[[runtime]]
== Runtime fields
A _runtime field_ is a field that is evaluated at query time. Runtime fields
enable you to:

* Add fields to existing documents without reindexing your data
* Start working with your data without understanding how it’s structured
* Override the value returned from an indexed field at query time
* Define fields for a specific use without modifying the underlying schema

You access runtime fields from the search API like any other field, and {es}
sees runtime fields no differently. You can define runtime fields in the
<<runtime-mapping-fields,index mapping>> or in the
<<runtime-search-request,search request>>. Your choice, which is part of the
inherent flexibility of runtime fields.

Use the <<search-fields,`fields`>> parameter on the `_search` API to
<<runtime-retrieving-fields,retrieve the values of runtime fields>>. Runtime
fields won't display in `_source`, but the `fields` API works for all fields,
even those that were not sent as part of the original `_source`.

Runtime fields are useful when working with log data
(see <<runtime-examples,examples>>), especially when you're unsure about the
data structure. Your search speed decreases, but your index size is much
smaller and you can more quickly process logs without having to index them.

[discrete]
[[runtime-benefits]]
=== Benefits
Because runtime fields aren't indexed, adding a runtime field doesn't increase
the index size. You define runtime fields directly in the index mapping, saving
storage costs and increasing ingestion speed. You can more quickly ingest
data into the Elastic Stack and access it right away. When you define a runtime
field, you can immediately use it in search requests, aggregations, filtering,
and sorting.

If you make a runtime field an indexed field, you don't need to modify any
queries that refer to the runtime field. Better yet, you can refer to some
indices where the field is a runtime field, and other indices where the field
is an indexed field. You have the flexibility to choose which fields to index
and which ones to keep as runtime fields.

At its core, the most important benefit of runtime fields is the ability to
add fields to documents after you've ingested them. This capability simplifies
mapping decisions because you don't have to decide how to parse your data up
front, and can use runtime fields to amend the mapping at any time. Using
runtime fields allows for a smaller index and faster ingest time, which
combined use less resources and reduce your operating costs.

[discrete]
[[runtime-incentives]]
=== Incentives
Runtime fields can replace many of the ways you can use scripting with the
`_search` API. How you use a runtime field is impacted by the number of
documents that the included script runs against. For example, if you're using
the `fields` parameter on the `_search` API to 
<<runtime-retrieving-fields,retrieve the values of a runtime field>>, the script
runs only against the top hits just like script fields do.

You can use <<script-fields,script fields>> to access values in `_source` and
return calculated values based on a script valuation. Runtime fields have these
same capabilities, but provide greater flexibility because you can query and
aggregate on runtime fields in a search request. Script fields can only fetch
values.

Similarly, you could write a <<query-dsl-script-query,script query>> that
filters documents in a search request based on a script. Runtime fields provide
a very similar feature that is more flexible. You write a script to create
field values and they are available everywhere, such as
<<search-fields,`fields`>>, <<query-dsl, all queries>>, and
<<search-aggregations, aggregations>>.

You can also use scripts to <<script-based-sorting,sort search results>>, but
that same script works exactly the same in a runtime field.

If you move a script from any of these sections in a search request to a
runtime field that is computing values from the same number of documents, the
performance should be about the same. The performance for these features is
largely dependent upon the calculations that the included script is running and
how many documents the script runs against. 

[discrete]
[[runtime-compromises]]
=== Compromises
Runtime fields use less disk space and provide flexibility in how you access
your data, but can impact search performance based on the computation defined in
the runtime script.

To balance search performance and flexibility, index fields that you'll
frequently search for and filter on, such as a timestamp. {es} automatically
uses these indexed fields first when running a query, resulting in a fast 
response time. You can then use runtime fields to limit the number of fields 
that {es} needs to calculate values for. Using indexed fields in tandem with 
runtime fields provides flexibility in the data that you index and how you
define queries for other fields.

Use the <<async-search,asynchronous search API>> to run searches that include
runtime fields. This method of search helps to offset the performance impacts
of computing values for runtime fields in each document containing that field.
If the query can't return the result set synchronously, you'll get results
asynchronously as they become available.

IMPORTANT: Queries against runtime fields are considered expensive. If
<<query-dsl-allow-expensive-queries,`search.allow_expensive_queries`>> is set
to `false`, expensive queries are not allowed and {es} will reject any queries
against runtime fields.

[[runtime-mapping-fields]]
=== Map a runtime field
You map runtime fields by adding a `runtime` section under the mapping
definition and defining
<<modules-scripting-using,a Painless script>>. This script has access to the
entire context of a document, including the original `_source` and any mapped
fields plus their values. At query time, the script runs and generates values
for each scripted field that is required for the query.

.Emitting runtime field values
****
When defining a Painless script to use with runtime fields, you must include
the {painless}/painless-runtime-fields-context.html[`emit` method] to emit
calculated values.
****

For example, the script in the following request calculates the day of the week
from the `@timestamp` field, which is defined as a `date` type. The script
calculates the day of the week based on the value of `timestamp`, and uses
`emit` to return the calculated value.

[source,console]
----
PUT my-index-000001/
{
  "mappings": {
    "runtime": {
      "day_of_week": {
        "type": "keyword",
        "script": {
          "source": "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))"
        }
      }
    },
    "properties": {
      "@timestamp": {"type": "date"}
    }
  }
}
----

The `runtime` section can be any of these data types:

// tag::runtime-data-types[]
* `boolean`
* `composite`
* `date`
* `double`
* `geo_point`
* `ip`
* `keyword`
* `long`
// end::runtime-data-types[]

Runtime fields with a `type` of `date` can accept the
<<mapping-date-format,`format`>> parameter exactly as the `date` field type.

