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//lcawley: Verified example output 2017-04-11
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[[ml-post-data]]
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==== Post Data to Jobs
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The post data API enables you to send data to an anomaly detection job for analysis.
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The job must have been opened prior to sending data.
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===== Request
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`POST _xpack/ml/anomaly_detectors/<job_id>/_data --data-binary @<data-file.json>`
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===== Description
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File sizes are limited to 100 Mb, so if your file is larger, then split it into
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multiple files and upload each one separately in sequential time order. When
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running in real time, it is generally recommended to perform many small uploads,
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rather than queueing data to upload larger files.
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When uploading data, check the <<ml-datacounts,job data counts>> for progress.
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The following records will not be processed:
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* Records not in chronological order and outside the latency window
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* Records with an invalid timestamp
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//TBD link to Working with Out of Order timeseries concept doc
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2017-04-27 14:17:06 -04:00
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IMPORTANT: Data can only be accepted from a single connection. Use a single
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connection synchronously to send data, close, flush, or delete a single job.
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It is not currently possible to post data to multiple jobs using wildcards
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or a comma-separated list.
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===== Path Parameters
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`job_id` (required)::
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(string) Identifier for the job
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===== Request Body
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`reset_start`::
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(string) Specifies the start of the bucket resetting range
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`reset_end`::
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(string) Specifies the end of the bucket resetting range
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===== Authorization
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You must have `manage_ml`, or `manage` cluster privileges to use this API.
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For more information, see <<privileges-list-cluster>>.
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===== Examples
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The following example posts data from the farequote.json file to the `farequote` job:
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[source,js]
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--------------------------------------------------
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$ curl -s -H "Content-type: application/json"
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-X POST http:\/\/localhost:9200/_xpack/ml/anomaly_detectors/it_ops_new_kpi/_data
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--data-binary @it_ops_new_kpi.json
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--------------------------------------------------
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2017-04-11 21:52:47 -04:00
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//TBD: Create example of how to post a small data example in Kibana?
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When the data is sent, you receive information about the operational progress of the job.
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For example:
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[source,js]
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----
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{
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"job_id":"it_ops_new_kpi",
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"processed_record_count":21435,
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"processed_field_count":64305,
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"input_bytes":2589063,
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"input_field_count":85740,
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"invalid_date_count":0,
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"missing_field_count":0,
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"out_of_order_timestamp_count":0,
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"empty_bucket_count":16,
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"sparse_bucket_count":0,
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"bucket_count":2165,
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"earliest_record_timestamp":1454020569000,
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"latest_record_timestamp":1455318669000,
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"last_data_time":1491952300658,
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"latest_empty_bucket_timestamp":1454541600000,
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"input_record_count":21435
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}
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----
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2017-04-19 13:52:30 -04:00
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For more information about these properties, see <<ml-jobstats,Job Stats>>.
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