Create a behavioral analytics collection Deprecated Technical preview; Added in 8.8.0

PUT /_application/analytics/{name}

Path parameters

  • name string Required

    The name of the analytics collection to be created or updated.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • acknowledged boolean Required

      For a successful response, this value is always true. On failure, an exception is returned instead.

    • name string Required

      The name of the analytics collection created or updated

PUT /_application/analytics/{name}
PUT _application/analytics/my_analytics_collection
resp = client.search_application.put_behavioral_analytics(
    name="my_analytics_collection",
)
const response = await client.searchApplication.putBehavioralAnalytics({
  name: "my_analytics_collection",
});
response = client.search_application.put_behavioral_analytics(
  name: "my_analytics_collection"
)
$resp = $client->searchApplication()->putBehavioralAnalytics([
    "name" => "my_analytics_collection",
]);
curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_application/analytics/my_analytics_collection"





















Get a document count Generally available

GET /_cat/count/{index}

All methods and paths for this operation:

GET /_cat/count

GET /_cat/count/{index}

Get quick access to a document count for a data stream, an index, or an entire cluster. The document count only includes live documents, not deleted documents which have not yet been removed by the merge process.

IMPORTANT: CAT APIs are only intended for human consumption using the command line or Kibana console. They are not intended for use by applications. For application consumption, use the count API.

Required authorization

  • Index privileges: read

Path parameters

  • index string | array[string] Required

    A comma-separated list of data streams, indices, and aliases used to limit the request. It supports wildcards (*). To target all data streams and indices, omit this parameter or use * or _all.

Query parameters

  • h string | array[string]

    A comma-separated list of columns names to display. It supports simple wildcards.

    Supported values include:

    • epoch (or t, time): The Unix epoch time in seconds since 1970-01-01 00:00:00.
    • timestamp (or ts, hms, hhmmss): The current time in HH:MM:SS format.
    • count (or dc, docs.count, docsCount): The document count in the cluster or index.

    Values are epoch, t, time, timestamp, ts, hms, hhmmss, count, dc, docs.count, or docsCount.

  • s string | array[string]

    List of columns that determine how the table should be sorted. Sorting defaults to ascending and can be changed by setting :asc or :desc as a suffix to the column name.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • epoch number | string

      seconds since 1970-01-01 00:00:00

      One of:

      seconds since 1970-01-01 00:00:00

    • timestamp string

      time in HH:MM:SS

    • count string

      the document count

GET /_cat/count/{index}
GET /_cat/count/my-index-000001?v=true&format=json
resp = client.cat.count(
    index="my-index-000001",
    v=True,
    format="json",
)
const response = await client.cat.count({
  index: "my-index-000001",
  v: "true",
  format: "json",
});
response = client.cat.count(
  index: "my-index-000001",
  v: "true",
  format: "json"
)
$resp = $client->cat()->count([
    "index" => "my-index-000001",
    "v" => "true",
    "format" => "json",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/count/my-index-000001?v=true&format=json"
Response examples (200)
A successful response from `GET /_cat/count/my-index-000001?v=true&format=json`. It retrieves the document count for the `my-index-000001` data stream or index.
[
  {
    "epoch": "1475868259",
    "timestamp": "15:24:20",
    "count": "120"
  }
]
A successful response from `GET /_cat/count?v=true&format=json`. It retrieves the document count for all data streams and indices in the cluster.
[
  {
    "epoch": "1475868259",
    "timestamp": "15:24:20",
    "count": "121"
  }
]

Get field data cache information Generally available

GET /_cat/fielddata/{fields}

All methods and paths for this operation:

GET /_cat/fielddata

GET /_cat/fielddata/{fields}

Get the amount of heap memory currently used by the field data cache on every data node in the cluster.

IMPORTANT: cat APIs are only intended for human consumption using the command line or Kibana console. They are not intended for use by applications. For application consumption, use the nodes stats API.

Required authorization

  • Cluster privileges: monitor

Path parameters

  • fields string | array[string] Required

    Comma-separated list of fields used to limit returned information. To retrieve all fields, omit this parameter.

Query parameters

  • bytes string

    The unit used to display byte values.

    Values are b, kb, mb, gb, tb, or pb.

  • fields string | array[string]

    Comma-separated list of fields used to limit returned information.

  • h string | array[string]

    A comma-separated list of columns names to display. It supports simple wildcards.

    Supported values include:

    • id: The node ID.
    • host (or h): The host name of the node.
    • ip: The IP address of the node.
    • node (or n): The node name.
    • field (or f): The field name.
    • size (or s): The field data usage.

    Values are id, host, h, ip, node, n, field, f, size, or s.

  • s string | array[string]

    List of columns that determine how the table should be sorted. Sorting defaults to ascending and can be changed by setting :asc or :desc as a suffix to the column name.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • id string

      node id

    • host string

      host name

    • ip string

      ip address

    • node string

      node name

    • field string

      field name

    • size string

      field data usage

GET /_cat/fielddata/{fields}
GET /_cat/fielddata?v=true&fields=body&format=json
resp = client.cat.fielddata(
    v=True,
    fields="body",
    format="json",
)
const response = await client.cat.fielddata({
  v: "true",
  fields: "body",
  format: "json",
});
response = client.cat.fielddata(
  v: "true",
  fields: "body",
  format: "json"
)
$resp = $client->cat()->fielddata([
    "v" => "true",
    "fields" => "body",
    "format" => "json",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/fielddata?v=true&fields=body&format=json"
Response examples (200)
A successful response from `GET /_cat/fielddata?v=true&fields=body&format=json`. You can specify an individual field in the request body or URL path. This example retrieves heap memory size information for the `body` field.
[
  {
    "id": "Nqk-6inXQq-OxUfOUI8jNQ",
    "host": "127.0.0.1",
    "ip": "127.0.0.1",
    "node": "Nqk-6in",
    "field": "body",
    "size": "544b"
  }
]
A successful response from `GET /_cat/fielddata/body,soul?v=true&format=json`. You can specify a comma-separated list of fields in the request body or URL path. This example retrieves heap memory size information for the `body` and `soul` fields. To get information for all fields, run `GET /_cat/fielddata?v=true`.
[
  {
    "id": "Nqk-6inXQq-OxUfOUI8jNQ",
    "host": "1127.0.0.1",
    "ip": "127.0.0.1",
    "node": "Nqk-6in",
    "field": "body",
    "size": "544b"
  },
  {
    "id": "Nqk-6inXQq-OxUfOUI8jNQ",
    "host": "127.0.0.1",
    "ip": "127.0.0.1",
    "node": "Nqk-6in",
    "field": "soul",
    "size": "480b"
  }
]




