If <<dynamic-field-mapping,dynamic field mapping>> is enabled where the
`dynamic` parameter is set to `runtime`, new fields are automatically added to
the index mapping as runtime fields:

[source,console]
----
PUT my-index-000001
{
  "mappings": {
    "dynamic": "runtime",
    "properties": {
      "@timestamp": {
        "type": "date"
      }
    }
  }
}
----

[[runtime-fields-scriptless]]
==== Define runtime fields without a script
Runtime fields typically include a Painless script that manipulates data in some
way. However, there are instances where you might define a runtime field
_without_ a script. For example, if you want to retrieve a single field from `_source` without making changes, you don't need a script. You can just create
a runtime field without a script, such as `day_of_week`:

[source,console]
----
PUT my-index-000001/
{
  "mappings": {
    "runtime": {
      "day_of_week": {
        "type": "keyword"
      }
    }
  }
}
----

When no script is provided, {es} implicitly looks in `_source` at query time
for a field with the same name as the runtime field, and returns a value if one
exists. If a field with the same name doesn’t exist, the response doesn't
include any values for that runtime field.

In most cases, retrieve field values through
<<doc-values,`doc_values`>> whenever possible. Accessing `doc_values` with a
runtime field is faster than retrieving values from `_source` because of how
data is loaded from Lucene.

However, there are cases where retrieving fields from `_source` is necessary.
For example, `text` fields do not have `doc_values` available by default, so you
have to retrieve values from `_source`. In other instances, you might choose to
disable `doc_values` on a specific field.

NOTE: You can alternatively prefix the field you want to retrieve values for
with `params._source` (such as `params._source.day_of_week`). For simplicity,
defining a runtime field in the mapping definition without a script is the
recommended option, whenever possible.

[[runtime-updating-scripts]]
==== Updating and removing runtime fields

You can update or remove runtime fields at any time. To replace an existing
runtime field, add a new runtime field to the mappings with the same name. To
remove a runtime field from the mappings, set the value of the runtime field to
`null`:

[source,console]
----
PUT my-index-000001/_mapping
{
 "runtime": {
   "day_of_week": null
 }
}
----
//TEST[continued]

.Downstream impacts
****
Updating or removing a runtime field while a dependent query is running can return
inconsistent results. Each shard might have access to different versions of the
script, depending on when the mapping change takes effect.

WARNING: Existing queries or visualizations in {kib} that rely on runtime fields can
fail if you remove or update the field. For example, a bar chart visualization
that uses a runtime field of type `ip` will fail if the type is changed
to `boolean`, or if the runtime field is removed.
****

[[runtime-search-request]]
=== Define runtime fields in a search request
You can specify a `runtime_mappings` section in a search request to create
runtime fields that exist only as part of the query. You specify a script
as part of the `runtime_mappings` section, just as you would if
<<runtime-mapping-fields,adding a runtime field to the mappings>>.

Defining a runtime field in a search request uses the same format as defining
a runtime field in the index mapping. Just copy the field definition from
the `runtime_mappings` in the search request to the `runtime` section of the
index mapping.

The following search request adds a `day_of_week` field to the
`runtime_mappings` section. The field values will be calculated dynamically,
and only within the context of this search request:

[source,console]
----
GET my-index-000001/_search
{
  "runtime_mappings": {
    "day_of_week": {
      "type": "keyword",
      "script": {
        "source": "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))"
      }
    }
  },
  "aggs": {
    "day_of_week": {
      "terms": {
        "field": "day_of_week"
      }
    }
  }
}
----
//TEST[continued]

[[runtime-search-request-examples]]
[discrete]
=== Create runtime fields that use other runtime fields
You can even define runtime fields in a search request that return values from
other runtime fields. For example, let's say you bulk index some sensor data:

[source,console]
----
POST my-index-000001/_bulk?refresh=true
{"index":{}}
{"@timestamp":1516729294000,"model_number":"QVKC92Q","measures":{"voltage":"5.2","start": "300","end":"8675309"}}
{"index":{}}
{"@timestamp":1516642894000,"model_number":"QVKC92Q","measures":{"voltage":"5.8","start": "300","end":"8675309"}}
{"index":{}}
{"@timestamp":1516556494000,"model_number":"QVKC92Q","measures":{"voltage":"5.1","start": "300","end":"8675309"}}
{"index":{}}
{"@timestamp":1516470094000,"model_number":"QVKC92Q","measures":{"voltage":"5.6","start": "300","end":"8675309"}}
{"index":{}}
{"@timestamp":1516383694000,"model_number":"HG537PU","measures":{"voltage":"4.2","start": "400","end":"8625309"}}
{"index":{}}
{"@timestamp":1516297294000,"model_number":"HG537PU","measures":{"voltage":"4.0","start": "400","end":"8625309"}}
----

You realize after indexing that your numeric data was mapped as type `text`.
You want to aggregate on the `measures.start` and `measures.end` fields, but
the aggregation fails because you can't aggregate on fields of type `text`.
Runtime fields to the rescue! You can add runtime fields with the same name as
your indexed fields and modify the data type:

[source,console]
----
PUT my-index-000001/_mapping
{
  "runtime": {
    "measures.start": {
      "type": "long"
    },
    "measures.end": {
      "type": "long"
    }
  }
}
----
// TEST[continued]

Runtime fields take precedence over fields defined with the same name in the
index mappings. This flexibility allows you to shadow existing fields and
calculate a different value, without modifying the field itself. If you made a
mistake in your index mapping, you can use runtime fields to calculate values
that <<runtime-override-values,override values>> in the mapping during the
search request.

