[[search-aggregations-bucket-categorize-text-aggregation]]
=== Categorize text aggregation
++++
<titleabbrev>Categorize text</titleabbrev>
++++

experimental::[]

A multi-bucket aggregation that groups semi-structured text into buckets. Each `text` field is re-analyzed
using a custom analyzer. The resulting tokens are then categorized creating buckets of similarly formatted
text values. This aggregation works best with machine generated text like system logs. Only the first 100 analyzed
tokens are used to categorize the text.

NOTE: If you have considerable memory allocated to your JVM but are receiving circuit breaker exceptions from this
      aggregation, you may be attempting to categorize text that is poorly formatted for categorization. Consider
      adding `categorization_filters` or running under <<search-aggregations-bucket-sampler-aggregation,sampler>>,
      <<search-aggregations-bucket-diversified-sampler-aggregation,diversified sampler>>, or
      <<search-aggregations-random-sampler-aggregation,random sampler>> to explore the created categories.

NOTE: The algorithm used for categorization was completely changed in version 8.3.0. As a result this aggregation
      will not work in a mixed version cluster where some nodes are on version 8.3.0 or higher and others are
      on a version older than 8.3.0. Upgrade all nodes in your cluster to the same version if you experience
      an error related to this change.

[[bucket-categorize-text-agg-syntax]]
==== Parameters

`categorization_analyzer`::
(Optional, object or string)
The categorization analyzer specifies how the text is analyzed and tokenized before
being categorized. The syntax is very similar to that used to define the `analyzer` in the
<<indices-analyze,Analyze endpoint>>. This
property cannot be used at the same time as `categorization_filters`.
+
The `categorization_analyzer` field can be specified either as a string or as an
object. If it is a string it must refer to a
<<analysis-analyzers,built-in analyzer>> or one added by another plugin. If it
is an object it has the following properties:
+
.Properties of `categorization_analyzer`
[%collapsible%open]
=====
`char_filter`::::
(array of strings or objects)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=char-filter]

`tokenizer`::::
(string or object)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=tokenizer]

`filter`::::
(array of strings or objects)
include::{es-repo-dir}/ml/ml-shared.asciidoc[tag=filter]
=====

`categorization_filters`::
(Optional, array of strings)
This property expects an array of regular expressions. The expressions
are used to filter out matching sequences from the categorization field values.
You can use this functionality to fine tune the categorization by excluding
sequences from consideration when categories are defined. For example, you can
exclude SQL statements that appear in your log files. This
property cannot be used at the same time as `categorization_analyzer`. If you
only want to define simple regular expression filters that are applied prior to
tokenization, setting this property is the easiest method. If you also want to
customize the tokenizer or post-tokenization filtering, use the
`categorization_analyzer` property instead and include the filters as
`pattern_replace` character filters.

`field`::
(Required, string)
The semi-structured text field to categorize.

`max_matched_tokens`::
(Optional, integer)
This parameter does nothing now, but is permitted for compatibility with the original
pre-8.3.0 implementation.

`max_unique_tokens`::
(Optional, integer)
This parameter does nothing now, but is permitted for compatibility with the original
pre-8.3.0 implementation.

`min_doc_count`::
(Optional, integer)
The minimum number of documents for a bucket to be returned to the results.

`shard_min_doc_count`::
(Optional, integer)
The minimum number of documents for a bucket to be returned from the shard before
merging.

`shard_size`::
(Optional, integer)
The number of categorization buckets to return from each shard before merging
all the results.

`similarity_threshold`::
(Optional, integer, default: `70`)
The minimum percentage of token weight that must match for text to be added to the
category bucket.
Must be between 1 and 100. The larger the value the narrower the categories.
Larger values will increase memory usage and create narrower categories.

`size`::
(Optional, integer, default: `10`)
The number of buckets to return.

==== Basic use

WARNING: Re-analyzing _large_ result sets will require a lot of time and memory. This aggregation should be
         used in conjunction with <<async-search, Async search>>. Additionally, you may consider
         using the aggregation as a child of either the <<search-aggregations-bucket-sampler-aggregation,sampler>> or
         <<search-aggregations-bucket-diversified-sampler-aggregation,diversified sampler>> aggregation.
         This will typically improve speed and memory use.

