elasticsearch/docs/painless/painless-guide/painless-execute-script.asciidoc

[[painless-execute-api]]
=== Painless execute API
experimental::[]

The Painless execute API runs a script and returns a result.

[[painless-execute-api-request]]
==== {api-request-title}
`POST /_scripts/painless/_execute`

[[painless-execute-api-desc]]
==== {api-description-title}
Use this API to build and test scripts, such as when defining a script for a
{ref}/runtime.html[runtime field]. This API requires very few dependencies, and is
especially useful if you don't have permissions to write documents on a cluster.

The API uses several _contexts_, which control how scripts are executed, what
variables are available at runtime, and what the return type is.

Each context requires a script, but additional parameters depend on the context
you're using for that script.

[[painless-execute-api-request-body]]
==== {api-request-body-title}
`script`:: (Required, object)
The Painless script to execute.
+
.Properties of `script`
--
include::../painless-contexts/painless-runtime-fields-context.asciidoc[tag=runtime-field-emit]
--

[[_contexts]]
`context`:: (Optional, string)
The context that the script should run in. Defaults to `painless_test` if no
context is specified.
+
.Properties of `context`
[%collapsible%open]
====
`painless_test`::
The default context if no other context is specified. See 
<<painless-execute-test,test context>>.

`filter`::
Treats scripts as if they were run inside a `script` query. See
<<painless-execute-filter-context,filter context>>.

`score`::
Treats scripts as if they were run inside a `script_score` function in a
`function_score` query. See <<painless-execute-core-context,score context>>.

[[painless-execute-runtime-context]]
.Field contexts
[%collapsible%open]
=====
--
The following options are specific to the field contexts.

NOTE: Result ordering in the field contexts is not guaranteed.
--

****
`boolean_field`:: 
The context for {ref}/boolean.html[`boolean` fields]. The script returns a `true`
or `false` response. See
<<painless-runtime-boolean,boolean_field context>>.

`date_field`:: 
The context for {ref}/date.html[`date` fields]. `emit` takes a `long` value and
the script returns a sorted list of dates. See
<<painless-runtime-datetime,date_time context>>.

`double_field`::
The context for `double` {ref}/number.html[numeric fields]. The script returns a
sorted list of `double` values. See
<<painless-runtime-double,double_field context>>.

`geo_point_field`::
The context for {ref}/geo-point.html[`geo-point` fields]. `emit` takes a
`geo-point` value and the script returns coordinates for the geo point. See
<<painless-runtime-geo,geo_point_field context>>.

`ip_field`::
The context for {ref}/ip.html[`ip` fields]. The script returns a sorted list of IP
addresses. See
<<painless-runtime-ip,ip_field context>>.

`keyword_field`::
The context for {ref}/keyword.html[`keyword` fields]. The script returns a sorted
list of `string` values. See
<<painless-runtime-keyword,keyword_field context>>.

`long_field`::
The context for `long` {ref}/number.html[numeric fields]. The script returns a
sorted list of `long` values. See <<painless-runtime-long,long_field context>>.
****
=====
====

`context_setup`:: (Required, object)
Additional parameters for the `context`.
+
NOTE: This parameter is required for all contexts except `painless_test`,
which is the default if no value is provided for `context`.
+
.Properties of `context_setup`
[%collapsible%open]
====
`document`:: (Required, string)
Document that's temporarily indexed in-memory and accessible from the script.

`index`:: (Required, string)
Index containing a mapping that's compatible with the indexed document.
====

`params`:: (`Map`, read-only)
Specifies any named parameters that are passed into the script as variables.

`query`:: (Optional, object)
NOTE: This parameter only applies when `score` is specified as the script 
`context`.
+
Use this parameter to specify a query for computing a score. Besides deciding
whether or not the document matches, the 
{ref}/query-filter-context.html#query-context[query clause] also calculates a 
relevance score in the `_score` metadata field.

[[painless-execute-test]]
==== Test context
The `painless_test` context runs scripts without additional parameters. The only
variable that is available is `params`, which can be used to access user defined
values. The result of the script is always converted to a string.

