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

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

By 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,mappings>>:

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

<<text,`text`>> fields store normalization factors in the index in order to be
able to score documents. If you only need matching capabilities on a `text`
field but do not care about the produced scores, you can configure elasticsearch
to not write norms to the index:

[source,js]
--------------------------------------------------
PUT index
{
  "mappings": {
    "type": {
      "properties": {
        "foo": {
          "type": "text",
          "norms": false
        }
      }
    }
  }
}
--------------------------------------------------
// CONSOLE

<<text,`text`>> fields also store frequencies and positions in the index by
default. Frequencies are used to compute scores and positions are used to run
phrase queries. If you do not need to run phrase queries, you can tell
elasticsearch to not index positions:

[source,js]
--------------------------------------------------
PUT index
{
  "mappings": {
    "type": {
      "properties": {
        "foo": {
          "type": "text",
          "index_options": "freqs"
        }
      }
    }
  }
}
--------------------------------------------------
// CONSOLE

Furthermore if you do not care about scoring either, you can configure
elasticsearch to just index matching documents for every term. You will
still be able to search on this field, but phrase queries will raise errors
and scoring will assume that terms appear only once in every document.

[source,js]
--------------------------------------------------
PUT index
{
  "mappings": {
    "type": {
      "properties": {
        "foo": {
          "type": "text",
          "norms": false,
          "index_options": "freqs"
        }
      }
    }
  }
}
--------------------------------------------------
// CONSOLE

[float]
=== 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,js]
--------------------------------------------------
PUT index
{
  "mappings": {
    "type": {
      "dynamic_templates": [
        {
          "strings": {
            "match_mapping_type": "string",
            "mapping": {
              "type": "keyword"
            }
          }
        }
      ]
    }
  }
}
--------------------------------------------------
// CONSOLE

[float]
=== Disable `_all`

The <<mapping-all-field,`_all`>> field indexes the value of all fields of a
document and can use significant space. If you never need to search against all
fields at the same time, it can be disabled.

[float]
=== 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>>.

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

When storing <<number,numeric data>>, using `float` over `double`, or `half_float`
over `float` can help save storage. This is also true for integer types, but less
since Elasticsearch will more easily compress them based on the number of bits
that they actually need.