230 lines
9.4 KiB
Plaintext
230 lines
9.4 KiB
Plaintext
[role="xpack"]
|
|
[[ml-configuring-categories]]
|
|
=== Categorizing log messages
|
|
|
|
Application log events are often unstructured and contain variable data. For
|
|
example:
|
|
//Obtained from it_ops_new_app_logs.json
|
|
[source,js]
|
|
----------------------------------
|
|
{"time":1454516381000,"message":"org.jdbi.v2.exceptions.UnableToExecuteStatementException: com.mysql.jdbc.exceptions.MySQLTimeoutException: Statement cancelled due to timeout or client request [statement:\"SELECT id, customer_id, name, force_disabled, enabled FROM customers\"]","type":"logs"}
|
|
----------------------------------
|
|
//NOTCONSOLE
|
|
|
|
You can use {ml} to observe the static parts of the message, cluster similar
|
|
messages together, and classify them into message categories.
|
|
|
|
The {ml} model learns what volume and pattern is normal for each category over
|
|
time. You can then detect anomalies and surface rare events or unusual types of
|
|
messages by using count or rare functions. For example:
|
|
|
|
//Obtained from it_ops_new_app_logs.sh
|
|
[source,js]
|
|
----------------------------------
|
|
PUT _xpack/ml/anomaly_detectors/it_ops_new_logs
|
|
{
|
|
"description" : "IT Ops Application Logs",
|
|
"analysis_config" : {
|
|
"categorization_field_name": "message", <1>
|
|
"bucket_span":"30m",
|
|
"detectors" :[{
|
|
"function":"count",
|
|
"by_field_name": "mlcategory", <2>
|
|
"detector_description": "Unusual message counts"
|
|
}],
|
|
"categorization_filters":[ "\\[statement:.*\\]"]
|
|
},
|
|
"analysis_limits":{
|
|
"categorization_examples_limit": 5
|
|
},
|
|
"data_description" : {
|
|
"time_field":"time",
|
|
"time_format": "epoch_ms"
|
|
}
|
|
}
|
|
----------------------------------
|
|
//CONSOLE
|
|
<1> The `categorization_field_name` property indicates which field will be
|
|
categorized.
|
|
<2> The resulting categories are used in a detector by setting `by_field_name`,
|
|
`over_field_name`, or `partition_field_name` to the keyword `mlcategory`. If you
|
|
do not specify this keyword in one of those properties, the API request fails.
|
|
|
|
The optional `categorization_examples_limit` property specifies the
|
|
maximum number of examples that are stored in memory and in the results data
|
|
store for each category. The default value is `4`. Note that this setting does
|
|
not affect the categorization; it just affects the list of visible examples. If
|
|
you increase this value, more examples are available, but you must have more
|
|
storage available. If you set this value to `0`, no examples are stored.
|
|
|
|
The optional `categorization_filters` property can contain an array of regular
|
|
expressions. If a categorization field value matches the regular expression, the
|
|
portion of the field that is matched is not taken into consideration when
|
|
defining categories. The categorization filters are applied in the order they
|
|
are listed in the job configuration, which allows you to disregard multiple
|
|
sections of the categorization field value. In this example, we have decided that
|
|
we do not want the detailed SQL to be considered in the message categorization.
|
|
This particular categorization filter removes the SQL statement from the categorization
|
|
algorithm.
|
|
|
|
If your data is stored in {es}, you can create an advanced job with these same
|
|
properties:
|
|
|
|
[role="screenshot"]
|
|
image::images/ml-category-advanced.jpg["Advanced job configuration options related to categorization"]
|
|
|
|
NOTE: To add the `categorization_examples_limit` property, you must use the
|
|
**Edit JSON** tab and copy the `analysis_limits` object from the API example.
|
|
|
|
[float]
|
|
[[ml-configuring-analyzer]]
|
|
==== Customizing the categorization analyzer
|
|
|
|
Categorization uses English dictionary words to identify log message categories.
|
|
By default, it also uses English tokenization rules. For this reason, if you use
|
|
the default categorization analyzer, only English language log messages are
|
|
supported, as described in the <<ml-limitations>>.
|
|
|
|
You can, however, change the tokenization rules by customizing the way the
|
|
categorization field values are interpreted. For example:
|
|
|
|
[source,js]
|
|
----------------------------------
|
|
PUT _xpack/ml/anomaly_detectors/it_ops_new_logs2
|
|
{
|
|
"description" : "IT Ops Application Logs",
|
|
"analysis_config" : {
|
|
"categorization_field_name": "message",
|
|
"bucket_span":"30m",
|
|
"detectors" :[{
|
|
"function":"count",
|
|
"by_field_name": "mlcategory",
|
|
"detector_description": "Unusual message counts"
|
|
}],
|
|
"categorization_analyzer":{
|
|
"char_filter": [
|
|
{ "type": "pattern_replace", "pattern": "\\[statement:.*\\]" } <1>
|
|
],
|
|
"tokenizer": "ml_classic", <2>
|
|
"filter": [
|
|
{ "type" : "stop", "stopwords": [
|
|
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday",
|
|
"Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun",
|
|
"January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December",
|
|
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec",
|
|
"GMT", "UTC"
|
|
] } <3>
|
|
]
|
|
}
|
|
},
|
|
"analysis_limits":{
|
|
"categorization_examples_limit": 5
|
|
},
|
|
"data_description" : {
|
|
"time_field":"time",
|
|
"time_format": "epoch_ms"
|
|
}
|
|
}
|
|
----------------------------------
|
|
//CONSOLE
|
|
<1> The
|
|
{ref}/analysis-pattern-replace-charfilter.html[`pattern_replace` character filter]
|
|
here achieves exactly the same as the `categorization_filters` in the first
|
|
example.