Now, you can easily run an
<<search-aggregations-metrics-avg-aggregation,average aggregation>> on the
`measures.start` and `measures.end` fields:

[source,console]
----
GET my-index-000001/_search
{
  "aggs": {
    "avg_start": {
      "avg": {
        "field": "measures.start"
      }
    },
    "avg_end": {
      "avg": {
        "field": "measures.end"
      }
    }
  }
}
----
// TEST[continued]
// TEST[s/_search/_search\?filter_path=aggregations/]

The response includes the aggregation results without changing the values for
the underlying data:

[source,console-result]
----
{
  "aggregations" : {
    "avg_start" : {
      "value" : 333.3333333333333
    },
    "avg_end" : {
      "value" : 8658642.333333334
    }
  }
}
----

Further, you can define a runtime field as part of a search query that
calculates a value, and then run a
<<search-aggregations-metrics-stats-aggregation,stats aggregation>> on that
field _in the same query_.

The `duration` runtime field doesn't exist in the index mapping, but we can
still search and aggregate on that field. The following query returns the
calculated value for the `duration` field and runs a stats aggregation to
compute statistics over numeric values extracted from the aggregated documents.

[source,console]
----
GET my-index-000001/_search
{
  "runtime_mappings": {
    "duration": {
      "type": "long",
      "script": {
        "source": """
          emit(doc['measures.end'].value - doc['measures.start'].value);
          """
      }
    }
  },
  "aggs": {
    "duration_stats": {
      "stats": {
        "field": "duration"
      }
    }
  }
}
----
// TEST[continued]
// TEST[s/_search/_search\?filter_path=aggregations/]

Even though the `duration` runtime field only exists in the context of a search
query, you can search and aggregate on that field. This flexibility is
incredibly powerful, enabling you to rectify mistakes in your index mappings
and dynamically complete calculations all within a single search request.

[source,console-result]
----
{
  "aggregations" : {
    "duration_stats" : {
      "count" : 6,
      "min" : 8624909.0,
      "max" : 8675009.0,
      "avg" : 8658309.0,
      "sum" : 5.1949854E7
    }
  }
}
----

[[runtime-override-values]]
=== Override field values at query time
If you create a runtime field with the same name as a field that
already exists in the mapping, the runtime field shadows the mapped field. At
query time, {es} evaluates the runtime field, calculates a value based on the
script, and returns the value as part of the query. Because the runtime field
shadows the mapped field, you can override the value returned in search without
modifying the mapped field.

For example, let's say you indexed the following documents into `my-index-000001`:

[source,console]
----
POST my-index-000001/_bulk?refresh=true
{"index":{}}
{"@timestamp":1516729294000,"model_number":"QVKC92Q","measures":{"voltage":5.2}}
{"index":{}}
{"@timestamp":1516642894000,"model_number":"QVKC92Q","measures":{"voltage":5.8}}
{"index":{}}
{"@timestamp":1516556494000,"model_number":"QVKC92Q","measures":{"voltage":5.1}}
{"index":{}}
{"@timestamp":1516470094000,"model_number":"QVKC92Q","measures":{"voltage":5.6}}
{"index":{}}
{"@timestamp":1516383694000,"model_number":"HG537PU","measures":{"voltage":4.2}}
{"index":{}}
{"@timestamp":1516297294000,"model_number":"HG537PU","measures":{"voltage":4.0}}
----

You later realize that the `HG537PU` sensors aren't reporting their true
voltage. The indexed values are supposed to be 1.7 times higher than
the reported values! Instead of reindexing your data, you can define a script in
the `runtime_mappings` section of the `_search` request to shadow the `voltage`
field and calculate a new value at query time.

If you search for documents where the model number matches `HG537PU`:

[source,console]
----
GET my-index-000001/_search
{
  "query": {
    "match": {
      "model_number": "HG537PU"
    }
  }
}
----
//TEST[continued]

The response includes indexed values for documents matching model number
`HG537PU`:

[source,console-result]
----
{
  ...
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0296195,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "F1BeSXYBg_szTodcYCmk",
        "_score" : 1.0296195,
        "_source" : {
          "@timestamp" : 1516383694000,
          "model_number" : "HG537PU",
          "measures" : {
            "voltage" : 4.2
          }
        }
      },
      {
        "_index" : "my-index-000001",
        "_id" : "l02aSXYBkpNf6QRDO62Q",
        "_score" : 1.0296195,
        "_source" : {
          "@timestamp" : 1516297294000,
          "model_number" : "HG537PU",
          "measures" : {
            "voltage" : 4.0
          }
        }
      }
    ]
  }
}
----
// TESTRESPONSE[s/\.\.\./"took" : $body.took,"timed_out" : $body.timed_out,"_shards" : $body._shards,/]
// TESTRESPONSE[s/"_id" : "F1BeSXYBg_szTodcYCmk"/"_id": $body.hits.hits.0._id/]
// TESTRESPONSE[s/"_id" : "l02aSXYBkpNf6QRDO62Q"/"_id": $body.hits.hits.1._id/]

The following request defines a runtime field where the script evaluates the
`model_number` field where the value is `HG537PU`. For each match, the script
multiplies the value for the `voltage` field by `1.7`.

