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Regular expressions

Serverless

Elastic Cloud Serverless projects are autoscaled environments, fully managed by Elastic and available on Elastic Cloud.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Serverless interfaces and procedures might differ from classic Elastic Stack deployments.

Stack

The Elastic Stack includes Elastic's core products such as Elasticsearch, Kibana, Logstash, and Beats.

Availability

  • Generally available

    This functionality is generally available and ready for production usage.

Unless stated otherwise on the page, this functionality is available when your Elastic Stack is deployed on Elastic Cloud Hosted, Elastic Cloud Enterprise, Elastic Cloud on Kubernetes, and self-managed environments.

This documentation corresponds to the latest patch available for each minor version. If you're not using the latest patch, check the release notes for changes.

Note

Regexes are enabled by default by the setting script.painless.regex.enabled which has a default value of limited the default. This enables the use of regular expressions but limits their complexity. Innocuous looking regexes can have sometimes have adverse performance and stack depth behavior, but they still remain a powerful tool. In addition to limited, you can set script.painless.regex.enabled to true in elasticsearch.yml to enable regular expressions without limiting them.

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:

JSON 
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:

JSON 
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:

JSON 
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:

JSON 
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).

Important

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:

JSON 
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:

JSON 
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.