|
|
<2> The `ml_classic` tokenizer works like the non-customizable tokenization
|
|
that was used for categorization in older versions of machine learning. If you
|
|
want the same categorization behavior as older versions, use this property value.
|
|
<3> By default, English day or month words are filtered from log messages before
|
|
categorization. If your logs are in a different language and contain
|
|
dates, you might get better results by filtering the day or month words in your
|
|
language.
|
|
|
|
The optional `categorization_analyzer` property allows even greater customization
|
|
of how categorization interprets the categorization field value. It can refer to
|
|
a built-in {es} analyzer or a combination of zero or more character filters,
|
|
a tokenizer, and zero or more token filters.
|
|
|
|
The `ml_classic` tokenizer and the day and month stopword filter are more or less
|
|
equivalent to the following analyzer, which is defined using only built-in {es}
|
|
{ref}/analysis-tokenizers.html[tokenizers] and
|
|
{ref}/analysis-tokenfilters.html[token filters]:
|
|
|
|
[source,js]
|
|
----------------------------------
|
|
PUT _xpack/ml/anomaly_detectors/it_ops_new_logs3
|
|
{
|
|
"description" : "IT Ops Application Logs",
|
|
"analysis_config" : {
|
|
"categorization_field_name": "message",
|
|
"bucket_span":"30m",
|
|
"detectors" :[{
|
|
"function":"count",
|
|
"by_field_name": "mlcategory",
|
|
"detector_description": "Unusual message counts"
|
|
}],
|
|
"categorization_analyzer":{
|
|
"tokenizer": {
|
|
"type" : "simple_pattern_split",
|
|
"pattern" : "[^-0-9A-Za-z_.]+" <1>
|
|
},
|
|
"filter": [
|
|
{ "type" : "pattern_replace", "pattern": "^[0-9].*" }, <2>
|
|
{ "type" : "pattern_replace", "pattern": "^[-0-9A-Fa-f.]+$" }, <3>
|
|
{ "type" : "pattern_replace", "pattern": "^[^0-9A-Za-z]+" }, <4>
|
|
{ "type" : "pattern_replace", "pattern": "[^0-9A-Za-z]+$" }, <5>
|
|
{ "type" : "stop", "stopwords": [
|
|
"",
|
|
"Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday", "Sunday",
|
|
"Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun",
|
|
"January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December",
|
|
"Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec",
|
|
"GMT", "UTC"
|
|
] }
|
|
]
|
|
}
|
|
},
|
|
"analysis_limits":{
|
|
"categorization_examples_limit": 5
|
|
},
|
|
"data_description" : {
|
|
"time_field":"time",
|
|
"time_format": "epoch_ms"
|
|
}
|
|
}
|
|
----------------------------------
|
|
//CONSOLE
|
|
<1> Tokens basically consist of hyphens, digits, letters, underscores and dots.
|
|
<2> By default, categorization ignores tokens that begin with a digit.
|
|
<3> By default, categorization also ignores tokens that are hexadecimal numbers.
|
|
<4> Underscores, hyphens, and dots are removed from the beginning of tokens.
|
|
<5> Underscores, hyphens, and dots are also removed from the end of tokens.
|
|
|
|
The key difference between the default `categorization_analyzer` and this example
|
|
analyzer is that using the `ml_classic` tokenizer is several times faster. The
|
|
difference in behavior is that this custom analyzer does not include accented
|
|
letters in tokens whereas the `ml_classic` tokenizer does, although that could
|
|
be fixed by using more complex regular expressions.
|
|
|
|
For more information about the `categorization_analyzer` property, see
|
|
{ref}/ml-job-resource.html#ml-categorizationanalyzer[Categorization Analyzer].
|
|
|
|
NOTE: To add the `categorization_analyzer` property in {kib}, you must use the
|
|
**Edit JSON** tab and copy the `categorization_analyzer` object from one of the
|
|
API examples above.
|
|
|
|
[float]
|
|
[[ml-viewing-categories]]
|
|
==== Viewing categorization results
|
|
|
|
After you open the job and start the {dfeed} or supply data to the job, you can
|
|
view the categorization results in {kib}. For example:
|
|
|
|
[role="screenshot"]
|
|
image::images/ml-category-anomalies.jpg["Categorization example in the Anomaly Explorer"]
|
|
|
|
For this type of job, the **Anomaly Explorer** contains extra information for
|
|
each anomaly: the name of the category (for example, `mlcategory 11`) and
|
|
examples of the messages in that category. In this case, you can use these
|
|
details to investigate occurrences of unusually high message counts for specific
|
|
message categories.
|