[role="xpack"]
[testenv="platinum"]
[[ml-get-job-stats]]
=== Get {anomaly-job} statistics API
++++
<titleabbrev>Get job statistics</titleabbrev>
++++

Retrieves usage information for {anomaly-jobs}.

[[ml-get-job-stats-request]]
==== {api-request-title}

`GET _ml/anomaly_detectors/<job_id>/_stats`

`GET _ml/anomaly_detectors/<job_id>,<job_id>/_stats` +

`GET _ml/anomaly_detectors/_stats` +

`GET _ml/anomaly_detectors/_all/_stats` 

[[ml-get-job-stats-prereqs]]
==== {api-prereq-title}

* If the {es} {security-features} are enabled, you must have `monitor_ml`,
`monitor`, `manage_ml`, or `manage` cluster privileges to use this API. See
<<security-privileges>>.

[[ml-get-job-stats-desc]]
==== {api-description-title}

You can get statistics for multiple {anomaly-jobs} in a single API request by
using a group name, a comma-separated list of jobs, or a wildcard expression.
You can get statistics for all {anomaly-jobs} by using `_all`, by specifying `*`
as the `<job_id>`, or by omitting the `<job_id>`.

IMPORTANT: This API returns a maximum of 10,000 jobs.

[[ml-get-job-stats-path-parms]]
==== {api-path-parms-title}

`<job_id>`::
(Optional, string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection-default]

[[ml-get-job-stats-query-parms]]
==== {api-query-parms-title}

`allow_no_jobs`::
(Optional, boolean)
include::{docdir}/ml/ml-shared.asciidoc[tag=allow-no-jobs]

[[ml-get-job-stats-results]]
==== {api-response-body-title}

The API returns the following information about the operational progress of a
job:

`assignment_explanation`::
(string) For open jobs only, contains messages relating to the selection
of a node to run the job.

[[datacounts]]`data_counts`::
(object) An object that describes the quantity of input to the job and any
related error counts. The `data_count` values are cumulative for the lifetime of
a job. If a model snapshot is reverted or old results are deleted, the job
counts are not reset.

`bucket_count`:::
(long) The number of bucket results produced by the job.

`earliest_record_timestamp`:::
(date) The timestamp of the earliest chronologically input document.

`empty_bucket_count`:::
(long) The number of buckets which did not contain any data. If your data
contains many empty buckets, consider increasing your `bucket_span` or using
functions that are tolerant to gaps in data such as `mean`, `non_null_sum` or
`non_zero_count`.

`input_bytes`:::
(long) The number of bytes of input data posted to the job.

`input_field_count`:::
(long) The total number of fields in input documents posted to the job. This
count includes fields that are not used in the analysis. However, be aware that
if you are using a {dfeed}, it extracts only the required fields from the
documents it retrieves before posting them to the job.

`input_record_count`:::
(long) The number of input documents posted to the job.

`invalid_date_count`:::
(long) The number of input documents with either a missing date field or a date
that could not be parsed.

`job_id`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]

`last_data_time`:::
(date) The timestamp at which data was last analyzed, according to server time.

`latest_empty_bucket_timestamp`:::
(date) The timestamp of the last bucket that did not contain any data.

`latest_record_timestamp`:::
(date) The timestamp of the latest chronologically input document.

`latest_sparse_bucket_timestamp`:::
(date) The timestamp of the last bucket that was considered sparse.

`missing_field_count`:::
(long) The number of input documents that are missing a field that the job is
configured to analyze. Input documents with missing fields are still processed
because it is possible that not all fields are missing. The value of
`processed_record_count` includes this count.
+
--
NOTE: If you are using {dfeeds} or posting data to the job in JSON format, a
high `missing_field_count` is often not an indication of data issues. It is not
necessarily a cause for concern.

--

`out_of_order_timestamp_count`:::
(long) The number of input documents that are out of time sequence and outside
of the latency window. This information is applicable only when you provide data
to the job by using the <<ml-post-data,post data API>>. These out of order
documents are  discarded, since jobs require time series data to be in ascending
chronological order.

`processed_field_count`:::
(long) The total number of fields in all the documents that have been processed
by the job. Only fields that are specified in the detector configuration object
contribute to this count. The timestamp is not included in this count.

