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[DOCS] Update all ML API examples with latest build output 

Original commit: elastic/x-pack-elasticsearch@f9fa3b813a
This commit is contained in:
lcawley 2017-04-11 18:52:47 -07:00
parent 298ee9f554
commit 1a6f813d5a
32 changed files with 538 additions and 409 deletions
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19
docs/en/rest-api/ml/close-job.asciidoc
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@ -1,8 +1,9 @@
//lcawley Verified example output 2017-04-11
[[ml-close-job]]
==== Close Jobs
An anomaly detection job must be opened in order for it to be ready to receive and analyze data.
A job may be opened and closed multiple times throughout its lifecycle.
The close job API enables you to close a job.
A job can be opened and closed multiple times throughout its lifecycle.
===== Request
@ -10,24 +11,24 @@ A job may be opened and closed multiple times throughout its lifecycle.
===== Description
A job can be closed once all data has been analyzed.
//A job can be closed once all data has been analyzed.
When you close a job, it runs housekeeping tasks such as pruning the model history,
flushing buffers, calculating final results and persisting the internal models.
flushing buffers, calculating final results and persisting the model snapshots.
Depending upon the size of the job, it could take several minutes to close and
the equivalent time to re-open.
Once closed, the anomaly detection job has almost no overhead on the cluster
(except for maintaining its meta data). A closed job is blocked for receiving
data and analysis operations, however you can still explore and navigate results.
After it is closed, the job has almost no overhead on the cluster except for
maintaining its meta data. A closed job cannot receive data or perform analysis
operations, but you can still explore and navigate results.
//NOTE:
//NOTE: TBD
//OUTDATED?: If using the {prelert} UI, the job will be automatically closed when stopping a datafeed job.
===== Path Parameters
`job_id` (required)::
(+string+) Identifier for the job
(+string+) Identifier for the job
===== Query Parameters

19
docs/en/rest-api/ml/datafeedresource.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-datafeed-resource]]
==== Data Feed Resources
@ -14,6 +15,10 @@ A data feed resource has the following properties:
"min_doc_count": 0}, "aggregations": {"events_per_min": {"sum": {
"field": "events_per_min"}}}}}`.
`chunking_config`::
(+object+) TBD.
For example: {"mode": "manual", "time_span": "30000000ms"}
`datafeed_id`::
(+string+) A numerical character string that uniquely identifies the data feed.
@ -41,6 +46,7 @@ A data feed resource has the following properties:
`types` (required)::
(+array+) TBD. For example: ["network","sql","kpi"]
[float]
[[ml-datafeed-counts]]
==== Data Feed Counts
@ -48,14 +54,19 @@ The get data feed statistics API provides information about the operational
progress of a data feed. For example:
`assigment_explanation`::
TBD. For example: ""
TBD. For example: " "
`datafeed_id`::
(+string+) A numerical character string that uniquely identifies the data feed.
`node`::
(+object+) TBD
The node that is running the query?
For example: `{"id": "0-o0tOoRTwKFZifatTWKNw","name": "0-o0tOo",
"ephemeral_id": "DOZltLxLS_SzYpW6hQ9hyg","transport_address": "127.0.0.1:9300",
"attributes": {"max_running_jobs": "10"}}
`id`::: TBD. For example, "0-o0tOoRTwKFZifatTWKNw".
`name`::: TBD. For example, "0-o0tOo".
`ephemeral_id::: TBD. For example, "DOZltLxLS_SzYpW6hQ9hyg".
`transport_address::: TBD. For example, "127.0.0.1:9300".
`attributes`::: TBD. For example, {"max_running_jobs": "10"}.
`state`::
(+string+) The status of the data feed,

1
docs/en/rest-api/ml/delete-datafeed.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-delete-datafeed]]
==== Delete Data Feeds

