Tune for disk usage

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:

PUT index
{
  "mappings": {
    "type": {
      "properties": {
        "foo": {
          "type": "integer",
          "index": false
        }
      }
    }
  }
}

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:

PUT index
{
  "mappings": {
    "type": {
      "properties": {
        "foo": {
          "type": "text",
          "norms": false
        }
      }
    }
  }
}

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:

PUT index
{
  "mappings": {
    "type": {
      "properties": {
        "foo": {
          "type": "text",
          "index_options": "freqs"
        }
      }
    }
  }
}

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.

PUT index
{
  "mappings": {
    "type": {
      "properties": {
        "foo": {
          "type": "text",
          "norms": false,
          "index_options": "freqs"
        }
      }
    }
  }
}

The 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:

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

The _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.

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 codec.

The 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.