`processed_record_count`:::
(long) The number of input documents that have been processed by the job. This
value includes documents with missing fields, since they are nonetheless
analyzed. If you use {dfeeds} and have aggregations in your search query, the
`processed_record_count` will be the number of aggregation results processed,
not the number of {es} documents.

`sparse_bucket_count`:::
(long) The number of buckets that contained few data points compared to the
expected number of data points. If your data contains many sparse buckets,
consider using a longer `bucket_span`.

[[forecastsstats]]`forecasts_stats`::
(object) An object that provides statistical information about forecasts
of this job. It has the following properties:
+
--
NOTE: Unless there is at least one forecast, `memory_bytes`, `records`,
`processing_time_ms` and `status` properties are omitted.

--

`forecasted_jobs`:::
(long) The number of jobs that have at least one forecast.

`memory_bytes`:::
(object) Statistics about the memory usage: minimum, maximum, average and total.

`records`:::
(object) Statistics about the number of forecast records: minimum, maximum,
saverage and total.

`processing_time_ms`:::
(object) Statistics about the forecast runtime in milliseconds: minimum, maximum,
average and total.

`status`:::
(object) Counts per forecast status. For example: `{"finished" : 2}`. 

`total`:::
(long) The number of forecasts currently available for this model.

`job_id`::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]

[[modelsizestats]]`model_size_stats`::
(object) An object that provides information about the size and contents of the
model. It has the following properties:
 
`bucket_allocation_failures_count`:::
(long) The number of buckets for which new entities in incoming data were not
processed due to insufficient model memory. This situation is also signified
by a `hard_limit: memory_status` property value.

`job_id`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]

`log_time`:::
(date) The timestamp of the `model_size_stats` according to server time.

`memory_status`:::
(string) The status of the mathematical models. This property can have one of
the following values:
+
--
* `ok`: The models stayed below the configured value.
* `soft_limit`: The models used more than 60% of the configured memory limit
and older unused models will be pruned to free up space.
* `hard_limit`: The models used more space than the configured memory limit.
As a result, not all incoming data was processed.
--

`model_bytes`:::
(long) The number of bytes of memory used by the models. This is the maximum
value since the last time the model was persisted. If the job is closed,
this value indicates the latest size.

`model_bytes_exceeded`:::
 (long) The number of bytes over the high limit for memory usage at the last
 allocation failure.

`model_bytes_memory_limit`:::
(long) The upper limit for memory usage, checked on increasing values.

`result_type`:::
(string) For internal use. The type of result.

`total_by_field_count`:::
(long) The number of `by` field values that were analyzed by the models.
+
--
NOTE: The `by` field values are counted separately for each detector and
partition.

--

`total_over_field_count`:::
(long) The number of `over` field values that were analyzed by the models.
+
--
NOTE: The `over` field values are counted separately for each detector and
partition.

--

`total_partition_field_count`:::
(long) The number of `partition` field values that were analyzed by the models.

`timestamp`:::
(date) The timestamp of the `model_size_stats` according to the timestamp of the
data.

[[stats-node]]`node`::
(object) Contains properties for the node that runs the job. This information is
available only for open jobs.

`attributes`:::
(object) Lists node attributes. For example,
`{"ml.machine_memory": "17179869184", "ml.max_open_jobs" : "20"}`.
  
`ephemeral_id`:::
(string) The ephemeral ID of the node.

`id`:::
(string) The unique identifier of the node.

`name`:::
(string) The node name.

`transport_address`:::
(string) The host and port where transport HTTP connections are accepted.

`open_time`::
(string) For open jobs only, the elapsed time for which the job has been open.
For example, `28746386s`.

`state`::
(string) The status of the job, which can be one of the following values:
+
--
* `closed`: The job finished successfully with its model state persisted. The
job must be opened before it can accept further data.
* `closing`: The job close action is in progress and has not yet completed. A
closing job cannot accept further data.
* `failed`: The job did not finish successfully due to an error. This situation
can occur due to invalid input data, a fatal error occurring during the analysis,
or an external interaction such as the process being killed by the Linux out of
memory (OOM) killer. If the job had irrevocably failed, it must be force closed
and then deleted. If the {dfeed} can be corrected, the job can be closed and
then re-opened.
* `opened`: The job is available to receive and process data.
* `opening`: The job open action is in progress and has not yet completed.
--

[[timingstats]]`timing_stats`::
(object) An object that provides statistical information about timing aspect of
this job. It has the following properties:

`average_bucket_processing_time_ms`:::
(double) Average of all bucket processing times in milliseconds.