4
docs/en/rest-api/ml/delete-job.asciidoc
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@ -1,3 +1,4 @@
//lcawley: Verified example output 2017-04-11
[[ml-delete-job]]
==== Delete Jobs
@ -17,7 +18,7 @@ IMPORTANT: Deleting a job must be done via this API only. Do not delete the
privileges are granted to anyone over the `.ml-*` indices.
Before you can delete a job, you must delete the data feeds that are associated with it.
//See <<>>.
See <<ml-delete-datafeed,Delete Data Feeds>>.
It is not currently possible to delete multiple jobs using wildcards or a comma separated list.
@ -25,6 +26,7 @@ It is not currently possible to delete multiple jobs using wildcards or a comma
`job_id` (required)::
(+string+) Identifier for the job
////
===== Responses

2
docs/en/rest-api/ml/delete-snapshot.asciidoc
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@ -1,7 +1,7 @@
[[ml-delete-snapshot]]
==== Delete Model Snapshots
The delete model snapshot API allows you to delete an existing model snapshot.
The delete model snapshot API enables you to delete an existing model snapshot.
===== Request

30
docs/en/rest-api/ml/flush-job.asciidoc
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@ -1,15 +1,16 @@
//lcawley: Verified example output 2017-04-11
[[ml-flush-job]]
==== Flush Jobs
The flush job API forces any buffered data to be processed by the {ml} job.
The flush job API forces any buffered data to be processed by the job.
===== Request
`POST _xpack/ml/anomaly_detectors/<job_id>/_flush`
===== Description
The flush job API is only applicable when sending data for analysis using the POST `_data` API.
Depending on the content of the buffer, then it might additionally calculate new results.
The flush job API is only applicable when sending data for analysis using the <<ml-post-data,post data API>>. Depending on the content of the buffer, then it might additionally calculate new results.
Both flush and close operations are similar, however the flush is more efficient if you are expecting to send more data for analysis.
When flushing, the job remains open and is available to continue analyzing data.
@ -22,21 +23,20 @@ A close operation additionally prunes and persists the model state to disk and t
===== Query Parameters
`calc_interim`::
(+boolean+; default: ++false++) If true (default false), will calculate interim
results for the most recent bucket or all buckets within the latency period
`start`::
(+string+; default: ++null++) When used in conjunction with `calc_interim`,
specifies the range of buckets on which to calculate interim results
`advance_time`::
(+string+) Specifies that no data prior to the date `advance_time` is expected.
`end`::
(+string+; default: ++null++) When used in conjunction with `calc_interim`,
specifies the range of buckets on which to calculate interim results
(+string+) When used in conjunction with `calc_interim`, specifies the range
of buckets on which to calculate interim results.
`calc_interim`::
(+boolean+) If true, calculates the interim results for the most recent bucket
or all buckets within the latency period.
`advance_time`::
(+string+; default: ++null++) Specifies that no data prior to the date `advance_time` is expected
`start`::
(+string+) When used in conjunction with `calc_interim`, specifies the range of
buckets on which to calculate interim results.
////
===== Responses
@ -49,7 +49,7 @@ A close operation additionally prunes and persists the model state to disk and t
////
===== Examples
The following example flushes the `event_rate` job:
The following example flushes the `farequote` job:
[source,js]
--------------------------------------------------

17
docs/en/rest-api/ml/get-bucket.asciidoc
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@ -1,7 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-get-bucket]]
==== Get Buckets
The get bucket API allows you to retrieve information about buckets in the
The get bucket API enables you to retrieve information about buckets in the
results from a job.
===== Request
@ -93,9 +94,9 @@ score and time constraints:
{
"job_id": "it-ops-kpi",
"timestamp": 1454943900000,
"anomaly_score": 87.2526,
"anomaly_score": 94.1706,
"bucket_span": 300,
"initial_anomaly_score": 83.3831,
"initial_anomaly_score": 94.1706,
"record_count": 1,
"event_count": 153,
"is_interim": false,
@ -104,17 +105,17 @@ score and time constraints:
"job_id": "it-ops-kpi",
"result_type": "bucket_influencer",
"influencer_field_name": "bucket_time",
"initial_anomaly_score": 83.3831,
"anomaly_score": 87.2526,
"raw_anomaly_score": 2.02204,
"probability": 0.0000109783,
"initial_anomaly_score": 94.1706,
"anomaly_score": 94.1706,
"raw_anomaly_score": 2.32119,
"probability": 0.00000575042,
"timestamp": 1454943900000,
"bucket_span": 300,
"sequence_num": 2,
"is_interim": false
}
],
"processing_time_ms": 3,
"processing_time_ms": 2,
"partition_scores": [],
"result_type": "bucket"
}

