Categorize text aggregation
editCategorize text aggregation
editA 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,
diversified sampler, or
random sampler to explore the created categories.
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.
Parameters
edit-
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.
From Elasticsearch 8.10.0, a new version number is used to track the configuration and state changes in the machine learning plugin. This new version number is decoupled from the product version and will increment independently.
-
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.
-
-
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. -
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.
Response body
edit-
key -
(string)
Consists of the tokens (extracted by the
categorization_analyzer) that are common to all values of the input field included in the category. -
doc_count - (integer) Number of documents matching the category.
-
max_matching_length -
(integer)
Categories from short messages containing few tokens may also match
categories containing many tokens derived from much longer messages.
max_matching_lengthis an indication of the maximum length of messages that should be considered to belong to the category. When searching for messages that match the category, any messages longer thanmax_matching_lengthshould be excluded. Use this field to prevent a search for members of a category of short messages from matching much longer ones. -
regex -
(string)
A regular expression that will match all values of the input field included
in the category. It is possible that the
regexdoes not incorporate every term inkey, if ordering varies between the values included in the category. However, in simple cases theregexwill be the ordered terms concatenated into a regular expression that allows for arbitrary sections in between them. It is not recommended to use theregexas the primary mechanism for searching for the original documents that were categorized. Search using a regular expression is very slow. Instead the terms in thekeyfield should be used to search for matching documents, as a terms search can use the inverted index and hence be much faster. However, there may be situations where it is useful to use theregexfield to test whether a small set of messages that have not been indexed match the category, or to confirm that the terms in thekeyoccur in the correct order in all the matched documents.
Basic use
editRe-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:
resp = client.search(
index="log-messages",
filter_path="aggregations",
aggs={
"categories": {
"categorize_text": {
"field": "message"
}
}
},
)
print(resp)
const response = await client.search({
index: "log-messages",
filter_path: "aggregations",
aggs: {
categories: {
categorize_text: {
field: "message",
},
},
},
});
console.log(response);
POST log-messages/_search?filter_path=aggregations
{
"aggs": {
"categories": {
"categorize_text": {
"field": "message"
}
}
}
}
Response:
{
"aggregations" : {
"categories" : {
"buckets" : [
{
"doc_count" : 3,
"key" : "Node shutting down",
"regex" : ".*?Node.+?shutting.+?down.*?",
"max_matching_length" : 49
},
{
"doc_count" : 1,
"key" : "Node starting up",
"regex" : ".*?Node.+?starting.+?up.*?",
"max_matching_length" : 47
},
{
"doc_count" : 1,
"key" : "User foo_325 logging on",
"regex" : ".*?User.+?foo_325.+?logging.+?on.*?",
"max_matching_length" : 52
},
{
"doc_count" : 1,
"key" : "User foo_864 logged off",
"regex" : ".*?User.+?foo_864.+?logged.+?off.*?",
"max_matching_length" : 52
}
]
}
}
}
Here is an example using categorization_filters
resp = client.search(
index="log-messages",
filter_path="aggregations",
aggs={
"categories": {
"categorize_text": {
"field": "message",
"categorization_filters": [
"\\w+\\_\\d{3}"
]
}
}
},
)
print(resp)
const response = await client.search({
index: "log-messages",
filter_path: "aggregations",
aggs: {
categories: {
categorize_text: {
field: "message",
categorization_filters: ["\\w+\\_\\d{3}"],
},
},
},
});
console.log(response);
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",
"regex" : ".*?Node.+?shutting.+?down.*?",
"max_matching_length" : 49
},
{
"doc_count" : 1,
"key" : "Node starting up",
"regex" : ".*?Node.+?starting.+?up.*?",
"max_matching_length" : 47
},
{
"doc_count" : 1,
"key" : "User logged off",
"regex" : ".*?User.+?logged.+?off.*?",
"max_matching_length" : 52
},
{
"doc_count" : 1,
"key" : "User logging on",
"regex" : ".*?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.)
resp = client.search(
index="log-messages",
filter_path="aggregations",
aggs={
"categories": {
"categorize_text": {
"field": "message",
"categorization_filters": [
"\\w+\\_\\d{3}"
],
"similarity_threshold": 11
}
}
},
)
print(resp)
const response = await client.search({
index: "log-messages",
filter_path: "aggregations",
aggs: {
categories: {
categorize_text: {
field: "message",
categorization_filters: ["\\w+\\_\\d{3}"],
similarity_threshold: 11,
},
},
},
});
console.log(response);
POST log-messages/_search?filter_path=aggregations
{
"aggs": {
"categories": {
"categorize_text": {
"field": "message",
"categorization_filters": ["\\w+\\_\\d{3}"],
"similarity_threshold": 11
}
}
}
}
|
The filters to apply to the analyzed tokens. It filters
out tokens like |
|
|
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.)
{
"aggregations" : {
"categories" : {
"buckets" : [
{
"doc_count" : 4,
"key" : "Node",
"regex" : ".*?Node.*?",
"max_matching_length" : 49
},
{
"doc_count" : 2,
"key" : "User",
"regex" : ".*?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.
resp = client.search(
index="log-messages",
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"
}
}
}
}
}
}
},
)
print(resp)
const response = await client.search({
index: "log-messages",
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",
},
},
},
},
},
},
},
});
console.log(response);
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",
"regex" : ".*?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",
"regex" : ".*?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",
"regex" : ".*?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",
"regex" : ".*?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",
"regex" : ".*?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
]
}
]
}
}
}
]
}
}
]
}
}
}