Get datafeeds Generally available; Added in 7.7.0

GET /_cat/ml/datafeeds/{datafeed_id}

All methods and paths for this operation:

GET /_cat/ml/datafeeds

GET /_cat/ml/datafeeds/{datafeed_id}

Get configuration and usage information about datafeeds. This API returns a maximum of 10,000 datafeeds. If the Elasticsearch security features are enabled, you must have monitor_ml, monitor, manage_ml, or manage cluster privileges to use this API.

IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get datafeed statistics API.

Required authorization

  • Cluster privileges: monitor_ml

Path parameters

  • datafeed_id string Required

    A numerical character string that uniquely identifies the datafeed.

Query parameters

  • allow_no_match boolean

    Specifies what to do when the request:

    • Contains wildcard expressions and there are no datafeeds that match.
    • Contains the _all string or no identifiers and there are no matches.
    • Contains wildcard expressions and there are only partial matches.

    If true, the API returns an empty datafeeds array when there are no matches and the subset of results when there are partial matches. If false, the API returns a 404 status code when there are no matches or only partial matches.

  • h string | array[string]

    Comma-separated list of column names to display.

    Supported values include:

    • ae (or assignment_explanation): For started datafeeds only, contains messages relating to the selection of a node.
    • bc (or buckets.count, bucketsCount): The number of buckets processed.
    • id: A numerical character string that uniquely identifies the datafeed.
    • na (or node.address, nodeAddress): For started datafeeds only, the network address of the node where the datafeed is started.
    • ne (or node.ephemeral_id, nodeEphemeralId): For started datafeeds only, the ephemeral ID of the node where the datafeed is started.
    • ni (or node.id, nodeId): For started datafeeds only, the unique identifier of the node where the datafeed is started.
    • nn (or node.name, nodeName): For started datafeeds only, the name of the node where the datafeed is started.
    • sba (or search.bucket_avg, searchBucketAvg): The average search time per bucket, in milliseconds.
    • sc (or search.count, searchCount): The number of searches run by the datafeed.
    • seah (or search.exp_avg_hour, searchExpAvgHour): The exponential average search time per hour, in milliseconds.
    • st (or search.time, searchTime): The total time the datafeed spent searching, in milliseconds.
    • s (or state): The status of the datafeed: starting, started, stopping, or stopped. If starting, the datafeed has been requested to start but has not yet started. If started, the datafeed is actively receiving data. If stopping, the datafeed has been requested to stop gracefully and is completing its final action. If stopped, the datafeed is stopped and will not receive data until it is re-started.
  • s string | array[string]

    Comma-separated list of column names or column aliases used to sort the response.

    Supported values include:

    • ae (or assignment_explanation): For started datafeeds only, contains messages relating to the selection of a node.
    • bc (or buckets.count, bucketsCount): The number of buckets processed.
    • id: A numerical character string that uniquely identifies the datafeed.
    • na (or node.address, nodeAddress): For started datafeeds only, the network address of the node where the datafeed is started.
    • ne (or node.ephemeral_id, nodeEphemeralId): For started datafeeds only, the ephemeral ID of the node where the datafeed is started.
    • ni (or node.id, nodeId): For started datafeeds only, the unique identifier of the node where the datafeed is started.
    • nn (or node.name, nodeName): For started datafeeds only, the name of the node where the datafeed is started.
    • sba (or search.bucket_avg, searchBucketAvg): The average search time per bucket, in milliseconds.
    • sc (or search.count, searchCount): The number of searches run by the datafeed.
    • seah (or search.exp_avg_hour, searchExpAvgHour): The exponential average search time per hour, in milliseconds.
    • st (or search.time, searchTime): The total time the datafeed spent searching, in milliseconds.
    • s (or state): The status of the datafeed: starting, started, stopping, or stopped. If starting, the datafeed has been requested to start but has not yet started. If started, the datafeed is actively receiving data. If stopping, the datafeed has been requested to stop gracefully and is completing its final action. If stopped, the datafeed is stopped and will not receive data until it is re-started.
  • time string

    The unit used to display time values.

    Values are nanos, micros, ms, s, m, h, or d.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • id string

      The datafeed identifier.

    • state string

      The status of the datafeed.

      Values are started, stopped, starting, or stopping.

    • assignment_explanation string

      For started datafeeds only, contains messages relating to the selection of a node.

    • buckets.count string

      The number of buckets processed.

    • search.count string

      The number of searches run by the datafeed.

    • search.time string

      The total time the datafeed spent searching, in milliseconds.

    • search.bucket_avg string

      The average search time per bucket, in milliseconds.

    • search.exp_avg_hour string

      The exponential average search time per hour, in milliseconds.

    • node.id string

      The unique identifier of the assigned node. For started datafeeds only, this information pertains to the node upon which the datafeed is started.

    • node.name string

      The name of the assigned node. For started datafeeds only, this information pertains to the node upon which the datafeed is started.

    • node.ephemeral_id string

      The ephemeral identifier of the assigned node. For started datafeeds only, this information pertains to the node upon which the datafeed is started.

    • node.address string

      The network address of the assigned node. For started datafeeds only, this information pertains to the node upon which the datafeed is started.