4
docs/en/rest-api/ml/get-category.asciidoc
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@ -1,7 +1,9 @@
//lcawley Verified example output 2017-04-11
[[ml-get-category]]
==== Get Categories
The get categories API allows you to retrieve information about the categories in the results for a job.
The get categories API enables you to retrieve information
about the categories in the results for a job.
===== Request

44
docs/en/rest-api/ml/get-datafeed-stats.asciidoc
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@ -1,7 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-get-datafeed-stats]]
==== Get Data Feed Statistics
The get data feed statistics API allows you to retrieve usage information for
The get data feed statistics API enables you to retrieve usage information for
data feeds.
===== Request
@ -10,7 +11,6 @@ data feeds.
`GET _xpack/ml/datafeeds/<feed_id>/_stats`
===== Description
If the data feed is stopped, the only information you receive is the
@ -25,24 +25,12 @@ If the data feed is stopped, the only information you receive is the
===== Results
The API returns the following usage information:
The API returns the following information:
`assigment_explanation`::
TBD
For example: ""
`datafeeds`::
(+array+) An array of data feed count objects.
For more information, see <<ml-datafeed-counts,Data Feed Counts>>.
`datafeed_id`::
(+string+) A numerical character string that uniquely identifies the data feed.
`node`::
(+object+) TBD
`state`::
(+string+) The status of the data feed, which can be one of the following values:
* `started`: The data feed is actively receiving data.
* `stopped`: The data feed is stopped and will not receive data until
it is re-started.
//failed?
////
===== Responses
@ -55,7 +43,17 @@ The API returns the following usage information:
////
===== Examples
.Example results for a started job
The following example gets usage information for the
`datafeed-farequote` data feed:
[source,js]
--------------------------------------------------
GET _xpack/ml/datafeeds/datafeed-farequote/_stats
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]
The API returns the following results:
----
{
"count": 1,
@ -64,15 +62,15 @@ The API returns the following usage information:
"datafeed_id": "datafeed-farequote",
"state": "started",
"node": {
"id": "0-o0tOoRTwKFZifatTWKNw",
"name": "0-o0tOo",
"ephemeral_id": "DOZltLxLS_SzYpW6hQ9hyg",
"id": "IO_gxe2_S8mrzu7OpmK5Jw",
"name": "IO_gxe2",
"ephemeral_id": "KHMWPZoMToOzSsZY9lDDgQ",
"transport_address": "127.0.0.1:9300",
"attributes": {
"max_running_jobs": "10"
}
},
"assigment_explanation": ""
"assignment_explanation": ""
}
]
}

