Tune for disk usage
editTune for disk usage
editDisable the features you do not need
editBy default, Elasticsearch indexes and adds doc values to most fields so that they
can be searched and aggregated out of the box. For instance, if you have a numeric
field called foo
that you need to run histograms on but that you never need to
filter on, you can safely disable indexing on this field in your
mappings:
response = client.indices.create( index: 'index', body: { mappings: { properties: { foo: { type: 'integer', index: false } } } } ) puts response
PUT index { "mappings": { "properties": { "foo": { "type": "integer", "index": false } } } }
text
fields store normalization factors in the index to facilitate
document scoring. If you only need matching capabilities on a text
field but do not care about the produced scores, you can use the
match_only_text
type instead. This field type
saves significant space by dropping scoring and positional information.
Don’t use default dynamic string mappings
editThe default dynamic string mappings will index string fields
both as text
and keyword
. This is wasteful if you only
need one of them. Typically an id
field will only need to be indexed as a
keyword
while a body
field will only need to be indexed as a text
field.
This can be disabled by either configuring explicit mappings on string fields
or setting up dynamic templates that will map string fields as either text
or keyword
.
For instance, here is a template that can be used in order to only map string
fields as keyword
:
response = client.indices.create( index: 'index', body: { mappings: { dynamic_templates: [ { strings: { match_mapping_type: 'string', mapping: { type: 'keyword' } } } ] } } ) puts response
PUT index { "mappings": { "dynamic_templates": [ { "strings": { "match_mapping_type": "string", "mapping": { "type": "keyword" } } } ] } }
Watch your shard size
editLarger shards are going to be more efficient at storing data. To increase the size of your shards, you can decrease the number of primary shards in an index by creating indices with fewer primary shards, creating fewer indices (e.g. by leveraging the Rollover API), or modifying an existing index using the Shrink API.
Keep in mind that large shard sizes come with drawbacks, such as long full recovery times.
Disable _source
editThe _source
field stores the original JSON body of the document. If you don’t need access to it you can disable it. However, APIs that needs access to _source
such as update, highlight and reindex won’t work.
Use best_compression
editThe _source
and stored fields can easily take a non negligible amount of disk
space. They can be compressed more aggressively by using the best_compression
codec.
Force merge
editIndices in Elasticsearch are stored in one or more shards. Each shard is a Lucene index and made up of one or more segments - the actual files on disk. Larger segments are more efficient for storing data.
The force merge API can be used to reduce the number of segments per shard. In many cases, the number of segments can be reduced to one per shard by setting max_num_segments=1
.
We recommend only force merging a read-only index (meaning the index is no longer receiving writes). When documents are updated or deleted, the old version is not immediately removed, but instead soft-deleted and marked with a "tombstone". These soft-deleted documents are automatically cleaned up during regular segment merges. But force merge can cause very large (> 5GB) segments to be produced, which are not eligible for regular merges. So the number of soft-deleted documents can then grow rapidly, resulting in higher disk usage and worse search performance. If you regularly force merge an index receiving writes, this can also make snapshots more expensive, since the new documents can’t be backed up incrementally.
Shrink index
editThe shrink API allows you to reduce the number of shards in an index. Together with the force merge API above, this can significantly reduce the number of shards and segments of an index.
Use the smallest numeric type that is sufficient
editThe type that you pick for numeric data can have a significant impact
on disk usage. In particular, integers should be stored using an integer type
(byte
, short
, integer
or long
) and floating points should either be
stored in a scaled_float
if appropriate or in the smallest type that fits the
use-case: using float
over double
, or half_float
over float
will help
save storage.
Use index sorting to colocate similar documents
editWhen Elasticsearch stores _source
, it compresses multiple documents at once
in order to improve the overall compression ratio. For instance it is very
common that documents share the same field names, and quite common that they
share some field values, especially on fields that have a low cardinality or
a zipfian distribution.
By default documents are compressed together in the order that they are added to the index. If you enabled index sorting then instead they are compressed in sorted order. Sorting documents with similar structure, fields, and values together should improve the compression ratio.
Put fields in the same order in documents
editDue to the fact that multiple documents are compressed together into blocks,
it is more likely to find longer duplicate strings in those _source
documents
if fields always occur in the same order.
Roll up historical data
editKeeping older data can be useful for later analysis but is often avoided due to storage costs. You can use downsampling to summarize and store historical data at a fraction of the raw data’s storage cost. See Downsampling a time series data stream.