diff --git a/docs/reference/ml/anomaly-detection/apis/get-job-stats.asciidoc b/docs/reference/ml/anomaly-detection/apis/get-job-stats.asciidoc index 2978cf47f5e..7523c048906 100644 --- a/docs/reference/ml/anomaly-detection/apis/get-job-stats.asciidoc +++ b/docs/reference/ml/anomaly-detection/apis/get-job-stats.asciidoc @@ -53,10 +53,271 @@ 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: +The API returns the following information about the operational progress of a +job: -`jobs`:: - (array) An array of {anomaly-job} statistics objects. +`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. + +`data_counts`.`bucket_count`::: +(long) The number of bucket results produced by the job. + +`data_counts`.`earliest_record_timestamp`::: +(date) The timestamp of the earliest chronologically input document. + +`data_counts`.`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`. + +`data_counts`.`input_bytes`::: +(long) The number of raw bytes read by the job. + +`data_counts`.`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. + +`data_counts`.`input_record_count`::: +(long) The number of data records read by the job. + +`data_counts`.`invalid_date_count`::: +(long) The number of records with either a missing date field or a date that +could not be parsed. + +`data_counts`.`job_id`::: +(string) +include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection] + +`data_counts`.`last_data_time`::: +(date) The timestamp at which data was last analyzed, according to server time. + +`data_counts`.`latest_empty_bucket_timestamp`::: +(date) The timestamp of the last bucket that did not contain any data. + +`data_counts`.`latest_record_timestamp`::: +(date) The timestamp of the latest chronologically input document. + +`data_counts`.`latest_sparse_bucket_timestamp`::: +(date) The timestamp of the last bucket that was considered sparse. + +`data_counts`.`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. + +-- + +`data_counts`.`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 <>. These out of order +documents are discarded, since jobs require time series data to be in ascending +chronological order. + +`data_counts`.`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 time stamp is not included in this count. + +`data_counts`.`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. + +`data_counts`.`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: `memory_bytes`, `records`, `processing_time_ms` and `status` require at +least one forecast. Otherwise, these fields are omitted. + +-- + +`forecasts_stats`.`forecasted_jobs`::: +(long) The number of jobs that have at least one forecast. + +`forecasts_stats`.`memory_bytes`::: +(object) Statistics about the memory usage: minimum, maximum, average and total. + +`forecasts_stats`.`processing_time_ms`::: +(object) Statistics about the forecast runtime in milliseconds: minimum, maximum, average and total. + +`forecasts_stats`.`records`::: +(object) Statistics about the number of forecast records: minimum, maximum, +average and total. + +`forecasts_stats`.`status`::: +(object) Counts per forecast status, for example: `{"finished" : 2}`. + +`forecasts_stats`.`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: + +`model_size_stats`.`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. + +`model_size_stats`.`job_id`::: +(string) +include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection] + +`model_size_stats`.`log_time`::: +(date) The timestamp of the `model_size_stats` according to server time. + +`model_size_stats`.`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_size_stats`.`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_size_stats`.`model_bytes_exceeded`::: + (long) The number of bytes over the high limit for memory usage at the last + allocation failure. + +`model_size_stats`.`model_bytes_memory_limit`::: +(long) The upper limit for memory usage, checked on increasing values. + +`model_size_stats`.`result_type`::: +(string) For internal use. The type of result. + +`model_size_stats`.`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. + +-- + +`model_size_stats`.`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. + +-- + +`model_size_stats`.`total_partition_field_count`::: +(long) The number of `partition` field values that were analyzed by the models. + +`model_size_stats`.`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. + +`node`.`attributes`::: +(object) Lists node attributes. For example, +`{"ml.machine_memory": "17179869184"}`. + +`node`.`ephemeral_id`::: +(string) The ephemeral id of the node. + +`node`.`id`::: +(string) The unique identifier of the node. + +`node`.`name`::: +(string) The node name. + +`node`.`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. 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: + +`timing_stats`.`average_bucket_processing_time_ms`::: +(double) Average of all bucket processing times in milliseconds. + +`timing_stats`.`bucket_count`::: +(long) The number of buckets processed. + +`timing_stats`.`exponential_average_bucket_processing_time_ms`::: +(double) Exponential moving average of all bucket processing times in +milliseconds. + +`timing_stats`.