47
docs/en/rest-api/ml/get-datafeed.asciidoc
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@ -1,7 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-get-datafeed]]
==== Get Data Feeds
The get data feeds API allows you to retrieve configuration information for
The get data feeds API enables you to retrieve configuration information for
data feeds.
===== Request
@ -23,14 +24,13 @@ OUTDATED?: The get job API can also be applied to all jobs by using `_all` as th
===== Results
The API returns information about the data feed resource.
For more information, see <<ml-datafeed-resource,data feed resources>>.
The API returns the following information:
`datafeeds`::
(+array+) An array of data feed objects.
For more information, see <<ml-datafeed-resource,data feed resources>>.
////
===== Query Parameters
None
===== Responses
200
@ -42,22 +42,32 @@ None
////
===== Examples
.Example results for a single data feed
The following example gets configuration information for the
`datafeed-it-ops-kpi` data feed:
[source,js]
--------------------------------------------------
GET _xpack/ml/datafeeds/datafeed-it-ops-kpi
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]
The API returns the following results:
----
{
"count": 1,
"datafeeds": [
{
"datafeed_id": "datafeed-it-ops",
"job_id": "it-ops",
"datafeed_id": "datafeed-it-ops-kpi",
"job_id": "it-ops-kpi",
"query_delay": "60s",
"frequency": "150s",
"indexes": [
"it_ops_metrics"
],
"types": [
"network",
"kpi",
"network",
"sql"
],
"query": {
@ -66,8 +76,8 @@ None
}
},
"aggregations": {
"@timestamp": {
"histogram": {
"buckets": {
"date_histogram": {
"field": "@timestamp",
"interval": 30000,
"offset": 0,
@ -82,11 +92,20 @@ None
"sum": {
"field": "events_per_min"
}
},
"@timestamp": {
"max": {
"field": "@timestamp"
}
}
}
}
},
"scroll_size": 1000
"scroll_size": 1000,
"chunking_config": {
"mode": "manual",
"time_span": "30000000ms"
}
}
]
}

5
docs/en/rest-api/ml/get-influencer.asciidoc
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@ -1,7 +1,7 @@
[[ml-get-influencer]]
==== Get Influencers
The get influencers API allows you to retrieve information about the influencers
The get influencers API enables you to retrieve information about the influencers
in a job.
===== Request
@ -11,7 +11,6 @@ in a job.
////
===== Description
////
===== Path Parameters
@ -83,7 +82,7 @@ In this example, the API returns the following information, sorted based on the
influencer score in descending order:
----
{
"count": 22,
"count": 28,
"influencers": [
{
"job_id": "it_ops_new_kpi",

82
docs/en/rest-api/ml/get-job-stats.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-get-job-stats]]
==== Get Job Statistics
@ -12,7 +13,6 @@ The get jobs API allows you to retrieve usage information for jobs.
////
===== Description
TBD
////
===== Path Parameters
@ -23,34 +23,11 @@ TBD
===== Results
The API returns the following usage information:
The API returns the following information:
`job_id`::
(+string+) A numerical character string that uniquely identifies the job.
`data_counts`::
(+object+) An object that describes the number of records processed and any related error counts.
See <<ml-datacounts,data counts objects>>.
`model_size_stats`::
(+object+) An object that provides information about the size and contents of the model.
See <<ml-modelsizestats,model size stats objects>>
`state`::
(+string+) The status of the job, which can be one of the following values:
`open`::: The job is actively receiving and processing data.
`closed`::: The job finished successfully with its model state persisted.
The job is still available to accept further data.
`closing`::: TBD
`failed`::: The job did not finish successfully due to an error.
This situation can occur due to invalid input data. In this case,
sending corrected data to a failed job re-opens the job and
resets it to an open state.
NOTE: If you send data in a periodic cycle and close the job at the end of
each transaction, the job is marked as closed in the intervals between
when data is sent. For example, if data is sent every minute and it takes
1 second to process, the job has a closed state for 59 seconds.
`jobs`::
(+array+) An array of job count objects.
For more information, see <<ml-jobcounts,Job Counts>>.
////
===== Responses
@ -64,41 +41,52 @@ when data is sent. For example, if data is sent every minute and it takes
////
===== Examples
.Example results for a single job
The following example gets usage information for the `farequote` job:
[source,js]
--------------------------------------------------
GET _xpack/ml/anomaly_detectors/farequote/_stats
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]
In this example, the API returns a single result that matches the specified
score and time constraints:
----
{
"count": 1,
"jobs": [
{
"job_id": "it-ops-kpi",
"job_id": "farequote",
"data_counts": {
"job_id": "it-ops",
"processed_record_count": 43272,
"processed_field_count": 86544,
"input_bytes": 2846163,
"input_field_count": 86544,
"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": 0,
"bucket_count": 4329,
"earliest_record_timestamp": 1454020560000,
"latest_record_timestamp": 1455318900000,
"last_data_time": 1491235405945,
"input_record_count": 43272
"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
},
"model_size_stats": {
"job_id": "it-ops",
"job_id": "farequote",
"result_type": "model_size_stats",
"model_bytes": 25586,
"total_by_field_count": 3,
"model_bytes": 387594,
"total_by_field_count": 21,
"total_over_field_count": 0,
"total_partition_field_count": 2,
"total_partition_field_count": 20,
"bucket_allocation_failures_count": 0,
"memory_status": "ok",
"log_time": 1491235406000,
"timestamp": 1455318600000
"log_time": 1491948163000,
"timestamp": 1455234600000
},
"state": "closed"
}

