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Painless Examples

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Painless Examples

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To illustrate how Painless works, let’s load some hockey stats into an Elasticsearch index:

PUT hockey/_bulk?refresh
{"index":{"_id":1}}
{"first":"johnny","last":"gaudreau","goals":[9,27,1],"assists":[17,46,0],"gp":[26,82,1],"born":"1993/08/13"}
{"index":{"_id":2}}
{"first":"sean","last":"monohan","goals":[7,54,26],"assists":[11,26,13],"gp":[26,82,82],"born":"1994/10/12"}
{"index":{"_id":3}}
{"first":"jiri","last":"hudler","goals":[5,34,36],"assists":[11,62,42],"gp":[24,80,79],"born":"1984/01/04"}
{"index":{"_id":4}}
{"first":"micheal","last":"frolik","goals":[4,6,15],"assists":[8,23,15],"gp":[26,82,82],"born":"1988/02/17"}
{"index":{"_id":5}}
{"first":"sam","last":"bennett","goals":[5,0,0],"assists":[8,1,0],"gp":[26,1,0],"born":"1996/06/20"}
{"index":{"_id":6}}
{"first":"dennis","last":"wideman","goals":[0,26,15],"assists":[11,30,24],"gp":[26,81,82],"born":"1983/03/20"}
{"index":{"_id":7}}
{"first":"david","last":"jones","goals":[7,19,5],"assists":[3,17,4],"gp":[26,45,34],"born":"1984/08/10"}
{"index":{"_id":8}}
{"first":"tj","last":"brodie","goals":[2,14,7],"assists":[8,42,30],"gp":[26,82,82],"born":"1990/06/07"}
{"index":{"_id":39}}
{"first":"mark","last":"giordano","goals":[6,30,15],"assists":[3,30,24],"gp":[26,60,63],"born":"1983/10/03"}
{"index":{"_id":10}}
{"first":"mikael","last":"backlund","goals":[3,15,13],"assists":[6,24,18],"gp":[26,82,82],"born":"1989/03/17"}
{"index":{"_id":11}}
{"first":"joe","last":"colborne","goals":[3,18,13],"assists":[6,20,24],"gp":[26,67,82],"born":"1990/01/30"}

Accessing Doc Values from Painless

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Document values can be accessed from a Map named doc.

For example, the following script calculates a player’s total goals. This example uses a strongly typed int and a for loop.

GET hockey/_search
{
  "query": {
    "function_score": {
      "script_score": {
        "script": {
          "lang": "painless",
          "source": """
            int total = 0;
            for (int i = 0; i < doc['goals'].length; ++i) {
              total += doc['goals'][i];
            }
            return total;
          """
        }
      }
    }
  }
}

Alternatively, you could do the same thing using a script field instead of a function score:

GET hockey/_search
{
  "query": {
    "match_all": {}
  },
  "script_fields": {
    "total_goals": {
      "script": {
        "lang": "painless",
        "source": """
          int total = 0;
          for (int i = 0; i < doc['goals'].length; ++i) {
            total += doc['goals'][i];
          }
          return total;
        """
      }
    }
  }
}

The following example uses a Painless script to sort the players by their combined first and last names. The names are accessed using doc['first'].value and doc['last'].value.

GET hockey/_search
{
  "query": {
    "match_all": {}
  },
  "sort": {
    "_script": {
      "type": "string",
      "order": "asc",
      "script": {
        "lang": "painless",
        "source": "doc['first.keyword'].value + ' ' + doc['last.keyword'].value"
      }
    }
  }
}

Missing values

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doc['field'].value throws an exception if the field is missing in a document.

To check if a document is missing a value, you can call doc['field'].size() == 0.

Updating Fields with Painless

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You can also easily update fields. You access the original source for a field as ctx._source.<field-name>.

First, let’s look at the source data for a player by submitting the following request:

GET hockey/_search
{
  "stored_fields": [
    "_id",
    "_source"
  ],
  "query": {
    "term": {
      "_id": 1
    }
  }
}

To change player 1’s last name to hockey, simply set ctx._source.last to the new value:

POST hockey/_update/1
{
  "script": {
    "lang": "painless",
    "source": "ctx._source.last = params.last",
    "params": {
      "last": "hockey"
    }
  }
}

You can also add fields to a document. For example, this script adds a new field that contains the player’s nickname, hockey.

