Aggregating data for faster performance
editAggregating data for faster performance
editBy default, datafeeds fetch data from Elasticsearch using search and scroll requests. It can be significantly more efficient, however, to aggregate data in Elasticsearch and to configure your anomaly detection jobs to analyze aggregated data.
One of the benefits of aggregating data this way is that Elasticsearch automatically distributes these calculations across your cluster. You can then feed this aggregated data into the machine learning features instead of raw results, which reduces the volume of data that must be considered while detecting anomalies.
If you use a terms aggregation and the cardinality of a term is high, the aggregation might not be effective and you might want to just use the default search and scroll behavior.
There are some limitations to using aggregations in datafeeds. Your aggregation
must include a date_histogram aggregation, which in turn must contain a max
aggregation on the time field. This requirement ensures that the aggregated data
is a time series and the timestamp of each bucket is the time of the last record
in the bucket.
You must also consider the interval of the date histogram aggregation carefully.
The bucket span of your anomaly detection job must be divisible by the value of the
calendar_interval or fixed_interval in your aggregation (with no remainder).
If you specify a frequency for your datafeed, it must also be divisible by this
interval.
As a rule of thumb, if your detectors use metric or sum analytical functions, set the date histogram aggregation interval to a tenth of the bucket span. This suggestion creates finer, more granular time buckets, which are ideal for this type of analysis. If your detectors use count or rare functions, set the interval to the same value as the bucket span.
When you create or update an anomaly detection job, you can include the names of aggregations, for example:
PUT _ml/anomaly_detectors/farequote
{
"analysis_config": {
"bucket_span": "60m",
"detectors": [{
"function": "mean",
"field_name": "responsetime",
"by_field_name": "airline"
}],
"summary_count_field_name": "doc_count"
},
"data_description": {
"time_field":"time"
}
}
In this example, the airline, responsetime, and time fields are
aggregations.
When the summary_count_field_name property is set to a non-null value,
the job expects to receive aggregated input. The property must be set to the
name of the field that contains the count of raw data points that have been
aggregated. It applies to all detectors in the job.
The aggregations are defined in the datafeed as follows:
PUT _ml/datafeeds/datafeed-farequote
{
"job_id":"farequote",
"indices": ["farequote"],
"aggregations": {
"buckets": {
"date_histogram": {
"field": "time",
"fixed_interval": "360s",
"time_zone": "UTC"
},
"aggregations": {
"time": {
"max": {"field": "time"}
},
"airline": {
"terms": {
"field": "airline",
"size": 100
},
"aggregations": {
"responsetime": {
"avg": {
"field": "responsetime"
}
}
}
}
}
}
}
}
In this example, the aggregations have names that match the fields that they
operate on. That is to say, the max aggregation is named time and its
field is also time. The same is true for the aggregations with the names
airline and responsetime. Since you must create the job before you can
create the datafeed, synchronizing your aggregation and field names can simplify
these configuration steps.
If you use a max aggregation on a time field, the aggregation name
in the datafeed must match the name of the time field, as in the previous example.
For all other aggregations, if the aggregation name doesn’t match the field name,
there are limitations in the drill-down functionality within the machine learning page in
Kibana.
Datafeeds support complex nested aggregations, this example uses the derivative
pipeline aggregation to find the first order derivative of the counter
system.network.out.bytes for each value of the field beat.name.
"aggregations": {
"beat.name": {
"terms": {
"field": "beat.name"
},
"aggregations": {
"buckets": {
"date_histogram": {
"field": "@timestamp",
"fixed_interval": "5m"
},
"aggregations": {
"@timestamp": {
"max": {
"field": "@timestamp"
}
},
"bytes_out_average": {
"avg": {
"field": "system.network.out.bytes"
}
},
"bytes_out_derivative": {
"derivative": {
"buckets_path": "bytes_out_average"
}
}
}
}
}
}
}
Datafeeds not only supports multi-bucket aggregations, but also single bucket
aggregations. The following shows two filter aggregations, each gathering the
number of unique entries for the error field.
{
"job_id":"servers-unique-errors",
"indices": ["logs-*"],
"aggregations": {
"buckets": {
"date_histogram": {
"field": "time",
"interval": "360s",
"time_zone": "UTC"
},
"aggregations": {
"time": {
"max": {"field": "time"}
}
"server1": {
"filter": {"term": {"source": "server-name-1"}},
"aggregations": {
"server1_error_count": {
"value_count": {
"field": "error"
}
}
}
},
"server2": {
"filter": {"term": {"source": "server-name-2"}},
"aggregations": {
"server2_error_count": {
"value_count": {
"field": "error"
}
}
}
}
}
}
}
}
When you define an aggregation in a datafeed, it must have the following form:
"aggregations": {
["bucketing_aggregation": {
"bucket_agg": {
...
},
"aggregations": {]
"data_histogram_aggregation": {
"date_histogram": {
"field": "time",
},
"aggregations": {
"timestamp": {
"max": {
"field": "time"
}
},
[,"<first_term>": {
"terms":{...
}
[,"aggregations" : {
[<sub_aggregation>]+
} ]
}]
}
}
}
}
}
The top level aggregation must be either a
bucket aggregation containing as single
sub-aggregation that is a date_histogram or the top level aggregation is the
required date_histogram. There must be exactly 1 date_histogram aggregation.
For more information, see
Date histogram aggregation.
The time_zone parameter in the date histogram aggregation must be set to
UTC, which is the default value.
Each histogram bucket has a key, which is the bucket start time. This key cannot be used for aggregations in datafeeds, however, because they need to know the time of the latest record within a bucket. Otherwise, when you restart a datafeed, it continues from the start time of the histogram bucket and possibly fetches the same data twice. The max aggregation for the time field is therefore necessary to provide the time of the latest record within a bucket.
You can optionally specify a terms aggregation, which creates buckets for different values of a field.
If you use a terms aggregation, by default it returns buckets for the top ten terms. Thus if the cardinality of the term is greater than 10, not all terms are analyzed.
You can change this behavior by setting the size parameter. To
determine the cardinality of your data, you can run searches such as:
GET .../_search {
"aggs": {
"service_cardinality": {
"cardinality": {
"field": "service"
}
}
}
}
By default, Elasticsearch limits the maximum number of terms returned to 10000. For high cardinality fields, the query might not run. It might return errors related to circuit breaking exceptions that indicate that the data is too large. In such cases, do not use aggregations in your datafeed. For more information, see Terms aggregation.
You can also optionally specify multiple sub-aggregations. The sub-aggregations are aggregated for the buckets that were created by their parent aggregation. For more information, see Aggregations.