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

[role="xpack"]
[testenv="basic"]
[[transform-painless-examples]]
= Painless examples for {transforms}
++++
<titleabbrev>Painless examples</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>>
* <<painless-count-http>>
* <<painless-compare>>
* <<painless-web-session>>

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.

[[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(...)` you 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

[[painless-time-features]]
== Getting time features by using aggregations

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": {
  "avg_hour_of_day": { <1>
    "avg":{
      "script": { <2>
        "source": """
          ZonedDateTime date =  doc['@timestamp'].value; <3>
          return date.getHour(); <4>
        """
      }
    }  
  },
  "avg_month_of_year": { <5>
    "avg":{
      "script": { <6> 
        "source": """
          ZonedDateTime date =  doc['@timestamp'].value; <7>
          return date.getMonthValue(); <8>
        """
      }
    }
  },
 ...
}
--------------------------------------------------
// NOTCONSOLE

<1> Name of the aggregation.
<2> Contains the Painless script that returns the hour of the day.
<3> Sets `date` based on the timestamp of the document.
<4> Returns the hour value from `date`.
<5> Name of the aggregation.
<6> Contains the Painless script that returns the month of the year.
<7> Sets `date` based on the timestamp of the document.
<8> Returns the month value from `date`.

[[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"
|===

[[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 
<<search-aggregations-pipeline-bucket-script-aggregation,bucket script>>. 
The example uses the {kib} sample web logs dataset.

[source,console]
--------------------------------------------------
PUT _transform/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.

[[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}. You can achieve a similar 
function with filter aggregations, check the 
{ref}/transform-examples.html#example-clientips[Finding suspicious client IPs] 
example for details.

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.

[[painless-compare]]
== Comparing indices by using scripted metric aggregations

This example shows how to compare the content of two indices by a {transform} 
that uses a scripted metric aggregation. 

[source,console]
--------------------------------------------------
POST _transform/_preview
{
  "id" : "index_compare",
  "source" : { <1>
    "index" : [
      "index1",
      "index2"
    ],
    "query" : {
      "match_all" : { }
    }
  },
  "dest" : { <2>
    "index" : "compare"
  },
  "pivot" : {
    "group_by" : {
      "unique-id" : {
        "terms" : {
          "field" : "<unique-id-field>" <3>
        }
      }
    },
    "aggregations" : {
      "compare" : { <4>
        "scripted_metric" : {
          "init_script" : "",
          "map_script" : "state.doc = new HashMap(params['_source'])", <5>
          "combine_script" : "return state", <6>
          "reduce_script" : """ <7>
            if (states.size() != 2) {
              return "count_mismatch"
            }
            if (states.get(0).equals(states.get(1))) {
              return "match"
            } else {
              return "mismatch"
            }
            """
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[skip:setup kibana sample data]

<1> The indices referenced in the `source` object are compared to each other.
<2> The `dest` index contains the results of the comparison.
<3> The `group_by` field needs to be a unique identifier for each document.
<4> Object of the `scripted_metric` aggregation.
<5> The `map_script` defines `doc` in the state object. By using 
`new HashMap(...)` you copy the source document, this is important whenever you 
want to pass the full source object from one phase to the next.
<6> The `combine_script` returns `state` from each shard.
<7> The `reduce_script` checks if the size of the indices are equal. If they are 
not equal, than it reports back a `count_mismatch`. Then it iterates through all 
the values of the two indices and compare them. If the values are equal, then it 
returns a `match`, otherwise returns a `mismatch`.

[[painless-web-session]]
== Getting web session details by using scripted metric aggregation

This example shows how to derive multiple features from a single transaction. 
Let's take a look on the example source document from the data:

.Source document
[%collapsible%open]
=====
[source,js]
--------------------------------------------------
{
  "_index":"apache-sessions",
  "_type":"_doc",
  "_id":"KvzSeGoB4bgw0KGbE3wP",
  "_score":1.0,
  "_source":{
    "@timestamp":1484053499256,
    "apache":{
      "access":{
        "sessionid":"571604f2b2b0c7b346dc685eeb0e2306774a63c2",
        "url":"http://www.leroymerlin.fr/v3/search/search.do?keyword=Carrelage%20salle%20de%20bain",
        "path":"/v3/search/search.do",
        "query":"keyword=Carrelage%20salle%20de%20bain",
        "referrer":"http://www.leroymerlin.fr/v3/p/produits/carrelage-parquet-sol-souple/carrelage-sol-et-mur/decor-listel-et-accessoires-carrelage-mural-l1308217717?resultOffset=0&resultLimit=51&resultListShape=MOSAIC&priceStyle=SALEUNIT_PRICE",
        "user_agent":{
          "original":"Mobile Safari 10.0 Mac OS X (iPad) Apple Inc.",
          "os_name":"Mac OS X (iPad)"
        },
        "remote_ip":"0337b1fa-5ed4-af81-9ef4-0ec53be0f45d",
        "geoip":{
          "country_iso_code":"FR",
          "location":{
            "lat":48.86,
            "lon":2.35
          }
        },
        "response_code":200,
        "method":"GET"
      }
    }
  }
}
...
--------------------------------------------------
// NOTCONSOLE
=====


By using the `sessionid` as a group-by field, you are able to enumerate events 
through the session and get more details of the session by using scripted metric 
aggregation.

