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Elastic Docs ›Elasticsearch Guide [master] ›REST APIs ›Machine learning anomaly detection APIs

Create datafeeds API

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Create datafeeds API

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Instantiates a datafeed.

Request

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PUT _ml/datafeeds/<feed_id>

Prerequisites

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  • You must create an anomaly detection job before you create a datafeed.

  • Requires the following privileges:

    • cluster: manage_ml (the machine_learning_admin built-in role grants this privilege)
    • source index configured in the datafeed: read

Description

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Datafeeds retrieve data from Elasticsearch for analysis by an anomaly detection job. You can associate only one datafeed to each anomaly detection job.

The datafeed contains a query that runs at a defined interval (frequency). If you are concerned about delayed data, you can add a delay (query_delay) at each interval. See Handling delayed data.

  • You must use Kibana, this API, or the create anomaly detection jobs API to create a datafeed. Do not add a datafeed directly to the .ml-config index using the Elasticsearch index API. If Elasticsearch security features are enabled, do not give users write privileges on the .ml-config index.
  • When Elasticsearch security features are enabled, your datafeed remembers which roles the user who created it had at the time of creation and runs the query using those same roles. If you provide secondary authorization headers, those credentials are used instead.

Path parameters

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<feed_id>
(Required, string) A numerical character string that uniquely identifies the datafeed. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.

Query parameters

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allow_no_indices
(Optional, Boolean) If true, wildcard indices expressions that resolve into no concrete indices are ignored. This includes the _all string or when no indices are specified. Defaults to true.
expand_wildcards

(Optional, string) Type of index that wildcard patterns can match. If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams. Supports comma-separated values, such as open,hidden. Valid values are:

all
Match any data stream or index, including hidden ones.
open
Match open, non-hidden indices. Also matches any non-hidden data stream.
closed
Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.
hidden
Match hidden data streams and hidden indices. Must be combined with open, closed, or both.
none
Wildcard patterns are not accepted.

Defaults to open.

ignore_throttled

(Optional, Boolean) If true, concrete, expanded or aliased indices are ignored when frozen. Defaults to true.

[7.16.0] Deprecated in 7.16.0.

ignore_unavailable
(Optional, Boolean) If true, unavailable indices (missing or closed) are ignored. Defaults to false.

Request body

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aggregations
(Optional, object) If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data. For more information, see Aggregating data for faster performance.
chunking_config

(Optional, object) Datafeeds might be required to search over long time periods, for several months or years. This search is split into time chunks in order to ensure the load on Elasticsearch is managed. Chunking configuration controls how the size of these time chunks are calculated and is an advanced configuration option.

Properties of chunking_config
mode

(string) There are three available modes:

  • auto: The chunk size is dynamically calculated. This is the default and recommended value when the datafeed does not use aggregations.
  • manual: Chunking is applied according to the specified time_span. Use this mode when the datafeed uses aggregations.
  • off: No chunking is applied.
time_span
(time units) The time span that each search will be querying. This setting is only applicable when the mode is set to manual. For example: 3h.
delayed_data_check_config

(Optional, object) Specifies whether the datafeed checks for missing data and the size of the window. For example: {"enabled": true, "check_window": "1h"}.

The datafeed can optionally search over indices that have already been read in an effort to determine whether any data has subsequently been added to the index. If missing data is found, it is a good indication that the query_delay option is set too low and the data is being indexed after the datafeed has passed that moment in time. See Working with delayed data.

This check runs only on real-time datafeeds.

Properties of delayed_data_check_config
check_window
(time units) The window of time that is searched for late data. This window of time ends with the latest finalized bucket. It defaults to null, which causes an appropriate check_window to be calculated when the real-time datafeed runs. In particular, the default check_window span calculation is based on the maximum of 2h or 8 * bucket_span.
enabled
(Boolean) Specifies whether the datafeed periodically checks for delayed data. Defaults to true.
frequency
(Optional, time units) The interval at which scheduled queries are made while the datafeed runs in real time. The default value is either the bucket span for short bucket spans, or, for longer bucket spans, a sensible fraction of the bucket span. For example: 150s. When frequency is shorter than the bucket span, interim results for the last (partial) bucket are written then eventually overwritten by the full bucket results. If the datafeed uses aggregations, this value must be divisible by the interval of the date histogram aggregation.
indices

(Required, array) An array of index names. Wildcards are supported. For example: ["it_ops_metrics", "server*"].

