In the last blog, we explored the various compression techniques supported by Parquet to reduce file size and improve query performance. But compression alone isn’t enough to maximize storage efficiency. Parquet also utilizes encoding techniques to further optimize how data is stored, especially for columns with repetitive or predictable patterns. In this post, we’ll dive into how encoding works in Parquet, the different types of encoding it supports, and how to use them to reduce storage footprint while maintaining performance.

What is Encoding in Parquet?

Encoding is the process of transforming data into a more efficient format to save space without losing information. In Parquet, encoding is applied to column data before compression. While compression algorithms focus on reducing redundancy at the byte level, encoding techniques work on the logical structure of the data, particularly for columns with repeating or predictable values.

By using encoding in combination with compression, Parquet achieves smaller file sizes and faster query performance. The choice of encoding is determined by the characteristics of the data in each column. Let’s take a look at the most common encoding techniques used in Parquet.

Types of Encoding in Parquet

Parquet supports several encoding techniques, each designed for specific types of data patterns. Here are the most commonly used ones:

1. Dictionary Encoding

Dictionary encoding is one of the most effective techniques for columns that contain repeated values. It works by creating a dictionary of unique values and then replacing each value in the column with a reference to the dictionary. This significantly reduces the amount of data stored, especially for categorical data.

How It Works: For a column that contains many repeated values (e.g., a "Department" column with repeated entries like "Sales," "Marketing," etc.), Parquet creates a dictionary of these unique values. Each value in the original column is then replaced with a small integer that refers to its position in the dictionary. The dictionary itself is stored once per column, making it very efficient.
Use Case: Dictionary encoding is highly effective for columns with a limited number of unique values (e.g., categorical data, zip codes, or status flags).
Pros: Reduces storage size significantly for columns with repeated values, especially when paired with compression algorithms like Gzip or Brotli.
Cons: May not be as effective for columns with a high number of unique values.

2. Run-Length Encoding (RLE)

Run-Length Encoding (RLE) is another powerful technique for compressing columns with consecutive repeating values. It works by storing the value once along with the number of times it repeats, instead of storing the repeated value multiple times.

How It Works: If a column contains long sequences of the same value (e.g., a "Status" column where many consecutive rows have the status "Active"), RLE stores the value once and records the number of times it repeats, rather than writing the value for each row. For example, instead of storing "Active" 100 times, RLE stores "Active: 100".
Use Case: RLE is ideal for columns with consecutive repeated values, such as status flags, binary values, or sorted columns.
Pros: Very effective at reducing file size for columns with repeated or sorted data.
Cons: Less effective on columns with highly variable data.

3. Bit-Packing

Bit-packing is an encoding technique that reduces the number of bits used to store small integers. Instead of storing each integer as a fixed-size 32-bit or 64-bit value, bit-packing stores each integer in the smallest number of bits necessary to represent it. This is particularly useful for columns that contain small integers, such as IDs or categorical data with a limited number of categories.

How It Works: If a column contains small integer values (e.g., a column with values ranging from 0 to 10), Parquet will use only 4 bits per value instead of 32 or 64 bits. This greatly reduces the amount of space required to store the data.
Use Case: Bit-packing is effective for columns containing small integer values, such as IDs, ratings, or categorical data with a limited range of possible values.
Pros: Reduces the number of bits used for small integers, leading to smaller file sizes.
Cons: Less effective for columns with large integer values or wide ranges of possible values.

4. Delta Encoding

Delta encoding is used to store differences between consecutive values rather than storing the full values themselves. This works well for columns where values are close together or follow a predictable pattern, such as timestamps, IDs, or monotonically increasing numbers.

How It Works: Instead of storing the full value for each row, delta encoding stores the difference between each consecutive value and the previous one. For example, if a timestamp column contains values like 10, 12, 14, 16, delta encoding would store 10, 2, 2, 2, where each subsequent value is the difference from the previous one.
Use Case: Delta encoding is effective for columns with ordered or predictable data patterns, such as timestamps, sequence numbers, or sorted columns.
Pros: Greatly reduces file size for columns with predictable patterns or ordered values.
Cons: Less effective for columns with random or unordered data.

5. Plain Encoding

Plain encoding is the default encoding method in Parquet and is used for columns where no other encoding is more effective. It simply stores the values as they are, without any additional compression or optimization.

How It Works: For columns where values vary greatly or where no pattern is detectable, plain encoding stores the values as-is. This encoding method is often used for strings, floating-point numbers, and other complex data types that do not benefit from the other encoding techniques.
Use Case: Plain encoding is used for columns where no significant reduction in size can be achieved through other encoding methods.
Pros: Simple and effective when no patterns or repetition exist in the data.
Cons: Offers no additional compression or size reduction.

Combining Encoding with Compression

The true power of Parquet comes from combining encoding with compression. For example, using dictionary encoding for a column with many repeated values, followed by Gzip compression, can lead to significant reductions in file size. Similarly, run-length encoding paired with ZSTD compression works well for columns with repeated sequences.

Here are some common pairings of encoding and compression techniques:

Dictionary Encoding + Gzip: Effective for categorical data or columns with repeated values.
Run-Length Encoding + Brotli: Works well for sorted or repeating columns, such as status flags or binary values.
Delta Encoding + ZSTD: Ideal for columns with ordered values, like timestamps or sequence numbers.

Optimizing Encoding for Performance

While encoding can reduce file size, it’s important to balance encoding choices with query performance. Certain encoding techniques, such as dictionary encoding, can improve query speed by reducing the amount of data that needs to be scanned. However, overly aggressive encoding can sometimes lead to slower read performance if it adds too much complexity to the decoding process.

Here are some tips for optimizing encoding in Parquet:

Test Encoding with Your Queries: Different workloads may benefit from different encoding techniques. Test how your queries perform with various encoding options to find the best balance between file size and performance.
Use Statistics to Skip Data: Parquet files store column-level statistics (such as min/max values) that can help query engines skip irrelevant data. Pairing encoding with Parquet’s built-in statistics allows for faster query execution.
Leverage Columnar Design: Since Parquet stores data column-wise, different columns can use different encoding techniques based on their data patterns. Optimize encoding for each column based on its characteristics.

Conclusion

Encoding is a powerful tool for optimizing storage and performance in Parquet files. By choosing the right encoding technique for each column, you can reduce file size while maintaining fast query performance. Whether you’re working with categorical data, ordered values, or repeated patterns, Parquet’s flexible encoding options allow you to tailor your data storage to fit your workload’s specific needs.

In the next post, we’ll dive into how metadata is used in Parquet files to further optimize data retrieval and improve query efficiency.

Stay tuned for part 7: Metadata in Parquet: Improving Data Efficiency.

All About Parquet Part 06 - Encoding in Parquet | Optimizing for Storage