Example:

[source,console]
--------------------------------------------------
POST log-messages/_search?filter_path=aggregations
{
  "aggs": {
    "categories": {
      "categorize_text": {
        "field": "message"
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:categorize_text]

Response:

[source,console-result]
--------------------------------------------------
{
  "aggregations" : {
    "categories" : {
      "buckets" : [
        {
          "doc_count" : 3,
          "key" : "Node shutting down",
          "max_matching_length" : 49
        },
        {
          "doc_count" : 1,
          "key" : "Node starting up",
          "max_matching_length" : 47
        },
        {
          "doc_count" : 1,
          "key" : "User foo_325 logging on",
          "max_matching_length" : 52
        },
        {
          "doc_count" : 1,
          "key" : "User foo_864 logged off",
          "max_matching_length" : 52
        }
      ]
    }
  }
}
--------------------------------------------------

Here is an example using `categorization_filters`

[source,console]
--------------------------------------------------
POST log-messages/_search?filter_path=aggregations
{
  "aggs": {
    "categories": {
      "categorize_text": {
        "field": "message",
        "categorization_filters": ["\\w+\\_\\d{3}"] <1>
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:categorize_text]

<1> The filters to apply to the analyzed tokens. It filters
    out tokens like `bar_123`.

Note how the `foo_<number>` tokens are not part of the
category results

[source,console-result]
--------------------------------------------------
{
  "aggregations" : {
    "categories" : {
      "buckets" : [
        {
          "doc_count" : 3,
          "key" : "Node shutting down",
          "max_matching_length" : 49
        },
        {
          "doc_count" : 1,
          "key" : "Node starting up",
          "max_matching_length" : 47
        },
        {
          "doc_count" : 1,
          "key" : "User logged off",
          "max_matching_length" : 52
        },
        {
          "doc_count" : 1,
          "key" : "User logging on",
          "max_matching_length" : 52
        }
      ]
    }
  }
}
--------------------------------------------------

Here is an example using `categorization_filters`.
The default analyzer uses the `ml_standard` tokenizer which is similar to a whitespace tokenizer
but filters out tokens that could be interpreted as hexadecimal numbers. The default analyzer
also uses the `first_line_with_letters` character filter, so that only the first meaningful line
of multi-line messages is considered.
But, it may be that a token is a known highly-variable token (formatted usernames, emails, etc.). In that case, it is good to supply
custom `categorization_filters` to filter out those tokens for better categories. These filters may also reduce memory usage as fewer
tokens are held in memory for the categories. (If there are sufficient examples of different usernames, emails, etc., then
categories will form that naturally discard them as variables, but for small input data where only one example exists this won't
happen.)

[source,console]
--------------------------------------------------
POST log-messages/_search?filter_path=aggregations
{
  "aggs": {
    "categories": {
      "categorize_text": {
        "field": "message",
        "categorization_filters": ["\\w+\\_\\d{3}"], <1>
        "similarity_threshold": 11 <2>
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:categorize_text]
<1> The filters to apply to the analyzed tokens. It filters
out tokens like `bar_123`.
<2> Require 11% of token weight to match before adding a message to an
    existing category rather than creating a new one.

The resulting categories are now very broad, merging the log groups.
(A `similarity_threshold` of 11% is generally too low. Settings over
50% are usually better.)

[source,console-result]
--------------------------------------------------
{
  "aggregations" : {
    "categories" : {
      "buckets" : [
        {
          "doc_count" : 4,
          "key" : "Node",
          "max_matching_length" : 49
        },
        {
          "doc_count" : 2,
          "key" : "User",
          "max_matching_length" : 52
        }
      ]
    }
  }
}
--------------------------------------------------

This aggregation can have both sub-aggregations and itself be a sub-aggregation. This allows gathering the top daily categories and the
top sample doc as below.