Because the default context is `painless_test`, you don't need to specify the
`context` or `context_setup`.

===== Request

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": "params.count / params.total",
    "params": {
      "count": 100.0,
      "total": 1000.0
    }
  }
}
----

===== Response

[source,console-result]
----
{
  "result": "0.1"
}
----

[[painless-execute-filter-context]]
==== Filter context
The `filter` context treats scripts as if they were run inside a `script` query.
For testing purposes, a document must be provided so that it will be temporarily
indexed in-memory and is accessible from the script. More precisely, the
`_source`, stored fields and doc values of such a document are available to the
script being tested.

===== Request

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

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": "doc['field'].value.length() <= params.max_length",
    "params": {
      "max_length": 4
    }
  },
  "context": "filter",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "field": "four"
    }
  }
}
----
// TEST[continued]

===== Response

[source,console-result]
----
{
  "result": true
}
----

[[painless-execute-core-context]]
==== Score context
The `score` context treats scripts as if they were run inside a `script_score`
function in a `function_score` query.

===== Request

[source,console]
----
PUT /my-index-000001
{
  "mappings": {
    "properties": {
      "field": {
        "type": "keyword"
      },
      "rank": {
        "type": "long"
      }
    }
  }
}
----

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": "doc['rank'].value / params.max_rank",
    "params": {
      "max_rank": 5.0
    }
  },
  "context": "score",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "rank": 4
    }
  }
}
----
// TEST[continued]

===== Response

[source,console-result]
----
{
  "result": 0.8
}
----

[[painless-execute-runtime-field-context]]
==== Field contexts
The field contexts treat scripts as if they were run inside the
{ref}/runtime-search-request.html[`runtime_mappings` section] of a search query.
You can use field contexts to test scripts for different field types, and then
include those scripts anywhere that they're supported, such as  <<painless-runtime-fields,runtime fields>>.

Choose a field context based on the data type you want to return.

[[painless-runtime-boolean]]
===== `boolean_field`
Use the `boolean_field` field context when you want to return a `true`
or `false` value from a script valuation. {ref}/boolean.html[Boolean fields]
accept `true` and `false` values, but can also accept strings that are
interpreted as either true or false. 

Let's say you have data for the top 100 science fiction books of all time. You
want to write scripts that return a boolean response such as whether books
exceed a certain page count, or if a book was published after a specific year.

Consider that your data is structured like this:

[source,console]
----
PUT /my-index-000001
{
  "mappings": {
    "properties": {
      "name": {
        "type": "keyword"
      },
      "author": {
        "type": "keyword"
      },
      "release_date": {
        "type": "date"
      },
      "page_count": {
        "type": "double"
      }
    }
  }
}
----

You can then write a script in the `boolean_field` context that indicates
whether a book was published before the year 1972:

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": """
      emit(doc['release_date'].value.year < 1972);
    """
  },
  "context": "boolean_field",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "name": "Dune",
      "author": "Frank Herbert",
      "release_date": "1965-06-01",
      "page_count": 604
    }
  }
}
----
// TEST[continued]

Because _Dune_ was published in 1965, the result returns as `true`:

[source,console-result]
----
{
  "result" : [
    true
  ]
}
----

Similarly, you could write a script that determines whether the first name of
an author exceeds a certain number of characters. The following script operates
on the `author` field to determine whether the author's first name contains at
least one character, but is less than five characters:

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": """
      int space = doc['author'].value.indexOf(' ');
      emit(space > 0 && space < 5);
    """
  },
  "context": "boolean_field",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "name": "Dune",
      "author": "Frank Herbert",
      "release_date": "1965-06-01",
      "page_count": 604
    }
  }
}
----
// TEST[continued]

Because `Frank` is five characters, the response returns `false` for the script
valuation:

[source,console-result]
----
{
  "result" : [
    false
  ]
}
----

[[painless-runtime-datetime]]
===== `date_time`
Several options are available for using
<<painless-datetime,using datetime in Painless>>. In this example, you'll
estimate when a particular author starting writing a book based on its release
date and the writing speed of that author. The example makes some assumptions,
but shows to write a script that operates on a date while incorporating
additional information.