Using the <<search-fields,`fields`>> parameter on the `_search` API, you can
retrieve the value that the script calculates for the `measures.voltage` field
for documents matching the search request:

[source,console]
----
POST my-index-000001/_search
{
  "runtime_mappings": {
    "measures.voltage": {
      "type": "double",
      "script": {
        "source":
        """if (doc['model_number.keyword'].value.equals('HG537PU'))
        {emit(1.7 * params._source['measures']['voltage']);}
        else{emit(params._source['measures']['voltage']);}"""
      }
    }
  },
  "query": {
    "match": {
      "model_number": "HG537PU"
    }
  },
  "fields": ["measures.voltage"]
}
----
//TEST[continued]

Looking at the response, the calculated values for `measures.voltage` on each
result are `7.14` and `6.8`. That's more like it! The runtime field calculated
this value as part of the search request without modifying the mapped value,
which still returns in the response:

[source,console-result]
----
{
  ...
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0296195,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "F1BeSXYBg_szTodcYCmk",
        "_score" : 1.0296195,
        "_source" : {
          "@timestamp" : 1516383694000,
          "model_number" : "HG537PU",
          "measures" : {
            "voltage" : 4.2
          }
        },
        "fields" : {
          "measures.voltage" : [
            7.14
          ]
        }
      },
      {
        "_index" : "my-index-000001",
        "_id" : "l02aSXYBkpNf6QRDO62Q",
        "_score" : 1.0296195,
        "_source" : {
          "@timestamp" : 1516297294000,
          "model_number" : "HG537PU",
          "measures" : {
            "voltage" : 4.0
          }
        },
        "fields" : {
          "measures.voltage" : [
            6.8
          ]
        }
      }
    ]
  }
}
----
// TESTRESPONSE[s/\.\.\./"took" : $body.took,"timed_out" : $body.timed_out,"_shards" : $body._shards,/]
// TESTRESPONSE[s/"_id" : "F1BeSXYBg_szTodcYCmk"/"_id": $body.hits.hits.0._id/]
// TESTRESPONSE[s/"_id" : "l02aSXYBkpNf6QRDO62Q"/"_id": $body.hits.hits.1._id/]

[[runtime-retrieving-fields]]
=== Retrieve a runtime field

Use the <<search-fields,`fields`>> parameter on the `_search` API to retrieve
the values of runtime fields. Runtime fields won't display in `_source`, but
the `fields` API works for all fields, even those that were not sent as part of
the original `_source`.

[[runtime-define-field-dayofweek]]
==== Define a runtime field to calculate the day of week
For example, the following request adds a runtime field called `day_of_week`.
The runtime field includes a script that calculates the day of the week based
on the value of the `@timestamp` field. We'll include `"dynamic":"runtime"` in
the request so that new fields are added to the mapping as runtime fields.

[source,console]
----
PUT my-index-000001/
{
  "mappings": {
    "dynamic": "runtime",
    "runtime": {
      "day_of_week": {
        "type": "keyword",
        "script": {
          "source": "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT))"
        }
      }
    },
    "properties": {
      "@timestamp": {"type": "date"}
    }
  }
}
----

[[runtime-ingest-data]]
==== Ingest some data
Let's ingest some sample data, which will result in two indexed fields:
`@timestamp` and `message`.

[source,console]
----
POST /my-index-000001/_bulk?refresh
{ "index": {}}
{ "@timestamp": "2020-06-21T15:00:01-05:00", "message" : "211.11.9.0 - - [2020-06-21T15:00:01-05:00] \"GET /english/index.html HTTP/1.0\" 304 0"}
{ "index": {}}
{ "@timestamp": "2020-06-21T15:00:01-05:00", "message" : "211.11.9.0 - - [2020-06-21T15:00:01-05:00] \"GET /english/index.html HTTP/1.0\" 304 0"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:30:17-05:00", "message" : "40.135.0.0 - - [2020-04-30T14:30:17-05:00] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:30:53-05:00", "message" : "232.0.0.0 - - [2020-04-30T14:30:53-05:00] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:31:12-05:00", "message" : "26.1.0.0 - - [2020-04-30T14:31:12-05:00] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:31:19-05:00", "message" : "247.37.0.0 - - [2020-04-30T14:31:19-05:00] \"GET /french/splash_inet.html HTTP/1.0\" 200 3781"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:31:27-05:00", "message" : "252.0.0.0 - - [2020-04-30T14:31:27-05:00] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:31:29-05:00", "message" : "247.37.0.0 - - [2020-04-30T14:31:29-05:00] \"GET /images/hm_brdl.gif HTTP/1.0\" 304 0"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:31:29-05:00", "message" : "247.37.0.0 - - [2020-04-30T14:31:29-05:00] \"GET /images/hm_arw.gif HTTP/1.0\" 304 0"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:31:32-05:00", "message" : "247.37.0.0 - - [2020-04-30T14:31:32-05:00] \"GET /images/nav_bg_top.gif HTTP/1.0\" 200 929"}
{ "index": {}}
{ "@timestamp": "2020-04-30T14:31:43-05:00", "message" : "247.37.0.0 - - [2020-04-30T14:31:43-05:00] \"GET /french/images/nav_venue_off.gif HTTP/1.0\" 304 0"}
----
//TEST[continued]

[[runtime-search-dayofweek]]
==== Search for the calculated day of week
The following request uses the search API to retrieve the `day_of_week` field
that the original request defined as a runtime field in the mapping. The value
for this field is calculated dynamically at query time without reindexing
documents or indexing the `day_of_week` field. This flexibility allows you to
modify the mapping without changing any field values.

[source,console]
----
GET my-index-000001/_search
{
  "fields": [
    "@timestamp",
    "day_of_week"
  ],
  "_source": false
}
----
// TEST[continued]

The previous request returns the `day_of_week` field for all matching documents.
We can define another runtime field called `client_ip` that also operates on
the `message` field and will further refine the query:

[source,console]
----
PUT /my-index-000001/_mapping
{
  "runtime": {
    "client_ip": {
      "type": "ip",
      "script" : {
      "source" : "String m = doc[\"message\"].value; int end = m.indexOf(\" \"); emit(m.substring(0, end));"
      }
    }
  }
}
----
//TEST[continued]

Run another query, but search for a specific IP address using the `client_ip`
runtime field:

[source,console]
----
GET my-index-000001/_search
{
  "size": 1,
  "query": {
    "match": {
      "client_ip": "211.11.9.0"
    }
  },
  "fields" : ["*"]
}
----
//TEST[continued]

This time, the response includes only two hits. The value for `day_of_week`
(`Sunday`) was calculated at query time using the runtime script defined in the
mapping, and the result includes only documents matching the `211.11.9.0` IP
address.