GET /_cat/ml/datafeeds/{datafeed_id}
GET _cat/ml/datafeeds?v=true&format=json
resp = client.cat.ml_datafeeds(
    v=True,
    format="json",
)
const response = await client.cat.mlDatafeeds({
  v: "true",
  format: "json",
});
response = client.cat.ml_datafeeds(
  v: "true",
  format: "json"
)
$resp = $client->cat()->mlDatafeeds([
    "v" => "true",
    "format" => "json",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/ml/datafeeds?v=true&format=json"
Response examples (200)
A successful response from `GET _cat/ml/datafeeds?v=true&format=json`.
[
  {
    "id": "datafeed-high_sum_total_sales",
    "state": "stopped",
    "buckets.count": "743",
    "search.count": "7"
  },
  {
    "id": "datafeed-low_request_rate",
    "state": "stopped",
    "buckets.count": "1457",
    "search.count": "3"
  },
  {
    "id": "datafeed-response_code_rates",
    "state": "stopped",
    "buckets.count": "1460",
    "search.count": "18"
  },
  {
    "id": "datafeed-url_scanning",
    "state": "stopped",
    "buckets.count": "1460",
    "search.count": "18"
  }
]

Get anomaly detection jobs Generally available; Added in 7.7.0

GET /_cat/ml/anomaly_detectors/{job_id}

All methods and paths for this operation:

GET /_cat/ml/anomaly_detectors

GET /_cat/ml/anomaly_detectors/{job_id}

Get configuration and usage information for anomaly detection jobs. This API returns a maximum of 10,000 jobs. If the Elasticsearch security features are enabled, you must have monitor_ml, monitor, manage_ml, or manage cluster privileges to use this API.

IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get anomaly detection job statistics API.

Required authorization

  • Cluster privileges: monitor_ml

Path parameters

  • job_id string Required

    Identifier for the anomaly detection job.

Query parameters

  • allow_no_match 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.

    If true, the API returns an empty jobs array when there are no matches and the subset of results when there are partial matches. If false, the API returns a 404 status code when there are no matches or only partial matches.

  • bytes string

    The unit used to display byte values.

    Values are b, kb, mb, gb, tb, or pb.

  • h string | array[string]

    Comma-separated list of column names to display.

    Supported values include:

    • assignment_explanation (or ae): For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.
    • buckets.count (or bc, bucketsCount): The number of bucket results produced by the job.
    • buckets.time.exp_avg (or btea, bucketsTimeExpAvg): Exponential moving average of all bucket processing times, in milliseconds.
    • buckets.time.exp_avg_hour (or bteah, bucketsTimeExpAvgHour): Exponentially-weighted moving average of bucket processing times calculated in a 1 hour time window, in milliseconds.
    • buckets.time.max (or btmax, bucketsTimeMax): Maximum among all bucket processing times, in milliseconds.
    • buckets.time.min (or btmin, bucketsTimeMin): Minimum among all bucket processing times, in milliseconds.
    • buckets.time.total (or btt, bucketsTimeTotal): Sum of all bucket processing times, in milliseconds.
    • data.buckets (or db, dataBuckets): The number of buckets processed.
    • data.earliest_record (or der, dataEarliestRecord): The timestamp of the earliest chronologically input document.
    • data.empty_buckets (or deb, dataEmptyBuckets): The number of buckets which did not contain any data.
    • data.input_bytes (or dib, dataInputBytes): The number of bytes of input data posted to the anomaly detection job.
    • data.input_fields (or dif, dataInputFields): 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.
    • data.input_records (or dir, dataInputRecords): The number of input documents posted to the anomaly detection job.
    • data.invalid_dates (or did, dataInvalidDates): The number of input documents with either a missing date field or a date that could not be parsed.
    • data.last (or dl, dataLast): The timestamp at which data was last analyzed, according to server time.
    • data.last_empty_bucket (or dleb, dataLastEmptyBucket): The timestamp of the last bucket that did not contain any data.
    • data.last_sparse_bucket (or dlsb, dataLastSparseBucket): The timestamp of the last bucket that was considered sparse.
    • data.latest_record (or dlr, dataLatestRecord): The timestamp of the latest chronologically input document.
    • data.missing_fields (or dmf, dataMissingFields): 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.
    • data.out_of_order_timestamps (or doot, dataOutOfOrderTimestamps): 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.
    • data.processed_fields (or dpf, dataProcessedFields): 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.
    • data.processed_records (or dpr, dataProcessedRecords): 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.
    • data.sparse_buckets (or dsb, dataSparseBuckets): The number of buckets that contained few data points compared to the expected number of data points.
    • forecasts.memory.avg (or fmavg, forecastsMemoryAvg): The average memory usage in bytes for forecasts related to the anomaly detection job.
    • forecasts.memory.max (or fmmax, forecastsMemoryMax): The maximum memory usage in bytes for forecasts related to the anomaly detection job.
    • forecasts.memory.min (or fmmin, forecastsMemoryMin): The minimum memory usage in bytes for forecasts related to the anomaly detection job.
