elasticsearch/docs/reference/transform/painless-examples.asciidoc

[role="xpack"]
[testenv="basic"]
[[transform-painless-examples]]
=== Painless examples for {transforms}
++++
<titleabbrev>Painless examples for {transforms}</titleabbrev>
++++

These examples demonstrate how to use Painless in {transforms}. You can learn 
more about the Painless scripting language in the 
{painless}/painless-guide.html[Painless guide].

* <<painless-top-hits>>
* <<painless-time-features>>
* <<painless-group-by>>
* <<painless-bucket-script>>

NOTE: While the context of the following examples is the {transform} use case, 
the Painless scripts in the snippets below can be used in other {es} search 
aggregations, too.


[discrete]
[[painless-top-hits]]
==== Getting top hits by using scripted metric aggregation

This snippet shows how to find the latest document, in other words the document 
with the earliest timestamp. From a technical perspective, it helps to achieve 
the function of a <<search-aggregations-metrics-top-hits-aggregation>> by using 
scripted metric aggregation in a {transform}, which provides a metric output.

[source,js]
--------------------------------------------------
"aggregations": {
  "latest_doc": { 
    "scripted_metric": {
      "init_script": "state.timestamp_latest = 0L; state.last_doc = ''", <1>
      "map_script": """ <2>
        def current_date = doc['@timestamp'].getValue().toInstant().toEpochMilli(); 
        if (current_date > state.timestamp_latest) 
        {state.timestamp_latest = current_date;
        state.last_doc = new HashMap(params['_source']);}
      """,
      "combine_script": "return state", <3>
      "reduce_script": """ <4>
        def last_doc = '';
        def timestamp_latest = 0L;
        for (s in states) {if (s.timestamp_latest > (timestamp_latest))
        {timestamp_latest = s.timestamp_latest; last_doc = s.last_doc;}} 
        return last_doc
      """
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

<1> The `init_script` creates a long type `timestamp_latest` and a string type 
`last_doc` in the `state` object.
<2> The `map_script` defines `current_date` based on the timestamp of the 
document, then compares `current_date` with `state.timestamp_latest`, finally 
returns `state.last_doc` from the shard. By using `new HashMap(...)` we copy the 
source document, this is important whenever you want to pass the full source 
object from one phase to the next.
<3> The `combine_script` returns `state` from each shard.
<4> The `reduce_script` iterates through the value of `s.timestamp_latest` 
returned by each shard and returns the document with the latest timestamp 
(`last_doc`). In the response, the top hit (in other words, the `latest_doc`) is 
nested below the `latest_doc` field.

Check the
<<scripted-metric-aggregation-scope,scope of scripts>>
for detailed explanation on the respective scripts.

You can retrieve the last value in a similar way: 

[source,js]
--------------------------------------------------
"aggregations": {
  "latest_value": {
    "scripted_metric": {
      "init_script": "state.timestamp_latest = 0L; state.last_value = ''",
      "map_script": """
        def current_date = doc['date'].getValue().toInstant().toEpochMilli(); 
        if (current_date > state.timestamp_latest) 
        {state.timestamp_latest = current_date;
        state.last_value = params['_source']['value'];}
      """,
      "combine_script": "return state",
      "reduce_script": """
        def last_value = '';
        def timestamp_latest = 0L; 
        for (s in states) {if (s.timestamp_latest > (timestamp_latest)) 
        {timestamp_latest = s.timestamp_latest; last_value = s.last_value;}} 
        return last_value
      """
    }
  }
}
--------------------------------------------------
// NOTCONSOLE


[discrete]
[[painless-time-features]]
==== Getting time features as scripted fields

This snippet shows how to extract time based features by using Painless in a 
{transform}. The snippet uses an index where `@timestamp` is defined as a `date` 
type field.