[source,console-result]
----
{
  ...
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "oWs5KXYB-XyJbifr9mrz",
        "_score" : 1.0,
        "_source" : {
          "@timestamp" : "2020-06-21T15:00:01-05:00",
          "message" : "211.11.9.0 - - [2020-06-21T15:00:01-05:00] \"GET /english/index.html HTTP/1.0\" 304 0"
        },
        "fields" : {
          "@timestamp" : [
            "2020-06-21T20:00:01.000Z"
          ],
          "client_ip" : [
            "211.11.9.0"
          ],
          "message" : [
            "211.11.9.0 - - [2020-06-21T15:00:01-05:00] \"GET /english/index.html HTTP/1.0\" 304 0"
          ],
          "day_of_week" : [
            "Sunday"
          ]
        }
      }
    ]
  }
}
----
// TESTRESPONSE[s/\.\.\./"took" : $body.took,"timed_out" : $body.timed_out,"_shards" : $body._shards,/]
// TESTRESPONSE[s/"_id" : "oWs5KXYB-XyJbifr9mrz"/"_id": $body.hits.hits.0._id/]
// TESTRESPONSE[s/"day_of_week" : \[\n\s+"Sunday"\n\s\]/"day_of_week": $body.hits.hits.0.fields.day_of_week/]

[[runtime-indexed]]
=== Index a runtime field
Runtime fields are defined by the context where they run. For example, you
can define runtime fields in the
<<runtime-search-request,context of a search query>> or within the
<<runtime-mapping-fields,`runtime` section>> of an index mapping. If you
decide to index a runtime field for greater performance, just move the full
runtime field definition (including the script) to the context of an index
mapping. {es} automatically uses these indexed fields to drive queries,
resulting in a fast response time. This capability means you can write a 
script only once, and apply it to any context that supports runtime fields.

NOTE: Indexing a `composite` runtime field is currently not supported.

You can then use runtime fields to limit the number of fields that {es} needs 
to calculate values for. Using indexed fields in tandem with runtime fields 
provides flexibility in the data that you index and how you define queries for 
other fields.

IMPORTANT: After indexing a runtime field, you cannot update the included
script. If you need to change the script, create a new field with the updated
script.

For example, let's say your company wants to replace some old pressure
valves. The connected sensors are only capable of reporting a fraction of
the true readings. Rather than outfit the pressure valves with new sensors,
you decide to calculate the values based on reported readings. Based on the
reported data, you define the following fields in your mapping for
`my-index-000001`:

[source,console]
----
PUT my-index-000001/
{
  "mappings": {
    "properties": {
      "timestamp": {
        "type": "date"
      },
      "temperature": {
        "type": "long"
      },
      "voltage": {
        "type": "double"
      },
      "node": {
        "type": "keyword"
      }
    }
  }
}
----

You then bulk index some sample data from your sensors. This data includes
`voltage` readings for each sensor:

[source,console]
----
POST my-index-000001/_bulk?refresh=true
{"index":{}}
{"timestamp": 1516729294000, "temperature": 200, "voltage": 5.2, "node": "a"}
{"index":{}}
{"timestamp": 1516642894000, "temperature": 201, "voltage": 5.8, "node": "b"}
{"index":{}}
{"timestamp": 1516556494000, "temperature": 202, "voltage": 5.1, "node": "a"}
{"index":{}}
{"timestamp": 1516470094000, "temperature": 198, "voltage": 5.6, "node": "b"}
{"index":{}}
{"timestamp": 1516383694000, "temperature": 200, "voltage": 4.2, "node": "c"}
{"index":{}}
{"timestamp": 1516297294000, "temperature": 202, "voltage": 4.0, "node": "c"}
----
// TEST[continued]

After talking to a few site engineers, you realize that the sensors should
be reporting at least _double_ the current values, but potentially higher.
You create a runtime field named `voltage_corrected` that retrieves the current
voltage and multiplies it by `2`:

[source,console]
----
PUT my-index-000001/_mapping
{
  "runtime": {
    "voltage_corrected": {
      "type": "double",
      "script": {
        "source": """
        emit(doc['voltage'].value * params['multiplier'])
        """,
        "params": {
          "multiplier": 2
        }
      }
    }
  }
}
----
// TEST[continued]

You retrieve the calculated values using the <<search-fields,`fields`>>
parameter on the `_search` API:

[source,console]
----
GET my-index-000001/_search
{
  "fields": [
    "voltage_corrected",
    "node"
  ],
  "size": 2
}
----
// TEST[continued]
// TEST[s/_search/_search\?filter_path=hits/]

//
////
[source,console-result]
----
{
  "hits" : {
    "total" : {
      "value" : 6,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "z4TCrHgBdg9xpPrU6z9k",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : 1516729294000,
          "temperature" : 200,
          "voltage" : 5.2,
          "node" : "a"
        },
        "fields" : {
          "voltage_corrected" : [
            10.4
          ],
          "node" : [
            "a"
          ]
        }
      },
      {
        "_index" : "my-index-000001",
        "_id" : "0ITCrHgBdg9xpPrU6z9k",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : 1516642894000,
          "temperature" : 201,
          "voltage" : 5.8,
          "node" : "b"
        },
        "fields" : {
          "voltage_corrected" : [
            11.6
          ],
          "node" : [
            "b"
          ]
        }
      }
    ]
  }
}
----
// TESTRESPONSE[s/"_id" : "z4TCrHgBdg9xpPrU6z9k"/"_id": $body.hits.hits.0._id/]
// TESTRESPONSE[s/"_id" : "0ITCrHgBdg9xpPrU6z9k"/"_id": $body.hits.hits.1._id/]
////
//

After reviewing the sensor data and running some tests, you determine that the
multiplier for reported sensor data should be `4`. To gain greater performance,
you decide to index the `voltage_corrected` runtime field with the new
`multiplier` parameter.

