Get anomaly detection job statistics API
editGet anomaly detection job statistics API
editRetrieves usage information for anomaly detection jobs.
Request
editGET _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
Prerequisites
editRequires the monitor_ml
cluster privilege. This privilege is included in the
machine_learning_user
built-in role.
Description
editThis API returns a maximum of 10,000 jobs.
Path parameters
edit-
<job_id>
-
(Optional, string)
Identifier for the anomaly detection job. It can be a job identifier, a group
name, or a wildcard expression. You can get statistics for multiple
anomaly detection 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 detection jobs by using
_all
, by specifying*
as the job identifier, or by omitting the identifier.
Query parameters
edit-
allow_no_match
-
(Optional, Boolean) Specifies what to do when the request:
- Contains wildcard expressions and there are no jobs that match.
-
Contains the
_all
string or no identifiers and there are no matches. - Contains wildcard expressions and there are only partial matches.
The default value is
true
, which returns an emptyjobs
array when there are no matches and the subset of results when there are partial matches. If this parameter isfalse
, the request returns a404
status code when there are no matches or only partial matches.
Response body
editThe API returns the following information about the operational progress of a job:
-
assignment_explanation
- (string) For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.
-
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.Properties of
data_counts
-
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 asmean
,non_null_sum
ornon_zero_count
. -
input_bytes
- (long) The number of bytes of input data posted to the anomaly detection job.
-
input_field_count
- (long) The total number of fields in input documents posted to the anomaly detection job. This count includes fields that are not used in the analysis. However, be aware that if you are using a datafeed, 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 anomaly detection 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) Identifier for the anomaly detection job.
-
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.
-
log_time
-
(date) The timestamp of the
data_counts
according to server time. -
missing_field_count
-
(long) The number of input documents that are missing a field that the anomaly detection job is configured to analyze. Input documents with missing fields are still processed because it is possible that not all fields are missing.
If you are using datafeeds 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.The value of
processed_record_count
includes this count. -
out_of_order_timestamp_count
- (long) The number of input documents that have a timestamp chronologically preceding the start of the current anomaly detection bucket offset by the latency window. This information is applicable only when you provide data to the anomaly detection job by using the 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
- The total number of fields in all the documents that have been processed by the anomaly detection 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 anomaly detection job.
This value includes documents with missing fields, since they are nonetheless
analyzed. If you use datafeeds and have aggregations in your search query, the
processed_record_count
is the number of aggregation results processed, not the number of Elasticsearch 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
.
-
-
deleting
-
(Boolean)
Indicates that the process of deleting the job is in progress but not yet
completed. It is only reported when
true
.
-
forecasts_stats
-
(object) An object that provides statistical information about forecasts belonging to this job. Some statistics are omitted if no forecasts have been made.
Unless there is at least one forecast,
memory_bytes
,records
,processing_time_ms
andstatus
properties are omitted.Properties of
forecasts_stats
-
forecasted_jobs
-
(long) A value of
0
indicates that forecasts do not exist for this job. A value of1
indicates that at least one forecast exists. -
memory_bytes
-
(object) The
avg
,min
,max
andtotal
memory usage in bytes for forecasts related to this job. If there are no forecasts, this property is omitted. -
records
-
(object) The
avg
,min
,max
andtotal
number ofmodel_forecast
documents written for forecasts related to this job. If there are no forecasts, this property is omitted. -
processing_time_ms
-
(object) The
avg
,min
,max
andtotal
runtime in milliseconds for forecasts related to this job. If there are no forecasts, this property is omitted. -
status
- (object) The count of forecasts by their status. For example: {"finished" : 2, "started" : 1}. If there are no forecasts, this property is omitted.
-
total
-
(long)
The number of individual forecasts currently available for the job. A value of
1
or more indicates that forecasts exist.
-
-
job_id
- (string) Identifier for the anomaly detection job.