`bucket_count`:::
(long) The number of buckets processed.

`exponential_average_bucket_processing_time_ms`:::
(double) Exponential moving average of all bucket processing times in
milliseconds.

`exponential_average_bucket_processing_time_per_hour_ms`:::
(double) Exponentially-weighted moving average of bucket processing times
calculated in a 1 hour time window.

`job_id`:::
(string)
include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection]

`maximum_bucket_processing_time_ms`:::
(double) Maximum among all bucket processing times in milliseconds.

`minimum_bucket_processing_time_ms`:::
(double) Minimum among all bucket processing times in milliseconds.

`total_bucket_processing_time_ms`:::
(double) Sum of all bucket processing times in milliseconds.

[[ml-get-job-stats-response-codes]]
==== {api-response-codes-title}

`404` (Missing resources)::
  If `allow_no_jobs` is `false`, this code indicates that there are no 
  resources that match the request or only partial matches for the request.

[[ml-get-job-stats-example]]
==== {api-examples-title}

[source,console]
--------------------------------------------------
GET _ml/anomaly_detectors/low_request_rate/_stats
--------------------------------------------------
// TEST[skip:Kibana sample data]

The API returns the following results:
[source,js]
----
{
  "count" : 1,
  "jobs" : [
    {
      "job_id" : "low_request_rate",
      "data_counts" : {
        "job_id" : "low_request_rate",
        "processed_record_count" : 1216,
        "processed_field_count" : 1216,
        "input_bytes" : 51678,
        "input_field_count" : 1216,
        "invalid_date_count" : 0,
        "missing_field_count" : 0,
        "out_of_order_timestamp_count" : 0,
        "empty_bucket_count" : 242,
        "sparse_bucket_count" : 0,
        "bucket_count" : 1457,
        "earliest_record_timestamp" : 1575172659612,
        "latest_record_timestamp" : 1580417369440,
        "last_data_time" : 1576017595046,
        "latest_empty_bucket_timestamp" : 1580356800000,
        "input_record_count" : 1216
      },
      "model_size_stats" : {
        "job_id" : "low_request_rate",
        "result_type" : "model_size_stats",
        "model_bytes" : 41480,
        "model_bytes_exceeded" : 0,
        "model_bytes_memory_limit" : 10485760,
        "total_by_field_count" : 3,
        "total_over_field_count" : 0,
        "total_partition_field_count" : 2,
        "bucket_allocation_failures_count" : 0,
        "memory_status" : "ok",
        "log_time" : 1576017596000,
        "timestamp" : 1580410800000
      },
      "forecasts_stats" : {
        "total" : 1,
        "forecasted_jobs" : 1,
        "memory_bytes" : {
          "total" : 9179.0,
          "min" : 9179.0,
          "avg" : 9179.0,
          "max" : 9179.0
        },
        "records" : {
          "total" : 168.0,
          "min" : 168.0,
          "avg" : 168.0,
          "max" : 168.0
        },
        "processing_time_ms" : {
          "total" : 40.0,
          "min" : 40.0,
          "avg" : 40.0,
          "max" : 40.0
        },
        "status" : {
          "finished" : 1
        }
      },
      "state" : "opened",
      "node" : {
        "id" : "7bmMXyWCRs-TuPfGJJ_yMw",
        "name" : "node-0",
        "ephemeral_id" : "hoXMLZB0RWKfR9UPPUCxXX",
        "transport_address" : "127.0.0.1:9300",
        "attributes" : {
          "ml.machine_memory" : "17179869184",
          "xpack.installed" : "true",
          "ml.max_open_jobs" : "20"
        }
      },
      "assignment_explanation" : "",
      "open_time" : "13s",
      "timing_stats" : {
        "job_id" : "low_request_rate",
        "bucket_count" : 1457,
        "total_bucket_processing_time_ms" : 1094.000000000001,
        "minimum_bucket_processing_time_ms" : 0.0,
        "maximum_bucket_processing_time_ms" : 48.0,
        "average_bucket_processing_time_ms" : 0.75085792724777,
        "exponential_average_bucket_processing_time_ms" : 0.5571716855800993,
        "exponential_average_bucket_processing_time_per_hour_ms" : 15.0
      }
    }
  ]
}
----
