Cassandra offers operators the ability to configure compression on a per-table basis. Compression reduces the size of data on disk by compressing the SSTable in user-configurable compression chunk_length_in_kb. Because Cassandra SSTables are immutable, the CPU cost of compressing is only necessary when the SSTable is written - subsequent updates to data will land in different SSTables, so Cassandra will not need to decompress, overwrite, and recompress data when UPDATE commands are issued. On reads, Cassandra will locate the relevant compressed chunks on disk, decompress the full chunk, and then proceed with the remainder of the read path (merging data from disks and memtables, read repair, and so on).

Configuring Compression

Compression is configured on a per-table basis as an optional argument to CREATE TABLE or ALTER TABLE. By default, three options are relevant:

  • class specifies the compression class - Cassandra provides three classes (LZ4Compressor, SnappyCompressor, and DeflateCompressor ). The default is LZ4Compressor.
  • chunk_length_in_kb specifies the number of kilobytes of data per compression chunk. The default is 64KB.
  • crc_check_chance determines how likely Cassandra is to verify the checksum on each compression chunk during reads. The default is 1.0.

Users can set compression using the following syntax:

CREATE TABLE keyspace.table (id int PRIMARY KEY) WITH compression = {'class': 'LZ4Compressor'};


ALTER TABLE keyspace.table WITH compression = {'class': 'SnappyCompressor', 'chunk_length_in_kb': 128, 'crc_check_chance': 0.5};

Once enabled, compression can be disabled with ALTER TABLE setting enabled to false:

ALTER TABLE keyspace.table WITH compression = {'enabled':'false'};

Operators should be aware, however, that changing compression is not immediate. The data is compressed when the SSTable is written, and as SSTables are immutable, the compression will not be modified until the table is compacted. Upon issuing a change to the compression options via ALTER TABLE, the existing SSTables will not be modified until they are compacted - if an operator needs compression changes to take effect immediately, the operator can trigger an SSTable rewrite using nodetool scrub or nodetool upgradesstables -a, both of which will rebuild the SSTables on disk, re-compressing the data in the process.

Benefits and Uses

Compression’s primary benefit is that it reduces the amount of data written to disk. Not only does the reduced size save in storage requirements, it often increases read and write throughput, as the CPU overhead of compressing data is faster than the time it would take to read or write the larger volume of uncompressed data from disk.

Compression is most useful in tables comprised of many rows, where the rows are similar in nature. Tables containing similar text columns (such as repeated JSON blobs) often compress very well.

Operational Impact

  • Compression metadata is stored off-heap and scales with data on disk. This often requires 1-3GB of off-heap RAM per terabyte of data on disk, though the exact usage varies with chunk_length_in_kb and compression ratios.
  • Streaming operations involve compressing and decompressing data on compressed tables - in some code paths (such as non-vnode bootstrap), the CPU overhead of compression can be a limiting factor.
  • The compression path checksums data to ensure correctness - while the traditional Cassandra read path does not have a way to ensure correctness of data on disk, compressed tables allow the user to set crc_check_chance (a float from 0.0 to 1.0) to allow Cassandra to probabilistically validate chunks on read to verify bits on disk are not corrupt.

Advanced Use

Advanced users can provide their own compression class by implementing the interface at