Categorize text aggregationedit
This functionality is in technical preview and may be changed or removed in a future release. Elastic will work to fix any issues, but features in technical preview are not subject to the support SLA of official GA features.
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.
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 sampler or
diversified sampler to explore the created categories.
Parametersedit
-
field - (Required, string) The semi-structured text field to categorize.
-
max_unique_tokens -
(Optional, integer, default:
50) The maximum number of unique tokens at any position up tomax_matched_tokens. Must be larger than 1. Smaller values use less memory and create fewer categories. Larger values will use more memory and create narrower categories. Max allowed value is100. -
max_matched_tokens -
(Optional, integer, default:
5) The maximum number of token positions to match on before attempting to merge categories. Larger values will use more memory and create narrower categories. Max allowed value is100.
Example:
max_matched_tokens of 2 would disallow merging of the categories
[foo bar baz]
[foo baz bozo]
As the first 2 tokens are required to match for the category.
Once max_unique_tokens is reached at a given position, a new * token is
added and all new tokens at that position are matched by the * token.
-
similarity_threshold -
(Optional, integer, default:
50) The minimum percentage of tokens 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. -
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 thecategorization_analyzerproperty instead and include the filters aspattern_replacecharacter filters. -
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
analyzerin the Analyze endpoint. This property cannot be used at the same time ascategorization_filters.The
categorization_analyzerfield can be specified either as a string or as an object. If it is a string it must refer to a built-in analyzer or one added by another plugin. If it is an object it has the following properties:Properties of
categorization_analyzer-
char_filter -
(array of strings or objects)
One or more character filters. In addition to the
built-in character filters, other plugins can provide more character filters.
This property is optional. If it is not specified, no character filters are
applied prior to categorization. If you are customizing some other aspect of the
analyzer and you need to achieve the equivalent of
categorization_filters(which are not permitted when some other aspect of the analyzer is customized), add them here as pattern replace character filters. -
tokenizer -
(string or object)
The name or definition of the tokenizer to use after
character filters are applied. This property is compulsory if
categorization_analyzeris specified as an object. Machine learning provides a tokenizer calledml_standardthat tokenizes in a way that has been determined to produce good categorization results on a variety of log file formats for logs in English. If you want to use that tokenizer but change the character or token filters, specify"tokenizer": "ml_standard"in yourcategorization_analyzer. Additionally, theml_classictokenizer is available, which tokenizes in the same way as the non-customizable tokenizer in old versions of the product (before 6.2).ml_classicwas the default categorization tokenizer in versions 6.2 to 7.13, so if you need categorization identical to the default for jobs created in these versions, specify"tokenizer": "ml_classic"in yourcategorization_analyzer. -
filter - (array of strings or objects) One or more token filters. In addition to the built-in token filters, other plugins can provide more token filters. This property is optional. If it is not specified, no token filters are applied prior to categorization.
-
-
shard_size - (Optional, integer) The number of categorization buckets to return from each shard before merging all the results.
-
size -
(Optional, integer, default:
10) The number of buckets to return. -
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.
Basic useedit
Re-analyzing large result sets will require a lot of time and memory. This aggregation should be used in conjunction with Async search. Additionally, you may consider using the aggregation as a child of either the sampler or diversified sampler aggregation. This will typically improve speed and memory use.
Example:
POST log-messages/_search?filter_path=aggregations
{
"aggs": {
"categories": {
"categorize_text": {
"field": "message"
}
}
}
}
Response:
{
"aggregations" : {
"categories" : {
"buckets" : [
{
"doc_count" : 3,
"key" : "Node shutting down"
},
{
"doc_count" : 1,
"key" : "Node starting up"
},
{
"doc_count" : 1,
"key" : "User foo_325 logging on"
},
{
"doc_count" : 1,
"key" : "User foo_864 logged off"
}
]
}
}
}
Here is an example using categorization_filters
POST log-messages/_search?filter_path=aggregations
{
"aggs": {
"categories": {
"categorize_text": {
"field": "message",
"categorization_filters": ["\\w+\\_\\d{3}"]
}
}
}
}
Note how the foo_<number> tokens are not part of the
category results
{
"aggregations" : {
"categories" : {
"buckets" : [
{
"doc_count" : 3,
"key" : "Node shutting down"
},
{
"doc_count" : 1,
"key" : "Node starting up"
},
{
"doc_count" : 1,
"key" : "User logged off"
},
{
"doc_count" : 1,
"key" : "User logging on"
}
]
}
}
}
Here is an example using categorization_filters.
The default analyzer is a whitespace analyzer with a custom token filter
which filters out tokens that start with any number.
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 will also reduce memory usage as fewer
tokens are held in memory for the categories.
POST log-messages/_search?filter_path=aggregations
{
"aggs": {
"categories": {
"categorize_text": {
"field": "message",
"categorization_filters": ["\\w+\\_\\d{3}"],
"max_matched_tokens": 2,
"similarity_threshold": 30
}
}
}
}
|
The filters to apply to the analyzed tokens. It filters
out tokens like |
|
|
Require at least 2 tokens before the log categories attempt to merge together |
|
|
Require 30% of the tokens to match before expanding a log categories to add a new log entry |
The resulting categories are now broad, matching the first token and merging the log groups.
{
"aggregations" : {
"categories" : {
"buckets" : [
{
"doc_count" : 4,
"key" : "Node *"
},
{
"doc_count" : 2,
"key" : "User *"
}
]
}
}
}
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.
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"
}
}
}
}
}
}
}
}
{
"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",
"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",
"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",
"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",
"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",
"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
]
}
]
}
}
}
]
}
}
]
}
}
}