[source,js]
--------------------------------------------------
"aggregations": {
  "script_fields": {
      "hour_of_day": { <1>
        "script": {
          "lang": "painless",
          "source": """
            ZonedDateTime date =  doc['@timestamp'].value; <2>
            return date.getHour(); <3>
          """
        }
      },
      "month_of_year": { <4>
        "script": {
          "lang": "painless",
          "source": """
            ZonedDateTime date =  doc['@timestamp'].value; <5>
            return date.getMonthValue(); <6>
          """
        }
      }
    },
  ...
}
--------------------------------------------------
// NOTCONSOLE

<1> Contains the Painless script that returns the hour of the day.
<2> Sets `date` based on the timestamp of the document.
<3> Returns the hour value from `date`.
<4> Contains the Painless script that returns the month of the year.
<5> Sets `date` based on the timestamp of the document.
<6> Returns the month value from `date`.


[discrete]
[[painless-group-by]]
==== Using Painless in `group_by`

It is possible to base the `group_by` property of a {transform} on the output of 
a script. The following example uses the {kib} sample web logs dataset. The goal 
here is to make the {transform} output easier to understand through normalizing 
the value of the fields that the data is grouped by.

[source,console]
--------------------------------------------------
POST _transform/_preview
{
  "source": {
    "index": [ <1>
      "kibana_sample_data_logs"
    ]
  },
  "pivot": {
    "group_by": {
      "agent": {
        "terms": {
          "script": { <2>
            "source": """String agent = doc['agent.keyword'].value; 
            if (agent.contains("MSIE")) { 
              return "internet explorer";
            } else if (agent.contains("AppleWebKit")) { 
              return "safari"; 
            } else if (agent.contains('Firefox')) { 
              return "firefox";
            } else { return agent }""",
            "lang": "painless"
          }
        }
      }
    },
    "aggregations": { <3>
      "200": {
        "filter": {
          "term": {
            "response": "200"
          }
        }
      },
      "404": {
        "filter": {
          "term": {
            "response": "404"
          }
        }
      },
      "503": {
        "filter": {
          "term": {
            "response": "503"
          }
        }
      }
    }
  },
  "dest": { <4>
    "index": "pivot_logs"
  }
} 
--------------------------------------------------
// TEST[skip:setup kibana sample data]

<1> Specifies the source index or indices.
<2> The script defines an `agent` string based on the `agent` field of the 
documents, then iterates through the values. If an `agent` field contains 
"MSIE", than the script returns "Internet Explorer". If it contains 
`AppleWebKit`, it returns "safari". It returns "firefox" if the field value 
contains "Firefox". Finally, in every other case, the value of the field is 
returned.
<3> The aggregations object contains filters that narrow down the results to 
documents that contains `200`, `404`, or `503` values in the `response` field.
<4> Specifies the destination index of the {transform}.

The API returns the following result:

[source,js]
--------------------------------------------------
{
  "preview" : [
    {
      "agent" : "firefox",
      "200" : 4931,
      "404" : 259,
      "503" : 172
    },
    {
      "agent" : "internet explorer",
      "200" : 3674,
      "404" : 210,
      "503" : 126
    },
    {
      "agent" : "safari",
      "200" : 4227,
      "404" : 332,
      "503" : 143
    }
  ],
  "mappings" : {
    "properties" : {
      "200" : {
        "type" : "long"
      },
      "agent" : {
        "type" : "keyword"
      },
      "404" : {
        "type" : "long"
      },
      "503" : {
        "type" : "long"
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

You can see that the `agent` values are simplified so it is easier to interpret 
them. The table below shows how normalization modifies the output of the 
{transform} in our example compared to the non-normalized values.