[source,console]
--------------------------------------------------
POST log-messages/_search?filter_path=aggregations
{
  "aggs": {
    "daily": {
      "date_histogram": {
        "field": "time",
        "fixed_interval": "1d"
      },
      "aggs": {
        "categories": {
          "categorize_text": {
            "field": "message",
            "categorization_filters": ["\\w+\\_\\d{3}"]
          },
          "aggs": {
            "hit": {
              "top_hits": {
                "size": 1,
                "sort": ["time"],
                "_source": "message"
              }
            }
          }
        }
      }
    }
  }
}
--------------------------------------------------
// TEST[setup:categorize_text]

[source,console-result]
--------------------------------------------------
{
  "aggregations" : {
    "daily" : {
      "buckets" : [
        {
          "key_as_string" : "2016-02-07T00:00:00.000Z",
          "key" : 1454803200000,
          "doc_count" : 3,
          "categories" : {
            "buckets" : [
              {
                "doc_count" : 2,
                "key" : "Node shutting down",
                "max_matching_length" : 49,
                "hit" : {
                  "hits" : {
                    "total" : {
                      "value" : 2,
                      "relation" : "eq"
                    },
                    "max_score" : null,
                    "hits" : [
                      {
                        "_index" : "log-messages",
                        "_id" : "1",
                        "_score" : null,
                        "_source" : {
                          "message" : "2016-02-07T00:00:00+0000 Node 3 shutting down"
                        },
                        "sort" : [
                          1454803260000
                        ]
                      }
                    ]
                  }
                }
              },
              {
                "doc_count" : 1,
                "key" : "Node starting up",
                "max_matching_length" : 47,
                "hit" : {
                  "hits" : {
                    "total" : {
                      "value" : 1,
                      "relation" : "eq"
                    },
                    "max_score" : null,
                    "hits" : [
                      {
                        "_index" : "log-messages",
                        "_id" : "2",
                        "_score" : null,
                        "_source" : {
                          "message" : "2016-02-07T00:00:00+0000 Node 5 starting up"
                        },
                        "sort" : [
                          1454803320000
                        ]
                      }
                    ]
                  }
                }
              }
            ]
          }
        },
        {
          "key_as_string" : "2016-02-08T00:00:00.000Z",
          "key" : 1454889600000,
          "doc_count" : 3,
          "categories" : {
            "buckets" : [
              {
                "doc_count" : 1,
                "key" : "Node shutting down",
                "max_matching_length" : 49,
                "hit" : {
                  "hits" : {
                    "total" : {
                      "value" : 1,
                      "relation" : "eq"
                    },
                    "max_score" : null,
                    "hits" : [
                      {
                        "_index" : "log-messages",
                        "_id" : "4",
                        "_score" : null,
                        "_source" : {
                          "message" : "2016-02-08T00:00:00+0000 Node 5 shutting down"
                        },
                        "sort" : [
                          1454889660000
                        ]
                      }
                    ]
                  }
                }
              },
              {
                "doc_count" : 1,
                "key" : "User logged off",
                "max_matching_length" : 52,
                "hit" : {
                  "hits" : {
                    "total" : {
                      "value" : 1,
                      "relation" : "eq"
                    },
                    "max_score" : null,
                    "hits" : [
                      {
                        "_index" : "log-messages",
                        "_id" : "6",
                        "_score" : null,
                        "_source" : {
                          "message" : "2016-02-08T00:00:00+0000 User foo_864 logged off"
                        },
                        "sort" : [
                          1454889840000
                        ]
                      }
                    ]
                  }
                }
              },
              {
                "doc_count" : 1,
                "key" : "User logging on",
                "max_matching_length" : 52,
                "hit" : {
                  "hits" : {
                    "total" : {
                      "value" : 1,
                      "relation" : "eq"
                    },
                    "max_score" : null,
                    "hits" : [
                      {
                        "_index" : "log-messages",
                        "_id" : "5",
                        "_score" : null,
                        "_source" : {
                          "message" : "2016-02-08T00:00:00+0000 User foo_325 logging on"
                        },
                        "sort" : [
                          1454889720000
                        ]
                      }
                    ]
                  }
                }
              }
            ]
          }
        }
      ]
    }
  }
}
--------------------------------------------------