In a new index named `my-index-000001`, copy the `voltage_corrected` runtime
field definition into the mappings of the new index. It's that simple! You can
add an optional parameter named `on_script_error` that determines whether to
reject the entire document if the script throws an error at index time
(default).

[source,console]
----
PUT my-index-000001/
{
  "mappings": {
    "properties": {
      "timestamp": {
        "type": "date"
      },
      "temperature": {
        "type": "long"
      },
      "voltage": {
        "type": "double"
      },
      "node": {
        "type": "keyword"
      },
      "voltage_corrected": {
        "type": "double",
        "on_script_error": "fail", <1>
        "script": {
          "source": """
        emit(doc['voltage'].value * params['multiplier'])
        """,
          "params": {
            "multiplier": 4
          }
        }
      }
    }
  }
}
----
<1> Causes the entire document to be rejected if the script throws an error at
index time. Setting the value to `ignore` will register the field in the
document’s `_ignored` metadata field and continue indexing.

Bulk index some sample data from your sensors into the `my-index-000001` index:

[source,console]
----
POST my-index-000001/_bulk?refresh=true
{ "index": {}}
{ "timestamp": 1516729294000, "temperature": 200, "voltage": 5.2, "node": "a"}
{ "index": {}}
{ "timestamp": 1516642894000, "temperature": 201, "voltage": 5.8, "node": "b"}
{ "index": {}}
{ "timestamp": 1516556494000, "temperature": 202, "voltage": 5.1, "node": "a"}
{ "index": {}}
{ "timestamp": 1516470094000, "temperature": 198, "voltage": 5.6, "node": "b"}
{ "index": {}}
{ "timestamp": 1516383694000, "temperature": 200, "voltage": 4.2, "node": "c"}
{ "index": {}}
{ "timestamp": 1516297294000, "temperature": 202, "voltage": 4.0, "node": "c"}
----
// TEST[continued]

You can now retrieve calculated values in a search query, and find documents
based on precise values. The following range query returns all documents where
the calculated `voltage_corrected` is greater than or equal to `16`, but less
than or equal to `20`. Again, use the <<search-fields,`fields`>> parameter on
the `_search` API to retrieve the fields you want:

[source,console]
----
POST my-index-000001/_search
{
  "query": {
    "range": {
      "voltage_corrected": {
        "gte": 16,
        "lte": 20,
        "boost": 1.0
      }
    }
  },
  "fields": [
    "voltage_corrected", "node"]
}
----
// TEST[continued]
// TEST[s/_search/_search\?filter_path=hits/]

The response includes the `voltage_corrected` field for the documents that
match the range query, based on the calculated value of the included script:

[source,console-result]
----
{
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "yoSLrHgBdg9xpPrUZz_P",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : 1516383694000,
          "temperature" : 200,
          "voltage" : 4.2,
          "node" : "c"
        },
        "fields" : {
          "voltage_corrected" : [
            16.8
          ],
          "node" : [
            "c"
          ]
        }
      },
      {
        "_index" : "my-index-000001",
        "_id" : "y4SLrHgBdg9xpPrUZz_P",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : 1516297294000,
          "temperature" : 202,
          "voltage" : 4.0,
          "node" : "c"
        },
        "fields" : {
          "voltage_corrected" : [
            16.0
          ],
          "node" : [
            "c"
          ]
        }
      }
    ]
  }
}
----
// TESTRESPONSE[s/"_id" : "yoSLrHgBdg9xpPrUZz_P"/"_id": $body.hits.hits.0._id/]
// TESTRESPONSE[s/"_id" : "y4SLrHgBdg9xpPrUZz_P"/"_id": $body.hits.hits.1._id/]

[[runtime-examples]]
=== Explore your data with runtime fields
Consider a large set of log data that you want to extract fields from.
Indexing the data is time consuming and uses a lot of disk space, and you just
want to explore the data structure without committing to a schema up front.

You know that your log data contains specific fields that you want to extract.
In this case, we want to focus on the `@timestamp` and `message` fields. By
using runtime fields, you can define scripts to calculate values at search
time for these fields.

[[runtime-examples-define-fields]]
==== Define indexed fields as a starting point

You can start with a simple example by adding the `@timestamp` and `message`
fields to the `my-index-000001` mapping as indexed fields. To remain flexible, use
`wildcard` as the field type for `message`:

[source,console]
----
PUT /my-index-000001/
{
  "mappings": {
    "properties": {
      "@timestamp": {
        "format": "strict_date_optional_time||epoch_second",
        "type": "date"
      },
      "message": {
        "type": "wildcard"
      }
    }
  }
}
----

[[runtime-examples-ingest-data]]
==== Ingest some data
After mapping the fields you want to retrieve, index a few records from
your log data into {es}. The following request uses the <<docs-bulk,bulk API>>
to index raw log data into `my-index-000001`. Instead of indexing all of your log
data, you can use a small sample to experiment with runtime fields.

The final document is not a valid Apache log format, but we can account for
that scenario in our script.