58
docs/en/rest-api/ml/get-job.asciidoc
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@ -1,17 +1,18 @@
//lcawley Verified example output 2017-04-11
[[ml-get-job]]
==== Get Jobs
The get jobs API allows you to retrieve configuration information about jobs.
The get jobs API enables you to retrieve configuration information for jobs.
===== Request
`GET _xpack/ml/anomaly_detectors/` +
`GET _xpack/ml/anomaly_detectors/<job_id>`
////
===== Description
OUTDATED?: The get job API can also be applied to all jobs by using `_all` as the job name.
////
===== Path Parameters
@ -21,14 +22,13 @@ OUTDATED?: The get job API can also be applied to all jobs by using `_all` as th
===== Results
The API returns information about the job resource. For more information, see
<<ml-job-resource,job resources>>.
he API returns the following information:
`jobs`::
(+array+) An array of job resources.
For more information, see <<ml-job-resource,Job Resources>>.
////
===== Query Parameters
None
===== Responses
200
@ -40,40 +40,48 @@ None
////
===== Examples
.Example results for a single job
The following example gets configuration information for the `farequote` job:
[source,js]
--------------------------------------------------
GET _xpack/ml/anomaly_detectors/farequote
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]
In this example, the API returns a single result that matches the specified
score and time constraints:
----
{
"count": 1,
"jobs": [
{
"job_id": "it-ops-kpi",
"description": "First simple job",
"create_time": 1491007356077,
"finished_time": 1491007365347,
{
"job_id": "farequote",
"job_type": "anomaly_detector",
"description": "Multi-metric job",
"create_time": 1491948149563,
"finished_time": 1491948166289,
"analysis_config": {
"bucket_span": "5m",
"latency": "0ms",
"summary_count_field_name": "doc_count",
"detectors": [
{
"detector_description": "low_sum(events_per_min)",
"function": "low_sum",
"field_name": "events_per_min",
"detector_description": "mean(responsetime)",
"function": "mean",
"field_name": "responsetime",
"partition_field_name": "airline",
"detector_rules": []
}
],
"influencers": [],
"use_per_partition_normalization": false
"influencers": [
"airline"
]
},
"data_description": {
"time_field": "@timestamp",
"time_format": "epoch_ms"
},
"model_plot_config": {
"enabled": true
},
"model_snapshot_retention_days": 1,
"model_snapshot_id": "1491007364",
"model_snapshot_id": "1491948163",
"results_index_name": "shared"
}
]