    • forecasts.memory.total (or fmt, forecastsMemoryTotal): The total memory usage in bytes for forecasts related to the anomaly detection job.
    • forecasts.records.avg (or fravg, forecastsRecordsAvg): The average number of model_forecast` documents written for forecasts related to the anomaly detection job.
    • forecasts.records.max (or frmax, forecastsRecordsMax): The maximum number of model_forecast documents written for forecasts related to the anomaly detection job.
    • forecasts.records.min (or frmin, forecastsRecordsMin): The minimum number of model_forecast documents written for forecasts related to the anomaly detection job.
    • forecasts.records.total (or frt, forecastsRecordsTotal): The total number of model_forecast documents written for forecasts related to the anomaly detection job.
    • forecasts.time.avg (or ftavg, forecastsTimeAvg): The average runtime in milliseconds for forecasts related to the anomaly detection job.
    • forecasts.time.max (or ftmax, forecastsTimeMax): The maximum runtime in milliseconds for forecasts related to the anomaly detection job.
    • forecasts.time.min (or ftmin, forecastsTimeMin): The minimum runtime in milliseconds for forecasts related to the anomaly detection job.
    • forecasts.time.total (or ftt, forecastsTimeTotal): The total runtime in milliseconds for forecasts related to the anomaly detection job.
    • forecasts.total (or ft, forecastsTotal): The number of individual forecasts currently available for the job.
    • id: Identifier for the anomaly detection job.
    • model.bucket_allocation_failures (or mbaf, modelBucketAllocationFailures): The number of buckets for which new entities in incoming data were not processed due to insufficient model memory.
    • model.by_fields (or mbf, modelByFields): The number of by field values that were analyzed by the models. This value is cumulative for all detectors in the job.
    • model.bytes (or mb, modelBytes): 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 (or mbe, modelBytesExceeded): The number of bytes over the high limit for memory usage at the last allocation failure.
    • model.categorization_status (or mcs, modelCategorizationStatus): The status of categorization for the job: ok or warn. If ok, categorization is performing acceptably well (or not being used at all). If 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.
    • model.categorized_doc_count (or mcdc, modelCategorizedDocCount): The number of documents that have had a field categorized.
    • model.dead_category_count (or mdcc, modelDeadCategoryCount): 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.
    • model.failed_category_count (or mdcc, modelFailedCategoryCount): 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.
    • model.frequent_category_count (or mfcc, modelFrequentCategoryCount): The number of categories that match more than 1% of categorized documents.
    • model.log_time (or mlt, modelLogTime): The timestamp when the model stats were gathered, according to server time.
    • model.memory_limit (or mml, modelMemoryLimit): The timestamp when the model stats were gathered, according to server time.
    • model.memory_status (or mms, modelMemoryStatus): The status of the mathematical models: ok, soft_limit, or hard_limit. If ok, the models stayed below the configured value. If 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. If hard_limit, the models used more space than the configured memory limit. As a result, not all incoming data was processed.
    • model.over_fields (or mof, modelOverFields): The number of over field values that were analyzed by the models. This value is cumulative for all detectors in the job.
    • model.partition_fields (or mpf, modelPartitionFields): The number of partition field values that were analyzed by the models. This value is cumulative for all detectors in the job.
    • model.rare_category_count (or mrcc, modelRareCategoryCount): The number of categories that match just one categorized document.
    • model.timestamp (or mt, modelTimestamp): The timestamp of the last record when the model stats were gathered.
    • model.total_category_count (or mtcc, modelTotalCategoryCount): The number of categories created by categorization.
    • node.address (or na, nodeAddress): The network address of the node that runs the job. This information is available only for open jobs.
    • node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that runs the job. This information is available only for open jobs.
    • node.id (or ni, nodeId): The unique identifier of the node that runs the job. This information is available only for open jobs.
    • node.name (or nn, nodeName): The name of the node that runs the job. This information is available only for open jobs.
    • opened_time (or ot): For open jobs only, the elapsed time for which the job has been open.
    • state (or s): The status of the anomaly detection job: closed, closing, failed, opened, or opening. If closed, the job finished successfully with its model state persisted. The job must be opened before it can accept further data. If closing, the job close action is in progress and has not yet completed. A closing job cannot accept further data. If 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. If opened, the job is available to receive and process data. If opening, the job open action is in progress and has not yet completed.
  • s string | array[string]