[source,console]
----
POST /my-index-000001/_bulk?refresh
{"index":{}}
{"timestamp":"2020-04-30T14:30:17-05:00","message":"40.135.0.0 - - [30/Apr/2020:14:30:17 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"}
{"index":{}}
{"timestamp":"2020-04-30T14:30:53-05:00","message":"232.0.0.0 - - [30/Apr/2020:14:30:53 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"}
{"index":{}}
{"timestamp":"2020-04-30T14:31:12-05:00","message":"26.1.0.0 - - [30/Apr/2020:14:31:12 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"}
{"index":{}}
{"timestamp":"2020-04-30T14:31:19-05:00","message":"247.37.0.0 - - [30/Apr/2020:14:31:19 -0500] \"GET /french/splash_inet.html HTTP/1.0\" 200 3781"}
{"index":{}}
{"timestamp":"2020-04-30T14:31:22-05:00","message":"247.37.0.0 - - [30/Apr/2020:14:31:22 -0500] \"GET /images/hm_nbg.jpg HTTP/1.0\" 304 0"}
{"index":{}}
{"timestamp":"2020-04-30T14:31:27-05:00","message":"252.0.0.0 - - [30/Apr/2020:14:31:27 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"}
{"index":{}}
{"timestamp":"2020-04-30T14:31:28-05:00","message":"not a valid apache log"}
----
// TEST[continued]

At this point, you can view how {es} stores your raw data.

[source,console]
----
GET /my-index-000001
----
// TEST[continued]

The mapping contains two fields: `@timestamp` and `message`.

[source,console-result]
----
{
  "my-index-000001" : {
    "aliases" : { },
    "mappings" : {
      "properties" : {
        "@timestamp" : {
          "type" : "date",
          "format" : "strict_date_optional_time||epoch_second"
        },
        "message" : {
          "type" : "wildcard"
        },
        "timestamp" : {
          "type" : "date"
        }
      }
    },
    ...
  }
}
----
// TESTRESPONSE[s/\.\.\./"settings": $body.my-index-000001.settings/]

[[runtime-examples-grok]]
==== Define a runtime field with a grok pattern
If you want to retrieve results that include `clientip`, you can add that
field as a runtime field in the mapping. The following runtime script defines a
<<grok,grok pattern>> that extracts structured fields out of a single text
field within a document. A grok pattern is like a regular expression that
supports aliased expressions that you can reuse.

The script matches on the `%{COMMONAPACHELOG}` log pattern, which understands
the structure of Apache logs. If the pattern matches, the script emits the
value of the matching IP address. If the pattern doesn't match
(`clientip != null`), the script just returns the field value without crashing.

[source,console]
----
PUT my-index-000001/_mappings
{
  "runtime": {
    "http.client_ip": {
      "type": "ip",
      "script": """
        String clientip=grok('%{COMMONAPACHELOG}').extract(doc["message"].value)?.clientip;
        if (clientip != null) emit(clientip); <1>
      """
    }
  }
}
----
// TEST[continued]
<1> This condition ensures that the script doesn't crash even if the pattern of
the message doesn't match.

Alternatively, you can define the same runtime field but in the context of a
search request. The runtime definition and the script are exactly the same as
the one defined previously in the index mapping. Just copy that definition into
the search request under the `runtime_mappings` section and include a query
that matches on the runtime field. This query returns the same results as if
you defined a search query for the `http.clientip` runtime field in your index
mappings, but only in the context of this specific search:

[source,console]
----
GET my-index-000001/_search
{
  "runtime_mappings": {
    "http.clientip": {
      "type": "ip",
      "script": """
        String clientip=grok('%{COMMONAPACHELOG}').extract(doc["message"].value)?.clientip;
        if (clientip != null) emit(clientip);
      """
    }
  },
  "query": {
    "match": {
      "http.clientip": "40.135.0.0"
    }
  },
  "fields" : ["http.clientip"]
}
----
// TEST[continued]

[[runtime-examples-grok-composite]]
==== Define a composite runtime field
You can also define a _composite_ runtime field to emit multiple fields from a 
single script. You can define a set of typed subfields and emit a map of 
values. At search time, each subfield retrieves the value associated with 
their name in the map. This means that you only need to specify your grok
pattern one time and can return multiple values:

[source,console]
----
PUT my-index-000001/_mappings
{
  "runtime": {
    "http": {
      "type": "composite",
      "script": "emit(grok(\"%{COMMONAPACHELOG}\").extract(doc[\"message\"].value))",
      "fields": {
        "clientip": {
          "type": "ip"
        },
        "verb": {
          "type": "keyword"
        },
        "response": {
          "type": "long"
        }
      }
    }
  }
}
----
// TEST[continued]

[[runtime-examples-grok-ip]]
===== Search for a specific IP address
Using the `http.clientip` runtime field, you can define a simple query to run a
search for a specific IP address and return all related fields.

[source,console]
----
GET my-index-000001/_search
{
  "query": {
    "match": {
      "http.clientip": "40.135.0.0"
    }
  },
  "fields" : ["*"]
}
----
// TEST[continued]

The API returns the following result. Because `http` is a `composite` runtime 
field, the response includes each of the sub-fields under `fields`, including
any associated values that match the query. Without building your data structure 
in advance, you can search and explore your data in meaningful ways to
experiment and determine which fields to index. 