[source,js]
--------------------------------------------------
POST _transform/_preview
{
  "source": {
    "index": "apache-sessions"
  },
  "pivot": {
    "group_by": {
      "sessionid": { <1>
        "terms": {
          "field": "apache.access.sessionid"
        }
      }
    },
    "aggregations": { <2>
      "distinct_paths": { 
        "cardinality": {
          "field": "apache.access.path"
        }
      },
      "num_pages_viewed": {
        "value_count": {
          "field": "apache.access.url"
        }
      },
      "session_details": {
        "scripted_metric": {
          "init_script": "state.docs = []", <3>
          "map_script": """ <4>
            Map span = [
              '@timestamp':doc['@timestamp'].value, 
              'url':doc['apache.access.url'].value,
              'referrer':doc['apache.access.referrer'].value
            ]; 
            state.docs.add(span)
          """,
          "combine_script": "return state.docs;", <5>
          "reduce_script": """ <6>
            def all_docs = []; 
            for (s in states) { 
              for (span in s) { 
                all_docs.add(span); 
              }
            }
            all_docs.sort((HashMap o1, HashMap o2)->o1['@timestamp'].millis.compareTo(o2['@timestamp'].millis)); 
            def size = all_docs.size();
            def min_time = all_docs[0]['@timestamp'];
            def max_time = all_docs[size-1]['@timestamp'];
            def duration = max_time.millis - min_time.millis;
            def entry_page = all_docs[0]['url'];
            def exit_path = all_docs[size-1]['url'];
            def first_referrer = all_docs[0]['referrer'];
            def ret = new HashMap();
            ret['first_time'] = min_time;
            ret['last_time'] = max_time;
            ret['duration'] = duration;
            ret['entry_page'] = entry_page;
            ret['exit_path'] = exit_path;
            ret['first_referrer'] = first_referrer;
            return ret;
          """
        }
      }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

<1> The data is grouped by `sessionid`.
<2> The aggregations counts the number of paths and enumerate the viewed pages 
during the session.
<3> The `init_script` creates an array type `doc` in the `state` object.
<4> The `map_script` defines a `span` array with a timestamp, a URL, and a 
referrer value which are based on the corresponding values of the document, then 
adds the value of the `span` array to the `doc` object.
<5> The `combine_script` returns `state.docs` from each shard.
<6> The `reduce_script` defines various objects like `min_time`, `max_time`, and 
`duration` based on the document fields, then declares a `ret` object, and 
copies the source document by using `new HashMap ()`. Next, the script defines 
`first_time`, `last_time`, `duration` and other fields inside the `ret` object 
based on the corresponding object defined earlier, finally returns `ret`.

The API call results in a similar response:

[source,js]
--------------------------------------------------
{
  "num_pages_viewed" : 2.0,
  "session_details" : {
    "duration" : 131374,
    "first_referrer" : "https://www.bing.com/",
    "entry_page" : "http://www.leroymerlin.fr/v3/p/produits/materiaux-menuiserie/porte-coulissante-porte-interieure-escalier-et-rambarde/barriere-de-securite-l1308218463",
    "first_time" : "2017-01-10T21:22:52.982Z",
    "last_time" : "2017-01-10T21:25:04.356Z",
    "exit_path" : "http://www.leroymerlin.fr/v3/p/produits/materiaux-menuiserie/porte-coulissante-porte-interieure-escalier-et-rambarde/barriere-de-securite-l1308218463?__result-wrapper?pageTemplate=Famille%2FMat%C3%A9riaux+et+menuiserie&resultOffset=0&resultLimit=50&resultListShape=PLAIN&nomenclatureId=17942&priceStyle=SALEUNIT_PRICE&fcr=1&*4294718806=4294718806&*14072=14072&*4294718593=4294718593&*17942=17942"
  },
  "distinct_paths" : 1.0,
  "sessionid" : "000046f8154a80fd89849369c984b8cc9d795814"
},
{
  "num_pages_viewed" : 10.0,
  "session_details" : {
    "duration" : 343112,
    "first_referrer" : "https://www.google.fr/",
    "entry_page" : "http://www.leroymerlin.fr/",
    "first_time" : "2017-01-10T16:57:39.937Z",
    "last_time" : "2017-01-10T17:03:23.049Z",
    "exit_path" : "http://www.leroymerlin.fr/v3/p/produits/porte-de-douche-coulissante-adena-e168578"
  },
  "distinct_paths" : 8.0,
  "sessionid" : "000087e825da1d87a332b8f15fa76116c7467da6"
}
...
--------------------------------------------------
// NOTCONSOLE