-
model_size_stats
-
(object) An object that provides information about the size and contents of the model.
Properties of
model_size_stats
-
assignment_memory_basis
-
(string) Indicates where to find the memory requirement that is used to decide where the job runs. The possible values are:
-
model_memory_limit
: The job’s memory requirement is calculated on the basis that its model memory will grow to themodel_memory_limit
specified in theanalysis_limits
of its config. -
current_model_bytes
: The job’s memory requirement is calculated on the basis that its current model memory size is a good reflection of what it will be in the future. -
peak_model_bytes
: The job’s memory requirement is calculated on the basis that its peak model memory size is a good reflection of what the model size will be in the future.
-
-
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. -
categorized_doc_count
- (long) The number of documents that have had a field categorized.
-
categorization_status
-
(string) The status of categorization for the job. Contains one of the following values:
-
ok
: Categorization is performing acceptably well (or not being used at all). -
warn
: Categorization is detecting a distribution of categories that suggests the input data is inappropriate for categorization. Problems could be that there is only one category, more than 90% of categories are rare, the number of categories is greater than 50% of the number of categorized documents, there are no frequently matched categories, or more than 50% of categories are dead.
-
-
dead_category_count
- (long) The number of categories created by categorization that will never be assigned again because another category’s definition makes it a superset of the dead category. (Dead categories are a side effect of the way categorization has no prior training.)
-
failed_category_count
-
(long)
The number of times that categorization wanted to create a new category but
couldn’t because the job had hit its
model_memory_limit
. This count does not track which specific categories failed to be created. Therefore you cannot use this value to determine the number of unique categories that were missed. -
frequent_category_count
- (long) The number of categories that match more than 1% of categorized documents.
-
job_id
- (string) Identifier for the anomaly detection job.
-
log_time
-
(date) The timestamp of the
model_size_stats
according to server time. -
memory_status
-
(string) The status of the mathematical models, which 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. Additionally, in categorization jobs no further category examples will be stored. -
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 model memory usage, checked on increasing values.
-
peak_model_bytes
- (long) The peak number of bytes of memory ever used by the models.
-
rare_category_count
- (long) The number of categories that match just one categorized document.
-
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. This value is cumulative for all detectors in the job. -
total_category_count
- (long) The number of categories created by categorization.
-
total_over_field_count
-
(long)
The number of
over
field values that were analyzed by the models. This value is cumulative for all detectors in the job. -
total_partition_field_count
-
(long)
The number of
partition
field values that were analyzed by the models. This value is cumulative for all detectors in the job. -
timestamp
- (date) The timestamp of the last record when the model stats were gathered.
-
-
node
-
(object) Contains properties for the node that runs the job. This information is available only for open jobs.
Properties of
node
-
attributes
-
(object)
Lists node attributes such as
ml.machine_memory
orml.max_open_jobs
settings. -
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.
-
state
-
(string) The status of the anomaly detection 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 datafeed 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.
-
-
timing_stats
-
(object) An object that provides statistical information about timing aspect of this job.
Properties of
timing_stats
-
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, in milliseconds.
-
job_id
- (string) Identifier for the anomaly detection job.
-
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.
-
Response codes
edit-
404
(Missing resources) -
If
allow_no_match
isfalse
, this code indicates that there are no resources that match the request or only partial matches for the request.
Examples
editresp = client.ml.get_job_stats( job_id="low_request_rate", ) print(resp)
response = client.ml.get_job_stats( job_id: 'low_request_rate' ) puts response
const response = await client.ml.getJobStats({ job_id: "low_request_rate", }); console.log(response);
GET _ml/anomaly_detectors/low_request_rate/_stats
The API returns the following results:
{ "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", "categorized_doc_count" : 0, "total_category_count" : 0, "frequent_category_count" : 0, "rare_category_count" : 0, "dead_category_count" : 0, "failed_category_count" : 0, "categorization_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" : "512" } }, "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 } } ] }