= Compression

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`. As 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).

Compression algorithms typically trade off between the following three
areas:

* *Compression speed*: How fast does the compression algorithm compress
data. This is critical in the flush and compaction paths because data
must be compressed before it is written to disk.
* *Decompression speed*: How fast does the compression algorithm
de-compress data. This is critical in the read and compaction paths as
data must be read off disk in a full chunk and decompressed before it
can be returned.
* *Ratio*: By what ratio is the uncompressed data reduced by. Cassandra
typically measures this as the size of data on disk relative to the
uncompressed size. For example a ratio of `0.5` means that the data on
disk is 50% the size of the uncompressed data. Cassandra exposes this
ratio per table as the `SSTable Compression Ratio` field of
`nodetool tablestats`.

Cassandra offers five compression algorithms by default that make
different tradeoffs in these areas. While benchmarking compression
algorithms depends on many factors (algorithm parameters such as
compression level, the compressibility of the input data, underlying
processor class, etc ...), the following table should help you pick a
starting point based on your application's requirements with an
extremely rough grading of the different choices by their performance in
these areas (A is relatively good, F is relatively bad):

[width="100%",cols="40%,19%,11%,13%,6%,11%",options="header",]
|===
|Compression Algorithm |Cassandra Class |Compression |Decompression
|Ratio |C* Version

|https://lz4.github.io/lz4/[LZ4] |`LZ4Compressor` | A+ | A+ | C+ | `>=1.2.2`

|https://lz4.github.io/lz4/[LZ4HC] |`LZ4Compressor` | C+ | A+ | B+ | `>= 3.6`

|https://facebook.github.io/zstd/[Zstd] |`ZstdCompressor` | A- | A- | A+ | `>= 4.0`

|http://google.github.io/snappy/[Snappy] |`SnappyCompressor` | A- | A | C | `>= 1.0`

|https://zlib.net[Deflate (zlib)] |`DeflateCompressor` | C | C | A | `>= 1.0`
|===

Generally speaking for a performance critical (latency or throughput)
application `LZ4` is the right choice as it gets excellent ratio per CPU
cycle spent. This is why it is the default choice in Cassandra.

For storage critical applications (disk footprint), however, `Zstd` may
be a better choice as it can get significant additional ratio to `LZ4`.

`Snappy` is kept for backwards compatibility and `LZ4` will typically be
preferable.

`Deflate` is kept for backwards compatibility and `Zstd` will typically
be preferable.

== Configuring Compression

Compression is configured on a per-table basis as an optional argument
to `CREATE TABLE` or `ALTER TABLE`. Three options are available for all
compressors:

* `class` (default: `LZ4Compressor`): specifies the compression class to
use. The two "fast" compressors are `LZ4Compressor` and
`SnappyCompressor` and the two "good" ratio compressors are
`ZstdCompressor` and `DeflateCompressor`.
* `chunk_length_in_kb` (default: `16KiB`): specifies the number of
kilobytes of data per compression chunk. The main tradeoff here is that
larger chunk sizes give compression algorithms more context and improve
their ratio, but require reads to deserialize and read more off disk.
* `crc_check_chance` (default: `1.0`): determines how likely Cassandra
is to verify the checksum on each compression chunk during reads to
protect against data corruption. Unless you have profiles indicating
this is a performance problem it is highly encouraged not to turn this
off as it is Cassandra's only protection against bitrot.

The `LZ4Compressor` supports the following additional options:

* `lz4_compressor_type` (default `fast`): specifies if we should use the
`high` (a.k.a `LZ4HC`) ratio version or the `fast` (a.k.a `LZ4`) version
of `LZ4`. The `high` mode supports a configurable level, which can allow
operators to tune the performance <-> ratio tradeoff via the
`lz4_high_compressor_level` option. Note that in `4.0` and above it may
be preferable to use the `Zstd` compressor.
* `lz4_high_compressor_level` (default `9`): A number between `1` and
`17` inclusive that represents how much CPU time to spend trying to get
more compression ratio. Generally lower levels are "faster" but they get
less ratio and higher levels are slower but get more compression ratio.

The `ZstdCompressor` supports the following options in addition:

* `compression_level` (default `3`): A number between `-131072` and `22`
inclusive that represents how much CPU time to spend trying to get more
compression ratio. The lower the level, the faster the speed (at the
cost of ratio). Values from 20 to 22 are called "ultra levels" and
should be used with caution, as they require more memory. The default of
`3` is a good choice for competing with `Deflate` ratios and `1` is a
good choice for competing with `LZ4`.

Users can set compression using the following syntax:

[source,cql]
----
CREATE TABLE keyspace.table (id int PRIMARY KEY) 
   WITH compression = {'class': 'LZ4Compressor'};
----

Or

[source,cql]
----
ALTER TABLE keyspace.table 
   WITH compression = {'class': 'LZ4Compressor', 'chunk_length_in_kb': 64, 'crc_check_chance': 0.5};
----

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

[source,cql]
----
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. Tables containing data
that has already been compressed or random data (e.g. benchmark
datasets) do not typically compress 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.
* To prevent slow compressors (`Zstd`, `Deflate`, `LZ4HC`) from blocking
flushes for too long, all three flush with the default fast `LZ4`
compressor and then rely on normal compaction to re-compress the data
into the desired compression strategy. See [.title-ref]#CASSANDRA-15379
<https://issues.apache.org/jira/browse/CASSANDRA-15379># for more
details.
* 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 `org.apache.cassandra.io.compress.ICompressor`.