Add the following fields to your index mapping to get started:

[source,console]
----
PUT /my-index-000001
{
  "mappings": {
    "properties": {
      "name": {
        "type": "keyword"
      },
      "author": {
        "type": "keyword"
      },
      "release_date": {
        "type": "date"
      },
      "page_count": {
        "type": "long"
      }
    }
  }
}
----

The following script makes the incredible assumption that when writing a book,
authors just write each page and don't do research or revisions. Further, the
script assumes that the average time it takes to write a page is eight hours.

The script retrieves the `author` and makes another fantastic assumption to
either divide or multiply the `pageTime` value based on the author's perceived
writing speed (yet another wild assumption).

The script subtracts the release date value (in milliseconds) from the
calculation of `pageTime` times the `page_count` to determine approximately
(based on numerous assumptions) when the author began writing the book.

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": """
      String author = doc['author'].value;
      long pageTime = 28800000;  <1>
      if (author == 'Robert A. Heinlein') {
        pageTime /= 2;           <2>
      } else if (author == 'Alastair Reynolds') {
        pageTime *= 2;           <3>
      }
      emit(doc['release_date'].value.toInstant().toEpochMilli() - pageTime * doc['page_count'].value);
    """
  },
  "context": "date_field",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "name": "Revelation Space",
      "author": "Alastair Reynolds",
      "release_date": "2000-03-15",
      "page_count": 585
    }
  }
}
----
//TEST[continued]
<1> Eight hours, represented in milliseconds
<2> Incredibly fast writing from Robert A. Heinlein
<3> Alastair Reynolds writes space operas at a much slower speed

In this case, the author is Alastair Reynolds. Based on a release date of
`2000-03-15`, the script calculates that the author started writing
`Revelation Space` on 19 February 1999. Writing a 585 page book in just over one
year is pretty impressive!

[source,console-result]
----
{
  "result" : [
    "1999-02-19T00:00:00.000Z"
  ]
}
----

[[painless-runtime-double]]
===== `double_field`
Use the `double_field` context for {ref}/number.html[numeric data] of type
`double`. For example, let's say you have sensor data that includes a `voltage`
field with values like 5.6. After indexing millions of documents, you discover
that the sensor with model number `QVKC92Q` is under reporting its voltage by a
factor of 1.7. Rather than reindex your data, you can fix it with a
runtime field.

You need to multiply this value, but only for
sensors that match a specific model number.

Add the following fields to your index mapping. The `voltage` field is a 
sub-field of the `measures` object.

[source,console]
----
PUT my-index-000001
{
  "mappings": {
    "properties": {
      "@timestamp": {
        "type": "date"
      },
      "model_number": {
        "type": "keyword"
      },
      "measures": {
        "properties": {
          "voltage": {
            "type": "double"
          }
        }
      }
    }
  }
}
----

The following script matches on any documents where the `model_number` equals
`QVKC92Q`, and then multiplies the `voltage` value by `1.7`. This script is
useful when you want to select specific documents and only operate on values
that match the specified criteria.

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": """
      if (doc['model_number'].value.equals('QVKC92Q'))
      {emit(1.7 * params._source['measures']['voltage']);}
      else{emit(params._source['measures']['voltage']);}
    """
  },
  "context": "double_field",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "@timestamp": 1516470094000,
      "model_number": "QVKC92Q",
      "measures": {
        "voltage": 5.6
      }
    }
  }
}
----
// TEST[continued]

The result includes the calculated voltage, which was determined by multiplying
the original value of `5.6` by `1.7`:

[source,console-result]
----
{
  "result" : [
    9.52
  ]
}
----

[[painless-runtime-geo]]
===== `geo_point_field`
{ref}/geo-point.html[Geo-point] fields accept latitude-longitude pairs. You can
define a geo-point field in several ways, and include values for latitude and
longitude in the document for your script.

If you already have a known geo-point, it's simpler to clearly state the
positions of `lat` and `lon` in your index mappings. 

