In the read path, Cassandra merges data on disk (in SSTables) with data in RAM (in memtables). To avoid checking every SSTable data file for the partition being requested, Cassandra employs a data structure known as a bloom filter.
Bloom filters are a probabilistic data structure that allows Cassandra to determine one of two possible states: - The data definitely does not exist in the given file, or - The data probably exists in the given file.
While bloom filters can not guarantee that the data exists in a given
SSTable, bloom filters can be made more accurate by allowing them to
consume more RAM. Operators have the opportunity to tune this behavior
per table by adjusting the the
bloom_filter_fp_chance to a float
between 0 and 1.
The default value for
bloom_filter_fp_chance is 0.1 for tables using
LeveledCompactionStrategy and 0.01 for all other cases.
Bloom filters are stored in RAM, but are stored offheap, so operators
should not consider bloom filters when selecting the maximum heap size.
As accuracy improves (as the
bloom_filter_fp_chance gets closer to 0),
memory usage increases non-linearly - the bloom filter for
bloom_filter_fp_chance = 0.01 will require about three times as much
memory as the same table with
bloom_filter_fp_chance = 0.1.
Typical values for
bloom_filter_fp_chance are usually between 0.01
(1%) to 0.1 (10%) false-positive chance, where Cassandra may scan an
SSTable for a row, only to find that it does not exist on the disk. The
parameter should be tuned by use case:
Users with more RAM and slower disks may benefit from setting the
bloom_filter_fp_chanceto a numerically lower number (such as 0.01) to avoid excess IO operations
Users with less RAM, more dense nodes, or very fast disks may tolerate a higher
bloom_filter_fp_chancein order to save RAM at the expense of excess IO operations
In workloads that rarely read, or that only perform reads by scanning the entire data set (such as analytics workloads), setting the
bloom_filter_fp_chanceto a much higher number is acceptable.
The bloom filter false positive chance is visible in the
DESCRIBE TABLE output as the field
can change the value with an
ALTER TABLE statement: :
ALTER TABLE keyspace.table WITH bloom_filter_fp_chance=0.01
Operators should be aware, however, that this change is not immediate:
the bloom filter is calculated when the file is written, and persisted
on disk as the Filter component of the SSTable. Upon issuing an
ALTER TABLE statement, new files on disk will be written with the new
bloom_filter_fp_chance, but existing sstables will not be modified
until they are compacted - if an operator needs a change to
bloom_filter_fp_chance to take effect, they can trigger an SSTable
nodetool scrub or
nodetool upgradesstables -a, both of
which will rebuild the sstables on disk, regenerating the bloom filters
in the progress.