[source,console-result]
----
{
  ...
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "sRVHBnwBB-qjgFni7h_O",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : "2020-04-30T14:30:17-05:00",
          "message" : "40.135.0.0 - - [30/Apr/2020:14:30:17 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"
        },
        "fields" : {
          "http.verb" : [
            "GET"
          ],
          "http.clientip" : [
            "40.135.0.0"
          ],
          "http.response" : [
            200
          ],
          "message" : [
            "40.135.0.0 - - [30/Apr/2020:14:30:17 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"
          ],
          "http.client_ip" : [
            "40.135.0.0"
          ],
          "timestamp" : [
            "2020-04-30T19:30:17.000Z"
          ]
        }
      }
    ]
  }
}
----
// TESTRESPONSE[s/\.\.\./"took" : $body.took,"timed_out" : $body.timed_out,"_shards" : $body._shards,/]
// TESTRESPONSE[s/"_id" : "sRVHBnwBB-qjgFni7h_O"/"_id": $body.hits.hits.0._id/]

Also, remember that `if` statement in the script?

[source,painless]
----
if (clientip != null) emit(clientip);
----

If the script didn't include this condition, the query would fail on any shard
that doesn't match the pattern. By including this condition, the query skips
data that doesn't match the grok pattern.

[[runtime-examples-grok-range]]
===== Search for documents in a specific range
You can also run a <<query-dsl-range-query,range query>> that operates on the
`timestamp` field. The following query returns any documents where the
`timestamp` is greater than or equal to `2020-04-30T14:31:27-05:00`:

[source,console]
----
GET my-index-000001/_search
{
  "query": {
    "range": {
      "timestamp": {
        "gte": "2020-04-30T14:31:27-05:00"
      }
    }
  }
}
----
// TEST[continued]

The response includes the document where the log format doesn't match, but the
timestamp falls within the defined range.

[source,console-result]
----
{
  ...
  "hits" : {
    "total" : {
      "value" : 2,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "hdEhyncBRSB6iD-PoBqe",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : "2020-04-30T14:31:27-05:00",
          "message" : "252.0.0.0 - - [30/Apr/2020:14:31:27 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"
        }
      },
      {
        "_index" : "my-index-000001",
        "_id" : "htEhyncBRSB6iD-PoBqe",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : "2020-04-30T14:31:28-05:00",
          "message" : "not a valid apache log"
        }
      }
    ]
  }
}
----
// TESTRESPONSE[s/\.\.\./"took" : $body.took,"timed_out" : $body.timed_out,"_shards" : $body._shards,/]
// TESTRESPONSE[s/"_id" : "hdEhyncBRSB6iD-PoBqe"/"_id": $body.hits.hits.0._id/]
// TESTRESPONSE[s/"_id" : "htEhyncBRSB6iD-PoBqe"/"_id": $body.hits.hits.1._id/]

[[runtime-examples-dissect]]
==== Define a runtime field with a dissect pattern
If you don't need the power of regular expressions, you can use
<<dissect-processor,dissect patterns>> instead of grok patterns. Dissect
patterns match on fixed delimiters but are typically faster that grok.

You can use dissect to achieve the same results as parsing the Apache logs with
a <<runtime-examples-grok,grok pattern>>. Instead of matching on a log
pattern, you include the parts of the string that you want to discard. Paying
special attention to the parts of the string you want to discard will help build
successful dissect patterns.

[source,console]
----
PUT my-index-000001/_mappings
{
  "runtime": {
    "http.client.ip": {
      "type": "ip",
      "script": """
        String clientip=dissect('%{clientip} %{ident} %{auth} [%{@timestamp}] "%{verb} %{request} HTTP/%{httpversion}" %{status} %{size}').extract(doc["message"].value)?.clientip;
        if (clientip != null) emit(clientip);
      """
    }
  }
}
----
// TEST[continued]

Similarly, you can define a dissect pattern to extract the https://developer.mozilla.org/en-US/docs/Web/HTTP/Status[HTTP response code]:

[source,console]
----
PUT my-index-000001/_mappings
{
  "runtime": {
    "http.responses": {
      "type": "long",
      "script": """
        String response=dissect('%{clientip} %{ident} %{auth} [%{@timestamp}] "%{verb} %{request} HTTP/%{httpversion}" %{response} %{size}').extract(doc["message"].value)?.response;
        if (response != null) emit(Integer.parseInt(response));
      """
    }
  }
}
----
// TEST[continued]

You can then run a query to retrieve a specific HTTP response using the
`http.responses` runtime field. Use the `fields` parameter of the `_search`
request to indicate which fields you want to retrieve:

[source,console]
----
GET my-index-000001/_search
{
  "query": {
    "match": {
      "http.responses": "304"
    }
  },
  "fields" : ["http.client_ip","timestamp","http.verb"]
}
----
// TEST[continued]

The response includes a single document where the HTTP response is `304`:

[source,console-result]
----
{
  ...
  "hits" : {
    "total" : {
      "value" : 1,
      "relation" : "eq"
    },
    "max_score" : 1.0,
    "hits" : [
      {
        "_index" : "my-index-000001",
        "_id" : "A2qDy3cBWRMvVAuI7F8M",
        "_score" : 1.0,
        "_source" : {
          "timestamp" : "2020-04-30T14:31:22-05:00",
          "message" : "247.37.0.0 - - [30/Apr/2020:14:31:22 -0500] \"GET /images/hm_nbg.jpg HTTP/1.0\" 304 0"
        },
        "fields" : {
          "http.verb" : [
            "GET"
          ],
          "http.client_ip" : [
            "247.37.0.0"
          ],
          "timestamp" : [
            "2020-04-30T19:31:22.000Z"
          ]
        }
      }
    ]
  }
}
----
// TESTRESPONSE[s/\.\.\./"took" : $body.took,"timed_out" : $body.timed_out,"_shards" : $body._shards,/]
// TESTRESPONSE[s/"_id" : "A2qDy3cBWRMvVAuI7F8M"/"_id": $body.hits.hits.0._id/]
