OpenSearch/docs/reference/ml/anomaly-detection/aggregations.asciidoc

[role="xpack"]
[[ml-configuring-aggregation]]
=== Aggregating data for faster performance

By default, {dfeeds} fetch data from {es} using search and scroll requests.
It can be significantly more efficient, however, to aggregate data in {es}
and to configure your {anomaly-jobs} to analyze aggregated data.

One of the benefits of aggregating data this way is that {es} automatically
distributes these calculations across your cluster. You can then feed this
aggregated data into the {ml-features} instead of raw results, which
reduces the volume of data that must be considered while detecting anomalies.

TIP: 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 {dfeeds}. 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-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 {dfeed}, it must also be divisible by this
interval.

TIP: As a rule of thumb, if your detectors use <<ml-metric-functions,metric>> or
<<ml-sum-functions,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 <<ml-count-functions,count>> or <<ml-rare-functions,rare>>
functions, set the interval to the same value as the bucket span.

When you create or update an {anomaly-job}, you can include the names of
aggregations, for example:

[source,console]
----------------------------------
PUT _ml/anomaly_detectors/farequote
{
  "analysis_config": {
    "bucket_span": "60m",
    "detectors": [{
      "function": "mean",
      "field_name": "responsetime",  <1>
      "by_field_name": "airline"  <1>
    }],
    "summary_count_field_name": "doc_count"
  },
  "data_description": {
    "time_field":"time"  <1>
  }
}
----------------------------------
// TEST[skip:setup:farequote_data]

<1> In this example, the `airline`, `responsetime`, and `time` fields are
aggregations. Only the aggregated fields defined in the `analysis_config` object 
are analyzed by the {anomaly-job}.

NOTE: 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 {dfeed} as follows:

[source,console]
----------------------------------
PUT _ml/datafeeds/datafeed-farequote
{
  "job_id":"farequote",
  "indices": ["farequote"],
  "aggregations": {
    "buckets": {
      "date_histogram": {
        "field": "time",
        "fixed_interval": "360s",
        "time_zone": "UTC"
      },
      "aggregations": {
        "time": {  <1>
          "max": {"field": "time"}
        },
        "airline": {  <1>
          "terms": {
            "field": "airline",
            "size": 100
          },
          "aggregations": {
            "responsetime": {  <1>
              "avg": {
                "field": "responsetime"
              }
            }
          }
        }
      }
    }
  }
}
----------------------------------
// TEST[skip:setup:farequote_job]

<1> 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`. 

IMPORTANT: Your {dfeed} can contain multiple aggregations, but only the ones
with names that match values in the job configuration are fed to the job.

{dfeeds-cap} 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`.

[source,js]
----------------------------------
"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"
            }
          }
        }
      }
    }
  }
}
----------------------------------
// NOTCONSOLE

{dfeeds-cap} 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.

[source,js]
----------------------------------
{
  "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"
              }
            }
          }
        }
      }
    }
  }
}
----------------------------------
// NOTCONSOLE

When you define an aggregation in a {dfeed}, it must have the following form:

[source,js]
----------------------------------
"aggregations": {
  ["bucketing_aggregation": {
    "bucket_agg": {
      ...
    },
    "aggregations": {]
      "data_histogram_aggregation": {
        "date_histogram": {
          "field": "time",
        },
        "aggregations": {
          "timestamp": {
            "max": {
              "field": "time"
            }
          },
          [,"<first_term>": {
            "terms":{...
            }
            [,"aggregations" : {
              [<sub_aggregation>]+
            } ]
          }]
        }
      }
    }
  }
}
----------------------------------
// NOTCONSOLE

The top level aggregation must be either a
{ref}/search-aggregations-bucket.html[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 one `date_histogram`
aggregation. For more information, see
{ref}/search-aggregations-bucket-datehistogram-aggregation.html[Date histogram aggregation].

NOTE: 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 {dfeeds}, however, because they need to know the
time of the latest record within a bucket. Otherwise, when you restart a {dfeed},
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.

IMPORTANT: 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:

[source,js]
--------------------------------------------------
GET .../_search {
  "aggs": {
    "service_cardinality": {
      "cardinality": {
        "field": "service"
        }
    }
  }
}
--------------------------------------------------
// NOTCONSOLE

By default, {es} 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 {dfeed}. For more
information, see
{ref}/search-aggregations-bucket-terms-aggregation.html[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 {ref}/search-aggregations.html[Aggregations].