47
docs/en/rest-api/ml/get-record.asciidoc
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@ -1,7 +1,8 @@
[[ml-get-record]]
//lcawley Verified example output 2017-04-11
==== Get Records
The get records API allows you to retrieve anomaly records for a job.
The get records API enables you to retrieve anomaly records for a job.
===== Request
@ -10,7 +11,6 @@ The get records API allows you to retrieve anomaly records for a job.
////
===== Description
////
===== Path Parameters
@ -21,7 +21,6 @@ The get records API allows you to retrieve anomaly records for a job.
`desc`::
(+boolean+) If true, the results are sorted in descending order.
//TBD: Using the "sort" value?
`end`::
(+string+) Returns records with timestamps earlier than this time.
@ -77,54 +76,42 @@ The following example gets bucket information for the `it-ops-kpi` job:
[source,js]
--------------------------------------------------
GET _xpack/ml/anomaly_detectors/it-ops-kpi/results/buckets
GET _xpack/ml/anomaly_detectors/it-ops-kpi/results/records
{
"sort": "record_score",
"desc": true,
"start": "1454944200000"
"start": "1454944100000"
}
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]
In this example, the API returns a single result that matches the specified
score and time constraints:
In this example, the API returns twelve results for the specified
time constraints:
----
{
"count": 6,
"count": 12,
"records": [
{
"job_id": "it_ops_new_kpi",
"job_id": "it-ops-kpi",
"result_type": "record",
"probability": 0.000113075,
"record_score": 86.9677,
"initial_record_score": 82.8891,
"bucket_span": 600,
"probability": 0.00000332668,
"record_score": 72.9929,
"initial_record_score": 65.7923,
"bucket_span": 300,
"detector_index": 0,
"sequence_num": 1,
"is_interim": false,
"timestamp": 1454944200000,
"partition_field_name": "kpi_indicator",
"partition_field_value": "online_purchases",
"function": "low_non_zero_count",
"function_description": "count",
"function": "low_sum",
"function_description": "sum",
"typical": [
3582.53
1806.48
],
"actual": [
575
288
],
"influencers": [
{
"influencer_field_name": "kpi_indicator",
"influencer_field_values": [
"online_purchases"
]
}
],
"kpi_indicator": [
"online_purchases"
]
"field_name": "events_per_min"
},
...
]

30
docs/en/rest-api/ml/get-snapshot.asciidoc
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@ -1,7 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-get-snapshot]]
==== Get Model Snapshots
The get model snapshots API allows you to retrieve information about model snapshots.
The get model snapshots API enables you to retrieve information about model snapshots.
===== Request
@ -11,7 +12,6 @@ The get model snapshots API allows you to retrieve information about model snaps
////
===== Description
OUTDATED?: The get job API can also be applied to all jobs by using `_all` as the job name.
////
===== Path Parameters
@ -74,7 +74,7 @@ The following example gets model snapshot information for the
[source,js]
--------------------------------------------------
GET _xpack/ml/anomaly_detectors/it_ops_new_logs/model_snapshots
GET _xpack/ml/anomaly_detectors/farequote/model_snapshots
{
"start": "1491852977000"
}
@ -88,25 +88,25 @@ In this example, the API provides a single result:
"count": 1,
"model_snapshots": [
{
"job_id": "it_ops_new_logs",
"timestamp": 1491852978000,
"description": "State persisted due to job close at 2017-04-10T12:36:18-0700",
"snapshot_id": "1491852978",
"job_id": "farequote",
"timestamp": 1491948163000,
"description": "State persisted due to job close at 2017-04-11T15:02:43-0700",
"snapshot_id": "1491948163",
"snapshot_doc_count": 1,
"model_size_stats": {
"job_id": "it_ops_new_logs",
"job_id": "farequote",
"result_type": "model_size_stats",
"model_bytes": 100393,
"total_by_field_count": 13,
"model_bytes": 387594,
"total_by_field_count": 21,
"total_over_field_count": 0,
"total_partition_field_count": 2,
"total_partition_field_count": 20,
"bucket_allocation_failures_count": 0,
"memory_status": "ok",
"log_time": 1491852978000,
"timestamp": 1455229800000
"log_time": 1491948163000,
"timestamp": 1455234600000
},
"latest_record_time_stamp": 1455232663000,
"latest_result_time_stamp": 1455229800000,
"latest_record_time_stamp": 1455235196000,
"latest_result_time_stamp": 1455234900000,
"retain": false
}
]