[width="50%"]

|===
| Non-normalized `agent` value                                                 | Normalized `agent` value 

| "Mozilla/4.0 (compatible; MSIE 6.0; Windows NT 5.1; SV1; .NET CLR 1.1.4322)" | "internet explorer"
| "Mozilla/5.0 (X11; Linux i686) AppleWebKit/534.24 (KHTML, like Gecko) Chrome/11.0.696.50 Safari/534.24" | "safari"
| "Mozilla/5.0 (X11; Linux x86_64; rv:6.0a1) Gecko/20110421 Firefox/6.0a1" | "firefox"
|===


[discrete]
[[painless-bucket-script]]
==== Getting duration by using bucket script

This example shows you how to get the duration of a session by client IP from a 
data log by using 
{ref}/search-aggregations-pipeline-bucket-script-aggregation.html[bucket script]. 
The example uses the {kib} sample web logs dataset.

[source,console]
--------------------------------------------------
PUT _data_frame/transforms/data_log
{
  "source": {
    "index": "kibana_sample_data_logs"
  },
  "dest": {
    "index": "data-logs-by-client"
  },
  "pivot": {
    "group_by": {
      "machine.os": {"terms": {"field": "machine.os.keyword"}},
      "machine.ip": {"terms": {"field": "clientip"}}
    },
    "aggregations": {
      "time_frame.lte": {
        "max": {
          "field": "timestamp"
        }
      },
      "time_frame.gte": {
        "min": {
          "field": "timestamp"
        }
      },
      "time_length": { <1>
        "bucket_script": {
          "buckets_path": { <2>
            "min": "time_frame.gte.value",
            "max": "time_frame.lte.value"
          },
          "script": "params.max - params.min" <3>
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[skip:setup kibana sample data]

<1> To define the length of the sessions, we use a bucket script.
<2> The bucket path is a map of script variables and their associated path to 
the buckets you want to use for the variable. In this particular case, `min` and 
`max` are variables mapped to `time_frame.gte.value` and `time_frame.lte.value`.
<3> Finally, the script substracts the start date of the session from the end 
date which results in the duration of the session.


[discrete]
[[painless-count-http]]
==== Counting HTTP responses by using scripted metric aggregation

You can count the different HTTP response types in a web log data set by using 
scripted metric aggregation as part of the {transform}. The example below 
assumes that the HTTP response codes are stored as keywords in the `response` 
field of the documents.

[source,js]
--------------------------------------------------
"aggregations": { <1>
  "responses.counts": { <2>
    "scripted_metric": { <3>
      "init_script": "state.responses = ['error':0L,'success':0L,'other':0L]", <4>
      "map_script": """ <5>
        def code = doc['response.keyword'].value;
        if (code.startsWith('5') || code.startsWith('4')) {
          state.responses.error += 1 ;
        } else if(code.startsWith('2')) {
          state.responses.success += 1;
        } else {
          state.responses.other += 1;
        }
        """,
      "combine_script": "state.responses", <6>
      "reduce_script": """ <7>
        def counts = ['error': 0L, 'success': 0L, 'other': 0L];
        for (responses in states) {
          counts.error += responses['error'];
          counts.success += responses['success'];
          counts.other += responses['other'];
        }
        return counts;
        """
      }
    },
  ...  
}
--------------------------------------------------
// NOTCONSOLE

<1> The `aggregations` object of the {transform} that contains all aggregations.
<2> Object of the `scripted_metric` aggregation.
<3> This `scripted_metric` performs a distributed operation on the web log data 
to count specific types of HTTP responses (error, success, and other).
<4> The `init_script` creates a `responses` array in the `state` object with 
three properties (`error`, `success`, `other`) with long data type.
<5> The `map_script` defines `code` based on the `response.keyword` value of the 
document, then it counts the errors, successes, and other responses based on the 
first digit of the responses.
<6> The `combine_script` returns `state.responses` from each shard.
<7> The `reduce_script` creates a `counts` array with the `error`, `success`, 
and `other` properties, then iterates through the value of `responses` returned 
by each shard and assigns the different response types to the appropriate 
properties of the `counts` object; error responses to the error counts, success 
responses to the success counts, and other responses to the other counts. 
Finally, returns the `counts` array with the response counts.