[source,console]
----
PUT /my-index-000001/
{
  "mappings": {
    "properties": {
      "lat": {
        "type": "double"
      },
     "lon": {
        "type": "double"
      }
    }
  }
}
----

You can then use the `geo_point_field` runtime field context to write a script
that retrieves the `lat` and `long` values.

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": """
      emit(doc['lat'].value, doc['lon'].value);
    """
  },
  "context": "geo_point_field",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "lat": 41.12,
      "lon": -71.34
    }
  }
}
----
// TEST[continued]

Because this you're working with a geo-point field type, the response includes
results that are formatted as `coordinates`. 

[source,console-result]
----
{
  "result" : [
    {
      "coordinates" : [
        -71.34000004269183,
        41.1199999647215
      ],
      "type" : "Point"
    }
  ]
}
----

[[painless-runtime-ip]]
===== `ip_field`
The `ip_field` context is useful for data that includes IP addresses of type
{ref}/ip.html[`ip`]. For example, let's say you have a `message` field from an Apache
log. This field contains an IP address, but also other data that you don't need. 

You can add the `message` field to your index mappings as a `wildcard` to accept
pretty much any data you want to put in that field.

[source,console]
----
PUT /my-index-000001/
{
  "mappings": {
    "properties": {
      "message": {
        "type": "wildcard"
      }
    }
  }
}
----

You can then define a runtime script with a grok pattern that extracts
structured fields out of the `message` field.

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

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": """
      String clientip=grok('%{COMMONAPACHELOG}').extract(doc["message"].value)?.clientip;
      if (clientip != null) emit(clientip); 
    """
  },
  "context": "ip_field",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "message": "40.135.0.0 - - [30/Apr/2020:14:30:17 -0500] \"GET /images/hm_bg.jpg HTTP/1.0\" 200 24736"
    }
  }
}
----
// TEST[continued]

The response includes only the IP address, ignoring all of the other data in the
`message` field.

[source,console-result]
----
{
  "result" : [
    "40.135.0.0"
  ]
}
----

[[painless-runtime-keyword]]
===== `keyword_field`
{ref}/keyword.html[Keyword fields] are often used in sorting, aggregations, and
term-level queries.

Let's say you have a timestamp. You want to calculate the day of the week based
on that value and return it, such as `Thursday`. The following request adds a
`@timestamp` field of type `date` to the index mappings:

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

To return the equivalent day of week based on your timestamp, you can create a
script in the `keyword_field` runtime field context:

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": """
      emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ROOT));
    """
  },
  "context": "keyword_field",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "@timestamp": "2020-04-30T14:31:43-05:00"
    }
  }
}
----
// TEST[continued]

The script operates on the value provided for the `@timestamp` field to
calculate and return the day of the week:

[source,console-result]
----
{
  "result" : [
    "Thursday"
  ]
}
----

[[painless-runtime-long]]
===== `long_field`
Let's say you have sensor data that a `measures` object. This object includes
a `start` and `end` field, and you want to calculate the difference between
those values.

The following request adds a `measures` object to the mappings with two fields,
both of type `long`:

[source,console]
----
PUT /my-index-000001/
{
  "mappings": {
    "properties": {
      "measures": {
        "properties": {
          "start": {
            "type": "long"
          },
          "end": {
           "type": "long" 
          }
        }
      }
    }
  }
}
----

You can then define a script that assigns values to the `start` and `end` fields
and operate on them. The following script extracts the value for the `end`
field from the `measures` object and subtracts it from the `start` field:

[source,console]
----
POST /_scripts/painless/_execute
{
  "script": {
    "source": """
      emit(doc['measures.end'].value - doc['measures.start'].value);
    """
  },
  "context": "long_field",
  "context_setup": {
    "index": "my-index-000001",
    "document": {
      "measures": {
        "voltage": "4.0",
        "start": "400",
        "end": "8625309"
      }
    }
  }
}
----
// TEST[continued]

The response includes the calculated value from the script valuation:

[source,console-result]
----
{
  "result" : [
    8624909
  ]
}
----