113
docs/en/rest-api/ml/jobcounts.asciidoc
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@ -1,33 +1,56 @@
//lcawley Verified example output 2017-04-11
[[ml-jobcounts]]
==== Job Counts
The `data_counts` object provides information about the operational progress of a job.
It describes the number of records processed and any related error counts.
The get job statistics API provides information about the operational
progress of a job.
NOTE: Job 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.
[[ml-datacounts]]
===== Data Counts Objects
A `data_counts` object has the following properties:
`data_counts`::
(+object+) An object that describes the number of records processed and any related error counts.
See <<ml-datacounts,data counts objects>>.
`job_id`::
(+string+) A numerical character string that uniquely identifies the job.
`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.
+
The following records are not processed:
* Records not in chronological order and outside the latency window
* Records with invalid timestamp
* Records filtered by an exclude transform
`model_size_stats`::
(+object+) An object that provides information about the size and contents of the model.
See <<ml-modelsizestats,model size stats objects>>
`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.
`state`::
(+string+) The status of the job, which can be one of the following values:
`open`::: The job is actively receiving and processing data.
`closed`::: The job finished successfully with its model state persisted.
The job is still available to accept further data.
`closing`::: TBD
`failed`::: The job did not finish successfully due to an error.
This situation can occur due to invalid input data. In this case,
sending corrected data to a failed job re-opens the job and
resets it to an open state.
NOTE: If you send data in a periodic cycle and close the job at the end of
each transaction, the job is marked as closed in the intervals between
when data is sent. For example, if data is sent every minute and it takes
1 second to process, the job has a closed state for 59 seconds.
[float]
[[ml-datacounts]]
===== Data Counts Objects
The `data_counts` object describes the number of records processed
and any related error counts. It has the following properties:
`bucket_count`::
(+long+) The number of bucket results produced by the job.
`earliest_record_timestamp`::
(+string+) The timestamp of the earliest chronologically ordered record.
The datetime string is in ISO 8601 format.
`empty_bucket_count`::
TBD
`input_bytes`::
(+long+) The number of raw bytes read by the job.
@ -36,9 +59,27 @@ A `data_counts` object has the following properties:
(+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 numerical character string that uniquely identifies the job.
`last_data_time`::
(++) TBD
`latest_record_timestamp`::
(+string+) The timestamp of the last chronologically ordered record.
If the records are not in strict chronological order, this value might not be
the same as the timestamp of the last record.
The datetime string is in ISO 8601 format.
`latest_sparse_bucket_timestamp`::
(++) TBD
`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.
@ -48,32 +89,24 @@ A `data_counts` object has the following properties:
(+long+) The number of records that are out of time sequence and outside of the latency window.
These records are discarded, since jobs require time series data to be in ascending chronological order.
`empty_bucket_count`::
TBD
`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.
+
The following records are not processed:
* Records not in chronological order and outside the latency window
* Records with invalid timestamp
* Records filtered by an exclude transform
`sparse_bucket_count`::
TBD
`bucket_count`::
(+long+) The number of bucket results produced by the job.
`earliest_record_timestamp`::
(+string+) The timestamp of the earliest chronologically ordered record.
The datetime string is in ISO 8601 format.
`latest_record_timestamp`::
(+string+) The timestamp of the last chronologically ordered record.
If the records are not in strict chronological order, this value might not be
the same as the timestamp of the last record.
The datetime string is in ISO 8601 format.
`last_data_time`::
TBD
`input_record_count`::
(+long+) The number of data records read by the job.
[float]
[[ml-modelsizestats]]
===== Model Size Stats Objects