    Comma-separated list of column names or column aliases used to sort the response.

    Supported values include:

    • assignment_explanation (or ae): For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.
    • buckets.count (or bc, bucketsCount): The number of bucket results produced by the job.
    • buckets.time.exp_avg (or btea, bucketsTimeExpAvg): Exponential moving average of all bucket processing times, in milliseconds.
    • buckets.time.exp_avg_hour (or bteah, bucketsTimeExpAvgHour): Exponentially-weighted moving average of bucket processing times calculated in a 1 hour time window, in milliseconds.
    • buckets.time.max (or btmax, bucketsTimeMax): Maximum among all bucket processing times, in milliseconds.
    • buckets.time.min (or btmin, bucketsTimeMin): Minimum among all bucket processing times, in milliseconds.
    • buckets.time.total (or btt, bucketsTimeTotal): Sum of all bucket processing times, in milliseconds.
    • data.buckets (or db, dataBuckets): The number of buckets processed.
    • data.earliest_record (or der, dataEarliestRecord): The timestamp of the earliest chronologically input document.
    • data.empty_buckets (or deb, dataEmptyBuckets): The number of buckets which did not contain any data.
    • data.input_bytes (or dib, dataInputBytes): The number of bytes of input data posted to the anomaly detection job.
    • data.input_fields (or dif, dataInputFields): 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.
    • data.input_records (or dir, dataInputRecords): The number of input documents posted to the anomaly detection job.
    • data.invalid_dates (or did, dataInvalidDates): The number of input documents with either a missing date field or a date that could not be parsed.
    • data.last (or dl, dataLast): The timestamp at which data was last analyzed, according to server time.
    • data.last_empty_bucket (or dleb, dataLastEmptyBucket): The timestamp of the last bucket that did not contain any data.
    • data.last_sparse_bucket (or dlsb, dataLastSparseBucket): The timestamp of the last bucket that was considered sparse.
    • data.latest_record (or dlr, dataLatestRecord): The timestamp of the latest chronologically input document.
    • data.missing_fields (or dmf, dataMissingFields): 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.
    • data.out_of_order_timestamps (or doot, dataOutOfOrderTimestamps): 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.
    • data.processed_fields (or dpf, dataProcessedFields): 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.
    • data.processed_records (or dpr, dataProcessedRecords): 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.
    • data.sparse_buckets (or dsb, dataSparseBuckets): The number of buckets that contained few data points compared to the expected number of data points.
    • forecasts.memory.avg (or fmavg, forecastsMemoryAvg): The average memory usage in bytes for forecasts related to the anomaly detection job.
    • forecasts.memory.max (or fmmax, forecastsMemoryMax): The maximum memory usage in bytes for forecasts related to the anomaly detection job.
    • forecasts.memory.min (or fmmin, forecastsMemoryMin): The minimum memory usage in bytes for forecasts related to the anomaly detection job.
    • forecasts.memory.total (or fmt, forecastsMemoryTotal): The total memory usage in bytes for forecasts related to the anomaly detection job.
    • forecasts.records.avg (or fravg, forecastsRecordsAvg): The average number of model_forecast` documents written for forecasts related to the anomaly detection job.
    • forecasts.records.max (or frmax, forecastsRecordsMax): The maximum number of model_forecast documents written for forecasts related to the anomaly detection job.
    • forecasts.records.min (or frmin, forecastsRecordsMin): The minimum number of model_forecast documents written for forecasts related to the anomaly detection job.
    • forecasts.records.total (or frt, forecastsRecordsTotal): The total number of model_forecast documents written for forecasts related to the anomaly detection job.
    • forecasts.time.avg (or ftavg, forecastsTimeAvg): The average runtime in milliseconds for forecasts related to the anomaly detection job.
    • forecasts.time.max (or ftmax, forecastsTimeMax): The maximum runtime in milliseconds for forecasts related to the anomaly detection job.
    • forecasts.time.min (or ftmin, forecastsTimeMin): The minimum runtime in milliseconds for forecasts related to the anomaly detection job.
    • forecasts.time.total (or ftt, forecastsTimeTotal): The total runtime in milliseconds for forecasts related to the anomaly detection job.
    • forecasts.total (or ft, forecastsTotal): The number of individual forecasts currently available for the job.
    • id: Identifier for the anomaly detection job.
    • model.bucket_allocation_failures (or mbaf, modelBucketAllocationFailures): The number of buckets for which new entities in incoming data were not processed due to insufficient model memory.
    • model.by_fields (or mbf, modelByFields): The number of by field values that were analyzed by the models. This value is cumulative for all detectors in the job.
    • model.bytes (or mb, modelBytes): 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 (or mbe, modelBytesExceeded): The number of bytes over the high limit for memory usage at the last allocation failure.
    • model.categorization_status (or mcs, modelCategorizationStatus): The status of categorization for the job: ok or warn. If ok, categorization is performing acceptably well (or not being used at all). If 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.
    • model.categorized_doc_count (or mcdc, modelCategorizedDocCount): The number of documents that have had a field categorized.
    • model.dead_category_count (or mdcc, modelDeadCategoryCount): 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.
    • model.failed_category_count (or mdcc, modelFailedCategoryCount): 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.
    • model.frequent_category_count (or mfcc, modelFrequentCategoryCount): The number of categories that match more than 1% of categorized documents.
    • model.log_time (or mlt, modelLogTime): The timestamp when the model stats were gathered, according to server time.
    • model.memory_limit (or mml, modelMemoryLimit): The timestamp when the model stats were gathered, according to server time.
    • model.memory_status (or mms, modelMemoryStatus): The status of the mathematical models: ok, soft_limit, or hard_limit. If ok, the models stayed below the configured value. If 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. If hard_limit, the models used more space than the configured memory limit. As a result, not all incoming data was processed.
    • model.over_fields (or mof, modelOverFields): The number of over field values that were analyzed by the models. This value is cumulative for all detectors in the job.
    • model.partition_fields (or mpf, modelPartitionFields): The number of partition field values that were analyzed by the models. This value is cumulative for all detectors in the job.
    • model.rare_category_count (or mrcc, modelRareCategoryCount): The number of categories that match just one categorized document.
    • model.timestamp (or mt, modelTimestamp): The timestamp of the last record when the model stats were gathered.
    • model.total_category_count (or mtcc, modelTotalCategoryCount): The number of categories created by categorization.
    • node.address (or na, nodeAddress): The network address of the node that runs the job. This information is available only for open jobs.
    • node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that runs the job. This information is available only for open jobs.
    • node.id (or ni, nodeId): The unique identifier of the node that runs the job. This information is available only for open jobs.
    • node.name (or nn, nodeName): The name of the node that runs the job. This information is available only for open jobs.
    • opened_time (or ot): For open jobs only, the elapsed time for which the job has been open.
    • state (or s): The status of the anomaly detection job: closed, closing, failed, opened, or opening. If closed, the job finished successfully with its model state persisted. The job must be opened before it can accept further data. If closing, the job close action is in progress and has not yet completed. A closing job cannot accept further data. If 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. If opened, the job is available to receive and process data. If opening, the job open action is in progress and has not yet completed.
  • time string

    The unit used to display time values.

    Values are nanos, micros, ms, s, m, h, or d.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • id string

      The anomaly detection job identifier.

    • state string

      The status of the anomaly detection job.

      Supported values include:

      • closing: The job close action is in progress and has not yet completed. A closing job cannot accept further data.
      • closed: The job finished successfully with its model state persisted. The job must be opened before it can accept further data.
      • opened: The job is available to receive and process 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.
      • opening: The job open action is in progress and has not yet completed.

      Values are closing, closed, opened, failed, or opening.

    • opened_time string

      For open jobs only, the amount of time the job has been opened.

    • assignment_explanation string

      For open anomaly detection jobs only, contains messages relating to the selection of a node to run the job.

    • data.processed_records string

      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.

    • data.processed_fields string

      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.

    • data.input_bytes number | string

      The number of bytes of input data posted to the anomaly detection job.

      One of:

      The number of bytes of input data posted to the anomaly detection job.

    • data.input_records string

      The number of input documents posted to the anomaly detection job.

    • data.input_fields string

      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.

    • data.invalid_dates string

      The number of input documents with either a missing date field or a date that could not be parsed.

    • data.missing_fields string

      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.

    • data.out_of_order_timestamps string

      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.

