Comparison of Gzip, LZO, Snappy, and Bzip2 Compression Formats for Hadoop

Gzip Compression Gzip offers a relatively high compression ratio and fast compression/decompression speed. Hadoop natively supports gzip, allowing gzip files to be processed like plain text, and most Linux systems include the gzip command.

Advantages : high compression ratio, fast speed, Hadoop native support, available native library, built‑in Linux command.

Disadvantages : does not support split.

Use Cases : suitable when each compressed file is within a single HDFS block (≈130 MB), such as hourly or daily log files, enabling parallel MapReduce processing without modifying existing programs.

LZO Compression LZO provides fast compression/decompression with reasonable compression ratio, supports split (the most popular format in Hadoop), and has a native library; the lzop command is available on Linux.

Advantages : fast speed, reasonable compression, split support, Hadoop native library, easy installation.

Disadvantages : lower compression ratio than gzip, not supported by Hadoop out‑of‑the‑box (requires installation), requires index files and a special input format to enable split.

Use Cases : large text files that remain larger than 200 MB after compression; the larger the file, the more LZO’s benefits become apparent.

Snappy Compression Snappy delivers very high compression speed with a moderate compression ratio and supports the Hadoop native library.

Advantages : extremely fast compression, reasonable compression ratio, Hadoop native library support.

Disadvantages : does not support split, lower compression ratio than gzip, not supported by Hadoop by default (requires installation), no standard Linux command.

Use Cases : compressing large intermediate data between Map and Reduce stages, or using it as the output format of one MapReduce job and the input format of another.

Bzip2 Compression Bzip2 supports split, provides a very high compression ratio (higher than gzip), and is natively supported by Hadoop (though without a native library); Linux includes the bzip2 command.

Advantages : split support, high compression ratio, Hadoop support, easy command‑line usage.

Disadvantages : slow compression/decompression speed, no native library support.

Use Cases : scenarios where speed is less critical but high compression is needed, such as archiving large MapReduce outputs, reducing storage for massive text files while retaining split capability and compatibility with existing programs.

A comparative chart summarizing the strengths and weaknesses of the four compression formats is shown below:

Support through likes and shares is greatly appreciated!