POST hockey/_update/1
{
  "script": {
    "lang": "painless",
    "source": """
      ctx._source.last = params.last;
      ctx._source.nick = params.nick
    """,
    "params": {
      "last": "gaudreau",
      "nick": "hockey"
    }
  }
}

Dates

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Date fields are exposed as ReadableDateTime, so they support methods like getYear, getDayOfWeek or e.g. getting milliseconds since epoch with getMillis. To use these in a script, leave out the get prefix and continue with lowercasing the rest of the method name. For example, the following returns every hockey player’s birth year:

GET hockey/_search
{
  "script_fields": {
    "birth_year": {
      "script": {
        "source": "doc.born.value.year"
      }
    }
  }
}

Regular expressions

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Regexes are disabled by default because they circumvent Painless’s protection against long running and memory hungry scripts. To make matters worse even innocuous looking regexes can have staggering performance and stack depth behavior. They remain an amazing powerful tool but are too scary to enable by default. To enable them yourself set script.painless.regex.enabled: true in elasticsearch.yml. We’d like very much to have a safe alternative implementation that can be enabled by default so check this space for later developments!

Painless’s native support for regular expressions has syntax constructs:

/pattern/: Pattern literals create patterns. This is the only way to create a pattern in painless. The pattern inside the /'s are just Java regular expressions. See Pattern flags for more.
=~: The find operator return a boolean, true if a subsequence of the text matches, false otherwise.
==~: The match operator returns a boolean, true if the text matches, false if it doesn’t.

Using the find operator (=~) you can update all hockey players with "b" in their last name:

POST hockey/_update_by_query
{
  "script": {
    "lang": "painless",
    "source": """
      if (ctx._source.last =~ /b/) {
        ctx._source.last += "matched";
      } else {
        ctx.op = "noop";
      }
    """
  }
}

Using the match operator (==~) you can update all the hockey players whose names start with a consonant and end with a vowel:

POST hockey/_update_by_query
{
  "script": {
    "lang": "painless",
    "source": """
      if (ctx._source.last ==~ /[^aeiou].*[aeiou]/) {
        ctx._source.last += "matched";
      } else {
        ctx.op = "noop";
      }
    """
  }
}

You can use the Pattern.matcher directly to get a Matcher instance and remove all of the vowels in all of their last names:

POST hockey/_update_by_query
{
  "script": {
    "lang": "painless",
    "source": "ctx._source.last = /[aeiou]/.matcher(ctx._source.last).replaceAll('')"
  }
}

Matcher.replaceAll is just a call to Java’s Matcher's replaceAll method so it supports $1 and \1 for replacements:

POST hockey/_update_by_query
{
  "script": {
    "lang": "painless",
    "source": "ctx._source.last = /n([aeiou])/.matcher(ctx._source.last).replaceAll('$1')"
  }
}

If you need more control over replacements you can call replaceAll on a CharSequence with a Function<Matcher, String> that builds the replacement. This does not support $1 or \1 to access replacements because you already have a reference to the matcher and can get them with m.group(1).

Calling Matcher.find inside of the function that builds the replacement is rude and will likely break the replacement process.

This will make all of the vowels in the hockey player’s last names upper case:

POST hockey/_update_by_query
{
  "script": {
    "lang": "painless",
    "source": """
      ctx._source.last = ctx._source.last.replaceAll(/[aeiou]/, m ->
        m.group().toUpperCase(Locale.ROOT))
    """
  }
}

Or you can use the CharSequence.replaceFirst to make the first vowel in their last names upper case:

POST hockey/_update_by_query
{
  "script": {
    "lang": "painless",
    "source": """
      ctx._source.last = ctx._source.last.replaceFirst(/[aeiou]/, m ->
        m.group().toUpperCase(Locale.ROOT))
    """
  }
}

Note: all of the _update_by_query examples above could really do with a query to limit the data that they pull back. While you could use a script query it wouldn’t be as efficient as using any other query because script queries aren’t able to use the inverted index to limit the documents that they have to check.

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