    • data.empty_buckets string

      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.sparse_buckets string

      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.

    • data.buckets string

      The total number of buckets processed.

    • data.earliest_record string

      The timestamp of the earliest chronologically input document.

    • data.latest_record string

      The timestamp of the latest chronologically input document.

    • data.last string

      The timestamp at which data was last analyzed, according to server time.

    • data.last_empty_bucket string

      The timestamp of the last bucket that did not contain any data.

    • data.last_sparse_bucket string

      The timestamp of the last bucket that was considered sparse.

    • model.bytes number | string

      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.

      One of:

      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.memory_status string

      The status of the mathematical models.

      Values are ok, soft_limit, or hard_limit.

    • model.bytes_exceeded number | string

      The number of bytes over the high limit for memory usage at the last allocation failure.

      One of:

      The number of bytes over the high limit for memory usage at the last allocation failure.

    • model.memory_limit string

      The upper limit for model memory usage, checked on increasing values.

    • model.by_fields string

      The number of by field values that were analyzed by the models. This value is cumulative for all detectors in the job.

    • model.over_fields string

      The number of over field values that were analyzed by the models. This value is cumulative for all detectors in the job.

    • model.partition_fields string

      The number of partition field values that were analyzed by the models. This value is cumulative for all detectors in the job.

    • model.bucket_allocation_failures string

      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.categorization_status string

      The status of categorization for the job.

      Values are ok or warn.

    • model.categorized_doc_count string

      The number of documents that have had a field categorized.

    • model.total_category_count string

      The number of categories created by categorization.

    • model.frequent_category_count string

      The number of categories that match more than 1% of categorized documents.

    • model.rare_category_count string

      The number of categories that match just one categorized document.

    • model.dead_category_count string

      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.

    • model.failed_category_count string

      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.

    • model.log_time string

      The timestamp when the model stats were gathered, according to server time.

    • model.timestamp string

      The timestamp of the last record when the model stats were gathered.

    • forecasts.total string

      The number of individual forecasts currently available for the job. A value of one or more indicates that forecasts exist.

    • forecasts.memory.min string

      The minimum memory usage in bytes for forecasts related to the anomaly detection job.

    • forecasts.memory.max string

      The maximum memory usage in bytes for forecasts related to the anomaly detection job.

    • forecasts.memory.avg string

      The average memory usage in bytes for forecasts related to the anomaly detection job.

    • forecasts.memory.total string

      The total memory usage in bytes for forecasts related to the anomaly detection job.

    • forecasts.records.min string

      The minimum number of model_forecast documents written for forecasts related to the anomaly detection job.

    • forecasts.records.max string

      The maximum number of model_forecast documents written for forecasts related to the anomaly detection job.

    • forecasts.records.avg string

      The average number of model_forecast documents written for forecasts related to the anomaly detection job.

    • forecasts.records.total string

      The total number of model_forecast documents written for forecasts related to the anomaly detection job.

    • forecasts.time.min string

      The minimum runtime in milliseconds for forecasts related to the anomaly detection job.

    • forecasts.time.max string

      The maximum runtime in milliseconds for forecasts related to the anomaly detection job.

    • forecasts.time.avg string

      The average runtime in milliseconds for forecasts related to the anomaly detection job.

    • forecasts.time.total string

      The total runtime in milliseconds for forecasts related to the anomaly detection job.

    • node.id string

      The uniqe identifier of the assigned node.

    • node.name string

      The name of the assigned node.

    • node.ephemeral_id string

      The ephemeral identifier of the assigned node.

    • node.address string

      The network address of the assigned node.

    • buckets.count string

      The number of bucket results produced by the job.

    • buckets.time.total string

      The sum of all bucket processing times, in milliseconds.

    • buckets.time.min string

      The minimum of all bucket processing times, in milliseconds.

    • buckets.time.max string

      The maximum of all bucket processing times, in milliseconds.

    • buckets.time.exp_avg string

      The exponential moving average of all bucket processing times, in milliseconds.

    • buckets.time.exp_avg_hour string

      The exponential moving average of bucket processing times calculated in a one hour time window, in milliseconds.

GET /_cat/ml/anomaly_detectors/{job_id}
GET _cat/ml/anomaly_detectors?h=id,s,dpr,mb&v=true&format=json
resp = client.cat.ml_jobs(
    h="id,s,dpr,mb",
    v=True,
    format="json",
)
const response = await client.cat.mlJobs({
  h: "id,s,dpr,mb",
  v: "true",
  format: "json",
});
response = client.cat.ml_jobs(
  h: "id,s,dpr,mb",
  v: "true",
  format: "json"
)
$resp = $client->cat()->mlJobs([
    "h" => "id,s,dpr,mb",
    "v" => "true",
    "format" => "json",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/ml/anomaly_detectors?h=id,s,dpr,mb&v=true&format=json"
Response examples (200)
A successful response from `GET _cat/ml/anomaly_detectors?h=id,s,dpr,mb&v=true&format=json`.
[
  {
    "id": "high_sum_total_sales",
    "s": "closed",
    "dpr": "14022",
    "mb": "1.5mb"
  },
  {
    "id": "low_request_rate",
    "s": "closed",
    "dpr": "1216",
    "mb": "40.5kb"
  },
  {
    "id": "response_code_rates",
    "s": "closed",
    "dpr": "28146",
    "mb": "132.7kb"
  },
  {
    "id": "url_scanning",
    "s": "closed",
    "dpr": "28146",
    "mb": "501.6kb"
  }
]




















































Get transform information Generally available; Added in 7.7.0

GET /_cat/transforms/{transform_id}

All methods and paths for this operation:

GET /_cat/transforms

GET /_cat/transforms/{transform_id}

Get configuration and usage information about transforms.

CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get transform statistics API.

Required authorization

  • Cluster privileges: monitor_transform

Path parameters

  • transform_id string Required

    A transform identifier or a wildcard expression. If you do not specify one of these options, the API returns information for all transforms.