`exponential_average_bucket_processing_time_per_hour_ms`::: +(double) Exponentially-weighted moving average of bucket processing times +calculated in a 1 hour time window. + +`timing_stats`.`job_id`::: +(string) +include::{docdir}/ml/ml-shared.asciidoc[tag=job-id-anomaly-detection] + +`timing_stats`.`maximum_bucket_processing_time_ms`::: +(double) Maximum among all bucket processing times in milliseconds. + +`timing_stats`.`minimum_bucket_processing_time_ms`::: +(double) Minimum among all bucket processing times in milliseconds. + +`timing_stats`.`total_bucket_processing_time_ms`::: +(double) Sum of all bucket processing times in milliseconds. [[ml-get-job-stats-response-codes]] ==== {api-response-codes-title} @@ -68,61 +329,102 @@ The API returns the following information: [[ml-get-job-stats-example]] ==== {api-examples-title} -The following example gets usage information for the `farequote` job: - [source,console] -------------------------------------------------- -GET _ml/anomaly_detectors/farequote/_stats +GET _ml/anomaly_detectors/low_request_rate/_stats -------------------------------------------------- -// TEST[skip:todo] +// TEST[skip:Kibana sample data] The API returns the following results: [source,js] ---- { - "count": 1, - "jobs": [ + "count" : 1, + "jobs" : [ { - "job_id": "farequote", - "data_counts": { - "job_id": "farequote", - "processed_record_count": 86275, - "processed_field_count": 172550, - "input_bytes": 6744714, - "input_field_count": 172550, - "invalid_date_count": 0, - "missing_field_count": 0, - "out_of_order_timestamp_count": 0, - "empty_bucket_count": 0, - "sparse_bucket_count": 15, - "bucket_count": 1528, - "earliest_record_timestamp": 1454803200000, - "latest_record_timestamp": 1455235196000, - "last_data_time": 1491948163685, - "latest_sparse_bucket_timestamp": 1455174900000, - "input_record_count": 86275 + "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": "farequote", - "result_type": "model_size_stats", - "model_bytes": 387594, - "total_by_field_count": 21, - "total_over_field_count": 0, - "total_partition_field_count": 20, - "bucket_allocation_failures_count": 0, - "memory_status": "ok", - "log_time": 1491948163000, - "timestamp": 1455234600000 + "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 }, - "state": "closed", - "timing_stats": { - "job_id": "farequote", - "minimum_bucket_processing_time_ms": 0.0, - "maximum_bucket_processing_time_ms": 15.0, - "average_bucket_processing_time_ms": 8.75, - "exponential_average_bucket_processing_time_ms": 6.1435899 + "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 } } ] } ----- +---- \ No newline at end of file diff --git a/docs/reference/ml/anomaly-detection/apis/jobcounts.asciidoc b/docs/reference/ml/anomaly-detection/apis/jobcounts.asciidoc deleted file mode 100644 index 9f205d6f614..00000000000 --- a/docs/reference/ml/anomaly-detection/apis/jobcounts.asciidoc +++ /dev/null @@ -1,260 +0,0 @@ -[role="xpack"] -[testenv="platinum"] -[[ml-jobstats]] -=== Job statistics - -The get job statistics API provides 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. - -`data_counts`:: - (object) An object that describes the number of records processed and - any related error counts. See <>. - -`job_id`:: - (string) A unique identifier for the job. - -`model_size_stats`:: - (object) An object that provides information about the size and contents of the model. - See <>. - -`forecasts_stats`:: - (object) An object that provides statistical information about forecasts - of this job. See <>. - -`timing_stats`:: - (object) An object that provides statistical information about timing aspect - of this job. See <>. - -`node`:: - (object) For open jobs only, contains information about the node where the - job runs. See <>. - -`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: - - `opened`::: The job is available to receive and process data. - `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. - 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. - `opening`::: The job open action is in progress and has not yet completed. - -[float] -[[ml-datacounts]] -==== Data Counts Objects - -The `data_counts` object describes the number of records processed -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 raw bytes read by the job. - -`input_field_count`:: - (long) The total number of record fields read by the job. This count includes - fields that are not used in the analysis. - -`input_record_count`:: - (long) The number of data records read by the job. - -`invalid_date_count`:: - (long) The number of records with either a missing date field or a date that could not be parsed. - -`job_id`:: - (string) A unique identifier for the 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. - -`missing_field_count`:: - (long) The number of records that are missing a field that the job is - configured to analyze. Records 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 records 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 <>. - These out of order records 