elasticsearch/docs/reference/how-to/disk-usage.asciidoc

[[tune-for-disk-usage]]
== Tune for disk usage

[discrete]
=== Disable the features you do not need

By default, {es} 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,mappings>>:

[source,console]
----
PUT index
{
  "mappings": {
    "properties": {
      "foo": {
        "type": "integer",
        "index": false
      }
    }
  }
}
----

<<text,`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-field-type,`match_only_text`>> type instead. This field type
saves significant space by dropping scoring and positional information.

[discrete]
[[default-dynamic-string-mapping]]
=== Don't use default dynamic string mappings

The default <<dynamic-mapping,dynamic string mappings>> will index string fields
both as <<text,`text`>> and <<keyword,`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`:

[source,console]
--------------------------------------------------
PUT index
{
  "mappings": {
    "dynamic_templates": [
      {
        "strings": {
          "match_mapping_type": "string",
          "mapping": {
            "type": "keyword"
          }
        }
      }
    ]
  }
}
--------------------------------------------------

[discrete]
=== Watch your shard size

Larger 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 <<indices-create-index,creating indices>> with fewer primary shards, creating fewer indices (e.g. by leveraging the <<indices-rollover-index,Rollover API>>), or modifying an existing index using the <<indices-shrink-index,Shrink API>>.

Keep in mind that large shard sizes come with drawbacks, such as long full recovery times.

[discrete]
[[disable-source]]
=== Disable `_source`

The <<mapping-source-field,`_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.

[discrete]
[[best-compression]]
=== Use `best_compression`

The `_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`
<<index-codec,codec>>.

[discrete]
=== Force merge

Indices 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 <<indices-forcemerge,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`.

include::{es-repo-dir}/indices/forcemerge.asciidoc[tag=force-merge-read-only-warn]

[discrete]
=== Shrink index

The <<indices-shrink-index,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.

[discrete]
=== Use the smallest numeric type that is sufficient

The type that you pick for <<number,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.

[discrete]
=== Use index sorting to colocate similar documents

When 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 {wikipedia}/Zipf%27s_law[zipfian] distribution.

By default documents are compressed together in the order that they are added
to the index. If you enabled <<index-modules-index-sorting,index sorting>>
then instead they are compressed in sorted order. Sorting documents with similar
structure, fields, and values together should improve the compression ratio.

[discrete]
=== Put fields in the same order in documents

Due 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.

[discrete]
[[roll-up-historical-data]]
=== Roll up historical data

Keeping older data can be useful for later analysis but is often avoided due to
storage costs. You can use data rollups to summarize and store historical data
at a fraction of the raw data's storage cost. See <<xpack-rollup>>.