Query parameters

  • allow_no_match boolean

    Specifies what to do when the request: contains wildcard expressions and there are no transforms that match; contains the _all string or no identifiers and there are no matches; contains wildcard expressions and there are only partial matches. If true, it returns an empty transforms array when there are no matches and the subset of results when there are partial matches. If false, the request returns a 404 status code when there are no matches or only partial matches.

  • from number

    Skips the specified number of transforms.

  • h string | array[string]

    Comma-separated list of column names to display.

    Supported values include:

    • changes_last_detection_time (or cldt): The timestamp when changes were last detected in the source indices.
    • checkpoint (or cp): The sequence number for the checkpoint.
    • checkpoint_duration_time_exp_avg (or cdtea, checkpointTimeExpAvg): Exponential moving average of the duration of the checkpoint, in milliseconds.
    • checkpoint_progress (or c, checkpointProgress): The progress of the next checkpoint that is currently in progress.
    • create_time (or ct, createTime): The time the transform was created.
    • delete_time (or dtime): The amount of time spent deleting, in milliseconds.
    • description (or d): The description of the transform.
    • dest_index (or di, destIndex): The destination index for the transform. The mappings of the destination index are deduced based on the source fields when possible. If alternate mappings are required, use the Create index API prior to starting the transform.
    • documents_deleted (or docd): The number of documents that have been deleted from the destination index due to the retention policy for this transform.
    • documents_indexed (or doci): The number of documents that have been indexed into the destination index for the transform.
    • docs_per_second (or dps): Specifies a limit on the number of input documents per second. This setting throttles the transform by adding a wait time between search requests. The default value is null, which disables throttling.
    • documents_processed (or docp): The number of documents that have been processed from the source index of the transform.
    • frequency (or f): The interval between checks for changes in the source indices when the transform is running continuously. Also determines the retry interval in the event of transient failures while the transform is searching or indexing. The minimum value is 1s and the maximum is 1h. The default value is 1m.
    • id: Identifier for the transform.
    • index_failure (or if): The number of indexing failures.
    • index_time (or itime): The amount of time spent indexing, in milliseconds.
    • index_total (or it): The number of index operations.
    • indexed_documents_exp_avg (or idea): Exponential moving average of the number of new documents that have been indexed.
    • last_search_time (or lst, lastSearchTime): The timestamp of the last search in the source indices. This field is only shown if the transform is running.
    • max_page_search_size (or mpsz): Defines the initial page size to use for the composite aggregation for each checkpoint. If circuit breaker exceptions occur, the page size is dynamically adjusted to a lower value. The minimum value is 10 and the maximum is 65,536. The default value is 500.
    • pages_processed (or pp): The number of search or bulk index operations processed. Documents are processed in batches instead of individually.
    • pipeline (or p): The unique identifier for an ingest pipeline.
    • processed_documents_exp_avg (or pdea): Exponential moving average of the number of documents that have been processed.
    • processing_time (or pt): The amount of time spent processing results, in milliseconds.
    • reason (or r): If a transform has a failed state, this property provides details about the reason for the failure.
    • search_failure (or sf): The number of search failures.
    • search_time (or stime): The amount of time spent searching, in milliseconds.
    • search_total (or st): The number of search operations on the source index for the transform.
    • source_index (or si, sourceIndex): The source indices for the transform. It can be a single index, an index pattern (for example, "my-index-*"), an array of indices (for example, ["my-index-000001", "my-index-000002"]), or an array of index patterns (for example, ["my-index-*", "my-other-index-*"]. For remote indices use the syntax "remote_name:index_name". If any indices are in remote clusters then the master node and at least one transform node must have the remote_cluster_client node role.
    • state (or s): The status of the transform, which can be one of the following values:

      • aborting: The transform is aborting.
      • failed: The transform failed. For more information about the failure, check the reason field.
      • indexing: The transform is actively processing data and creating new documents.
      • started: The transform is running but not actively indexing data.
      • stopped: The transform is stopped.
      • stopping: The transform is stopping.
    • transform_type (or tt): Indicates the type of transform: batch or continuous.

    • trigger_count (or tc): The number of times the transform has been triggered by the scheduler. For example, the scheduler triggers the transform indexer to check for updates or ingest new data at an interval specified in the frequency property.

    • version (or v): The version of Elasticsearch that existed on the node when the transform was created.

  • s string | array[string]

    Comma-separated list of column names or column aliases used to sort the response.

    Supported values include:

    • changes_last_detection_time (or cldt): The timestamp when changes were last detected in the source indices.
    • checkpoint (or cp): The sequence number for the checkpoint.
    • checkpoint_duration_time_exp_avg (or cdtea, checkpointTimeExpAvg): Exponential moving average of the duration of the checkpoint, in milliseconds.
    • checkpoint_progress (or c, checkpointProgress): The progress of the next checkpoint that is currently in progress.
    • create_time (or ct, createTime): The time the transform was created.
    • delete_time (or dtime): The amount of time spent deleting, in milliseconds.
    • description (or d): The description of the transform.
    • dest_index (or di, destIndex): The destination index for the transform. The mappings of the destination index are deduced based on the source fields when possible. If alternate mappings are required, use the Create index API prior to starting the transform.
    • documents_deleted (or docd): The number of documents that have been deleted from the destination index due to the retention policy for this transform.
    • documents_indexed (or doci): The number of documents that have been indexed into the destination index for the transform.
    • docs_per_second (or dps): Specifies a limit on the number of input documents per second. This setting throttles the transform by adding a wait time between search requests. The default value is null, which disables throttling.
    • documents_processed (or docp): The number of documents that have been processed from the source index of the transform.
    • frequency (or f): The interval between checks for changes in the source indices when the transform is running continuously. Also determines the retry interval in the event of transient failures while the transform is searching or indexing. The minimum value is 1s and the maximum is 1h. The default value is 1m.
    • id: Identifier for the transform.
    • index_failure (or if): The number of indexing failures.
    • index_time (or itime): The amount of time spent indexing, in milliseconds.
    • index_total (or it): The number of index operations.
    • indexed_documents_exp_avg (or idea): Exponential moving average of the number of new documents that have been indexed.
    • last_search_time (or lst, lastSearchTime): The timestamp of the last search in the source indices. This field is only shown if the transform is running.
    • max_page_search_size (or mpsz): Defines the initial page size to use for the composite aggregation for each checkpoint. If circuit breaker exceptions occur, the page size is dynamically adjusted to a lower value. The minimum value is 10 and the maximum is 65,536. The default value is 500.
    • pages_processed (or pp): The number of search or bulk index operations processed. Documents are processed in batches instead of individually.
    • pipeline (or p): The unique identifier for an ingest pipeline.
    • processed_documents_exp_avg (or pdea): Exponential moving average of the number of documents that have been processed.
    • processing_time (or pt): The amount of time spent processing results, in milliseconds.
    • reason (or r): If a transform has a failed state, this property provides details about the reason for the failure.
    • search_failure (or sf): The number of search failures.
    • search_time (or stime): The amount of time spent searching, in milliseconds.
    • search_total (or st): The number of search operations on the source index for the transform.
    • source_index (or si, sourceIndex): The source indices for the transform. It can be a single index, an index pattern (for example, "my-index-*"), an array of indices (for example, ["my-index-000001", "my-index-000002"]), or an array of index patterns (for example, ["my-index-*", "my-other-index-*"]. For remote indices use the syntax "remote_name:index_name". If any indices are in remote clusters then the master node and at least one transform node must have the remote_cluster_client node role.
    • state (or s): The status of the transform, which can be one of the following values:

      • aborting: The transform is aborting.
      • failed: The transform failed. For more information about the failure, check the reason field.
      • indexing: The transform is actively processing data and creating new documents.
      • started: The transform is running but not actively indexing data.
      • stopped: The transform is stopped.
      • stopping: The transform is stopping.
    • transform_type (or tt): Indicates the type of transform: batch or continuous.

    • trigger_count (or tc): The number of times the transform has been triggered by the scheduler. For example, the scheduler triggers the transform indexer to check for updates or ingest new data at an interval specified in the frequency property.

    • version (or v): The version of Elasticsearch that existed on the node when the transform was created.

  • time string

    The unit used to display time values.

    Values are nanos, micros, ms, s, m, h, or d.

  • size number

    The maximum number of transforms to obtain.

Responses

  • 200 application/json
    Hide response attributes Show response attributes object
    • id string

      The transform identifier.

    • state string

      The status of the transform. Returned values include: aborting: The transform is aborting. failed: The transform failed. For more information about the failure, check thereasonfield. indexing: The transform is actively processing data and creating new documents. started: The transform is running but not actively indexing data. stopped: The transform is stopped. stopping`: The transform is stopping.

    • checkpoint string

      The sequence number for the checkpoint.

    • documents_processed string

      The number of documents that have been processed from the source index of the transform.

    • checkpoint_progress string | null

      The progress of the next checkpoint that is currently in progress.

    • last_search_time string | null

      The timestamp of the last search in the source indices. This field is shown only if the transform is running.

    • changes_last_detection_time string | null

      The timestamp when changes were last detected in the source indices.

    • create_time string

      The time the transform was created.

    • version string

      The version of Elasticsearch that existed on the node when the transform was created.

    • source_index string

      The source indices for the transform.

    • dest_index string

      The destination index for the transform.

    • pipeline string

      The unique identifier for the ingest pipeline.

    • description string

      The description of the transform.

    • transform_type string

      The type of transform: batch or continuous.

    • frequency string

      The interval between checks for changes in the source indices when the transform is running continuously.

    • max_page_search_size string

      The initial page size that is used for the composite aggregation for each checkpoint.

    • docs_per_second string

      The number of input documents per second.

    • reason string

      If a transform has a failed state, these details describe the reason for failure.

    • search_total string

      The total number of search operations on the source index for the transform.

    • search_failure string

      The total number of search failures.

    • search_time string

      The total amount of search time, in milliseconds.

    • index_total string

      The total number of index operations done by the transform.

    • index_failure string

      The total number of indexing failures.

    • index_time string

      The total time spent indexing documents, in milliseconds.

    • documents_indexed string

      The number of documents that have been indexed into the destination index for the transform.

    • delete_time string

      The total time spent deleting documents, in milliseconds.

    • documents_deleted string

      The number of documents deleted from the destination index due to the retention policy for the transform.

    • trigger_count string

      The number of times the transform has been triggered by the scheduler. For example, the scheduler triggers the transform indexer to check for updates or ingest new data at an interval specified in the frequency property.

    • pages_processed string

      The number of search or bulk index operations processed. Documents are processed in batches instead of individually.

    • processing_time string

      The total time spent processing results, in milliseconds.

    • checkpoint_duration_time_exp_avg string

      The exponential moving average of the duration of the checkpoint, in milliseconds.

    • indexed_documents_exp_avg string

      The exponential moving average of the number of new documents that have been indexed.

    • processed_documents_exp_avg string

      The exponential moving average of the number of documents that have been processed.

GET /_cat/transforms/{transform_id}
GET /_cat/transforms?v=true&format=json
resp = client.cat.transforms(
    v=True,
    format="json",
)
const response = await client.cat.transforms({
  v: "true",
  format: "json",
});
response = client.cat.transforms(
  v: "true",
  format: "json"
)
$resp = $client->cat()->transforms([
    "v" => "true",
    "format" => "json",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/transforms?v=true&format=json"
Response examples (200)
A successful response from `GET /_cat/transforms?v=true&format=json`.
[
  {
    "id" : "ecommerce_transform",
    "state" : "started",
    "checkpoint" : "1",
    "documents_processed" : "705",
    "checkpoint_progress" : "100.00",
    "changes_last_detection_time" : null
  }
]