned for part 6: Encoding in Parquet: Optimizing for Storage.