If any indices are in remote clusters then the machine learning nodes need to have the remote_cluster_client role.

indices_options

(Optional, object) Specifies index expansion options that are used during search.

For example:

{
   "expand_wildcards": ["all"],
   "ignore_unavailable": true,
   "allow_no_indices": "false",
   "ignore_throttled": true
}

For more information about these options, see Multi-target syntax.

job_id
(Required, string) Identifier for the anomaly detection job.
max_empty_searches
(Optional,integer) If a real-time datafeed has never seen any data (including during any initial training period) then it will automatically stop itself and close its associated job after this many real-time searches that return no documents. In other words, it will stop after frequency times max_empty_searches of real-time operation. If not set then a datafeed with no end time that sees no data will remain started until it is explicitly stopped. By default this setting is not set.
query
(Optional, object) The Elasticsearch query domain-specific language (DSL). This value corresponds to the query object in an Elasticsearch search POST body. All the options that are supported by Elasticsearch can be used, as this object is passed verbatim to Elasticsearch. By default, this property has the following value: {"match_all": {"boost": 1}}.
query_delay
(Optional, time units) The number of seconds behind real time that data is queried. For example, if data from 10:04 a.m. might not be searchable in Elasticsearch until 10:06 a.m., set this property to 120 seconds. The default value is randomly selected between 60s and 120s. This randomness improves the query performance when there are multiple jobs running on the same node. For more information, see Handling delayed data.
runtime_mappings

(Optional, object) Specifies runtime fields for the datafeed search.

For example:

{
  "day_of_week": {
    "type": "keyword",
    "script": {
      "source": "emit(doc['@timestamp'].value.dayOfWeekEnum.getDisplayName(TextStyle.FULL, Locale.ENGLISH))"
    }
  }
}
script_fields
(Optional, object) Specifies scripts that evaluate custom expressions and returns script fields to the datafeed. The detector configuration objects in a job can contain functions that use these script fields. For more information, see Transforming data with script fields and Script fields.
scroll_size
(Optional, unsigned integer) The size parameter that is used in Elasticsearch searches when the datafeed does not use aggregations. The default value is 1000. The maximum value is the value of index.max_result_window which is 10,000 by default.

Examples

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Create a datafeed for an anomaly detection job (test-job):

resp = client.ml.put_datafeed(
    datafeed_id="datafeed-test-job",
    pretty=True,
    indices=[
        "kibana_sample_data_logs"
    ],
    query={
        "bool": {
            "must": [
                {
                    "match_all": {}
                }
            ]
        }
    },
    job_id="test-job",
)
print(resp)
const response = await client.ml.putDatafeed({
  datafeed_id: "datafeed-test-job",
  pretty: "true",
  indices: ["kibana_sample_data_logs"],
  query: {
    bool: {
      must: [
        {
          match_all: {},
        },
      ],
    },
  },
  job_id: "test-job",
});
console.log(response);
PUT _ml/datafeeds/datafeed-test-job?pretty
{
  "indices": [
    "kibana_sample_data_logs"
  ],
  "query": {
    "bool": {
      "must": [
        {
          "match_all": {}
        }
      ]
    }
  },
  "job_id": "test-job"
}

When the datafeed is created, you receive the following results:

{
  "datafeed_id" : "datafeed-test-job",
  "job_id" : "test-job",
  "authorization" : {
    "roles" : [
      "superuser"
    ]
  },
  "query_delay" : "91820ms",
  "chunking_config" : {
    "mode" : "auto"
  },
  "indices_options" : {
    "expand_wildcards" : [
      "open"
    ],
    "ignore_unavailable" : false,
    "allow_no_indices" : true,
    "ignore_throttled" : true
  },
  "query" : {
    "bool" : {
      "must" : [
        {
          "match_all" : { }
        }
      ]
    }
  },
  "indices" : [
    "kibana_sample_data_logs"
  ],
  "scroll_size" : 1000,
  "delayed_data_check_config" : {
    "enabled" : true
  }
}
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