Get overall buckets API

GET _ml/anomaly_detectors/<job_id>/results/overall_buckets

GET _ml/anomaly_detectors/<job_id>,<job_id>/results/overall_buckets

GET _ml/anomaly_detectors/_all/results/overall_buckets

Requires the monitor_ml cluster privilege. This privilege is included in the machine_learning_user built-in role.

By default, an overall bucket has a span equal to the largest bucket span of the specified anomaly detection jobs. To override that behavior, use the optional bucket_span parameter. To learn more about the concept of buckets, see Buckets.

The overall_score is calculated by combining the scores of all the buckets within the overall bucket span. First, the maximum anomaly_score per anomaly detection job in the overall bucket is calculated. Then the top_n of those scores are averaged to result in the overall_score. This means that you can fine-tune the overall_score so that it is more or less sensitive to the number of jobs that detect an anomaly at the same time. For example, if you set top_n to 1, the overall_score is the maximum bucket score in the overall bucket. Alternatively, if you set top_n to the number of jobs, the overall_score is high only when all jobs detect anomalies in that overall bucket. If you set the bucket_span parameter (to a value greater than its default), the overall_score is the maximum overall_score of the overall buckets that have a span equal to the jobs' largest bucket span.

You can also specify the query parameters (such as allow_no_match and bucket_span) in the request body.

The API returns an array of overall bucket objects, which have the following properties:

GET _ml/anomaly_detectors/job-*/results/overall_buckets
{
  "overall_score": 80,
  "start": "1403532000000"
}

In this example, the API returns a single result that matches the specified score and time constraints. The overall_score is the max job score as top_n defaults to 1 when not specified:

{
  "count": 1,
  "overall_buckets": [
    {
      "timestamp" : 1403532000000,
      "bucket_span" : 3600,
      "overall_score" : 80.0,
      "jobs" : [
        {
          "job_id" : "job-1",
          "max_anomaly_score" : 30.0
        },
        {
          "job_id" : "job-2",
          "max_anomaly_score" : 10.0
        },
        {
          "job_id" : "job-3",
          "max_anomaly_score" : 80.0
        }
      ],
      "is_interim" : false,
      "result_type" : "overall_bucket"
    }
  ]
}

The next example is similar but this time top_n is set to 2:

GET _ml/anomaly_detectors/job-*/results/overall_buckets
{
  "top_n": 2,
  "overall_score": 50.0,
  "start": "1403532000000"
}

Note how the overall_score is now the average of the top 2 job scores:

{
  "count": 1,
  "overall_buckets": [
    {
      "timestamp" : 1403532000000,
      "bucket_span" : 3600,
      "overall_score" : 55.0,
      "jobs" : [
        {
          "job_id" : "job-1",
          "max_anomaly_score" : 30.0
        },
        {
          "job_id" : "job-2",
          "max_anomaly_score" : 10.0
        },
        {
          "job_id" : "job-3",
          "max_anomaly_score" : 80.0
        }
      ],
      "is_interim" : false,
      "result_type" : "overall_bucket"
    }
  ]
}