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 records that have been processed - by the job. Only fields that are specified in the detector configuration - object contribute to this count. The time stamp is not included in this count. - -`processed_record_count`:: - (long) The number of records that have been processed by the job. - This value includes records 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 aggregated records - 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`. - -[float] -[[ml-modelsizestats]] -==== Model Size Stats Objects - -The `model_size_stats` object 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) A numerical character string that uniquely identifies the job. - -`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. - -`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. - -[float] -[[ml-forecastsstats]] -==== Forecasts Stats Objects - -The `forecasts_stats` object shows statistics about forecasts. It has the following properties: - -`total`:: - (long) The number of forecasts currently available for this model. - -`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, average 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}. - -NOTE: `memory_bytes`, `records`, `processing_time_ms` and `status` require at least 1 forecast, otherwise -these fields are omitted. - -[float] -[[ml-timingstats]] -==== Timing Stats Objects - -The `timing_stats` object shows timing-related statistics about the job's progress. It has the following properties: - -`job_id`:: - (string) A numerical character string that uniquely identifies the job. - -`bucket_count`:: - (long) The number of buckets processed. - -`minimum_bucket_processing_time_ms`:: - (double) Minimum among all bucket processing times in milliseconds. - -`maximum_bucket_processing_time_ms`:: - (double) Maximum among all bucket processing times in milliseconds. - -`average_bucket_processing_time_ms`:: - (double) Average of all bucket processing times in milliseconds. - -`exponential_average_bucket_processing_time_ms`:: - (double) Exponential moving average of all bucket processing times in milliseconds. - - -[float] -[[ml-stats-node]] -==== Node Objects - -The `node` objects contains properties for the node that runs the job. -This information is available only for open jobs. - -`id`:: - (string) The unique identifier of the node. - -`name`:: - (string) The node name. - -`ephemeral_id`:: - (string) The ephemeral id of the node. - -`transport_address`:: - (string) The host and port where transport HTTP connections are accepted. - -`attributes`:: - (object) For example, {"ml.machine_memory": "17179869184"}. diff --git a/docs/reference/ml/anomaly-detection/apis/post-data.asciidoc b/docs/reference/ml/anomaly-detection/apis/post-data.asciidoc index cfd3d4ca67f..d4fa82b217f 100644 --- a/docs/reference/ml/anomaly-detection/apis/post-data.asciidoc +++ b/docs/reference/ml/anomaly-detection/apis/post-data.asciidoc @@ -37,10 +37,10 @@ and upload each one separately in sequential time order. When running in real time, it is generally recommended that you perform many small uploads, rather than queueing data to upload larger files. -When uploading data, check the <> for progress. -The following records will not be processed: +When uploading data, check the job data counts for progress. +The following documents will not be processed: -* Records not in chronological order and outside the latency window +* Documents not in chronological order and outside the latency window * Records with an invalid timestamp //TBD link to Working with Out of Order timeseries concept doc @@ -109,4 +109,4 @@ the job. For example: } ---- -For more information about these properties, see <>. +For more information about these properties, see <>. diff --git a/docs/reference/redirects.asciidoc b/docs/reference/redirects.asciidoc index 613752c0856..c5431d30a09 100644 --- a/docs/reference/redirects.asciidoc +++ b/docs/reference/redirects.asciidoc @@ -475,4 +475,19 @@ See the details in <>, <>, [[ml-datafeed-delayed-data-check-config]] <>, [[ml-datafeed-counts]] -<>. \ No newline at end of file +<>. + +[role="exclude",id="ml-jobstats"] +=== Job statistics + +This +[[ml-datacounts]] +page +[[ml-modelsizestats]] +was +[[ml-forecastsstats]] +deleted. +[[ml-timingstats]] +See +[[ml-stats-node]] +the details in <>. diff --git a/docs/reference/rest-api/defs.asciidoc b/docs/reference/rest-api/defs.asciidoc index 39e98c27e42..5c3b5969bf9 100644 --- a/docs/reference/rest-api/defs.asciidoc +++ b/docs/reference/rest-api/defs.asciidoc @@ -6,13 +6,11 @@ These resource definitions are used in APIs related to {ml-features} and {security-features} and in {kib} advanced {ml} job configuration options. * <> -* <> * <> * <> * <> include::{es-repo-dir}/ml/df-analytics/apis/analysisobjects.asciidoc[] -include::{es-repo-dir}/ml/anomaly-detection/apis/jobcounts.asciidoc[] include::{es-repo-dir}/ml/anomaly-detection/apis/snapshotresource.asciidoc[] include::{xes-repo-dir}/rest-api/security/role-mapping-resources.asciidoc[] include::{es-repo-dir}/ml/anomaly-detection/apis/resultsresource.asciidoc[]