Choosing the Right Compression Algorithm for Data Engineering Workloads

A Comparative Analysis of Compression algorithms

1. Zstandard (Zstd)
High compression ratios and quick decompression times are hallmarks of the contemporary compression algorithm Zstd. It was created by Meta in 2016 and is quite adaptable, enabling developers to alter compression levels to balance ratio and speed.

• Advantages:

    – Excellent compression ratio with low CPU utilisation.
    – Supports parallelism.

• Use Cases:
   – Ideal for large-scale data pipelines where both compression efficiency and speed are critical.

2. Snappy
Snappy is a technique for quick compression that prioritises speed over high compression ratios. Google developed it in 2011, and mostly used in big data frameworks like Apache Hadoop and Apache Kafka.

• Advantages:
      – Exceptionally fast compression and decompression.
      – Very low CPU usage.

• Use Cases:
      – Well-suited for use cases where speed is more critical than storage space, such as streaming               data pipelines.

3. Gzip
Developed in 1992, one of the first and most used compression algorithms is Gzip. It tends to be slower than more recent options, but it offers a reasonable compromise between speed and compression ratio.

• Advantages:
      – Good compression ratio.
      – Compatibility with most data systems.

• Use Cases:
       – Suitable for archival purposes and scenarios where compatibility is crucial, and

       decompression speed is not deal breaker.

4. LZO
With LZO lower compression ratios are sacrificed in order to achieve faster compression and decompression speeds. It is frequently employed in situations where performance is crucial. It was developed in 1994 and most commonly used to compress images. Social media platforms like Twitter and Meta have reportedly used LZO internally for storing data.

• Advantages:
       – High speed.
       – Low memory requirements.

• Use Cases:
       – Useful in applications requiring real-time data processing, such as log aggregation systems.

5. Bzip2
Although speed is sacrificed, Bzip2 is renowned for its excellent compression ratios. It is CPU-intensive and employs the Burrows-Wheeler transform. It was developed in 1996 and more efficient than older compression algorithms like LZW and Deflate.

• Advantages:
        – Outstanding compression ratio.

• Use Cases:
       – Best for compressing data that will be accessed seldom, like archival storage.

Below comparative table analyses the compression ratio, storage space, and the speed of compression & decompression:

The above data is based on the general performance trends observed industry wide. The actual performance would depend on various factors like type and format of data, what kind of storage and compute resources are utilised, and specific implementation.

How File Format Impacts Compression Efficiency

Another important element affecting how well compression works is the file format selection. The two main types of file formats—row-based and columnar-based—each have their own benefits and use cases.

• Row-Based Formats:
    – Examples: Avro, CSV, JSON.

Features:
   – Data is stored row by row.
   – Quite effective for write-heavy operations and row-level operations.
Compression Efficiency:
   – Compression algorithms operate on complete rows, which may include a variety of data formats. Suboptimal compression ratios may result from this.
Note: To improve compression efficiency, formats such as Avro offer codec integration (e.g., Snappy, Zstd).

• Columnar-Based Formats
      – Examples: Parquet, ORC.
Features:
   – Data is stored column by column.
   – Optimized for analytical queries which are usually very read-heavy operations

Compression Efficiency:
   – Columnar formats allow algorithms to compress data of the same type (e.g., integers, floats) together, resulting in significantly higher compression ratios.
Note: Parquet and ORC support multiple compression codecs, including Snappy, Zstd, and Gzip, providing users the flexibility to optimize for speed or storage.

Choosing the Right Combination

It is very important to select the right match of data format and compression algorithm The effectiveness of a compression algorithm depends majorly on the use case and the data format:

1. Streaming Pipelines & Real time / near real time workloads:
– File Format: Avro or JSON.
– Compression: Snappy or LZO for speed.

2. Batch pipelines & in general Analytical Workloads:
– File Format: Parquet or ORC.
– Compression: Zstd is quite good considering it offers a very good balance between speed and ratio, or Gzip for better compression ratios when speed is less critical.

3. Archival Storage:
– File Format: Any format of choice, though Parquet is most commonly preferred for its compact       size.
– Compression: Bzip2 or Gzip for achieving very high compression ratio.

Below is a tabular summary:

Conclusion
Selecting the appropriate compression algorithm along with the right file format is quite important for building a robust, performant and cost-effective data engineering pipeline. Algorithms like Zstd and Snappy excel in performance and flexibility, while file formats like Parquet and ORC provide superior compression efficiency for analytical workloads. By carefully evaluating the workload requirements and balancing speed, storage, and compatibility, a data engineer can make informed decisions that significantly enhances the data processing capabilities.