21
docs/en/rest-api/ml/jobresource.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-job-resource]]
==== Job Resources
@ -26,16 +27,19 @@ A job resource has the following properties:
`job_id`::
(+string+) A numerical character string that uniquely identifies the job.
`job_type`::
(+string+) TBD. For example: "anomaly_detector".
`model_plot_config`:: TBD
`enabled`:: TBD. For example, `true`.
`model_snapshot_id`::
TBD. For example, `1491007364`.
(+string+) A numerical character string that uniquely identifies the model
snapshot. For example, `1491007364`.
`model_snapshot_retention_days`::
(+long+) The time in days that model snapshots are retained for the job. Older snapshots are deleted.
The default value is 1 day.
(+long+) The time in days that model snapshots are retained for the job.
Older snapshots are deleted. The default value is 1 day.
`results_index_name`::
TBD. For example, `shared`.
@ -46,8 +50,8 @@ A job resource has the following properties:
An analysis configuration object has the following properties:
`bucket_span` (required)::
(+unsigned integer+) The size of the interval that the analysis is aggregated into, measured in seconds.
The default value is 300 seconds (5 minutes).
(+unsigned integer+) The size of the interval that the analysis is aggregated into, measured in seconds. The default value is 5 minutes.
//TBD: Is this now measured in minutes?
`categorization_field_name`::
(+string+) If not null, the values of the specified field will be categorized.
@ -78,8 +82,7 @@ and an error is returned.
the use of influencers is recommended as it aggregates results for each influencer entity.
`latency`::
(+unsigned integer+) The size of the window, in seconds, in which to expect data that is out of time order.
The default value is 0 seconds (no latency).
(+unsigned integer+) The size of the window, in seconds, in which to expect data that is out of time order. The default value is 0 milliseconds (no latency).
NOTE: Latency is only applicable when you send data by using the <<ml-post-data, Post Data to Jobs>> API.
@ -127,7 +130,7 @@ Each detector has the following properties:
(+string+) A description of the detector. For example, `low_sum(events_per_min)`.
`detector_rules`::
TBD
(+array+) TBD
`exclude_frequent`::
(+string+) Contains one of the following values: `all`, `none`, `by`, or `over`.

5
docs/en/rest-api/ml/open-job.asciidoc
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@ -1,8 +1,9 @@
//lcawley Verified example output 2017-04-11
[[ml-open-job]]
==== Open Jobs
An anomaly detection job must be opened in order for it to be ready to receive and analyze data.
A job may be opened and closed multiple times throughout its lifecycle.
A job must be opened in order for it to be ready to receive and analyze data.
A job can be opened and closed multiple times throughout its lifecycle.
===== Request

40
docs/en/rest-api/ml/post-data.asciidoc
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@ -1,3 +1,4 @@
//lcawley: Verified example output 2017-04-11
[[ml-post-data]]
==== Post Data to Jobs
@ -17,10 +18,9 @@ many small uploads, rather than queueing data to upload larger files.
IMPORTANT: Data can only be accepted from a single connection.
Do not attempt to access the data endpoint from different threads at the same time.
Use a single connection synchronously to send data, close, flush or delete a single job.
+
It is not currently possible to post data to multiple jobs using wildcards or a comma separated list.
Use a single connection synchronously to send data, close, flush, or delete a single job.
It is not currently possible to post data to multiple jobs using wildcards
or a comma separated list.
===== Path Parameters
@ -30,10 +30,10 @@ IMPORTANT: Data can only be accepted from a single connection.
===== Request Body
`reset_start`::
(+string+; default: ++null++) Specifies the start of the bucket resetting range
(+string+) Specifies the start of the bucket resetting range
`reset_end`::
(+string+; default: ++null++) Specifies the end of the bucket resetting range"
(+string+) Specifies the end of the bucket resetting range"
////
===== Responses
@ -55,30 +55,34 @@ The following example posts data from the farequote.json file to the `farequote`
[source,js]
--------------------------------------------------
$ curl -s -H "Content-type: application/json"
-X POST http:\/\/localhost:9200/_xpack/ml/anomaly_detectors/farequote --data-binary @farequote.json
-X POST http:\/\/localhost:9200/_xpack/ml/anomaly_detectors/it_ops_new_kpi/_data
--data-binary @it_ops_new_kpi.json
--------------------------------------------------
// CONSOLE
// TEST[skip:todo]
//TBD: Create example of how to post a small data example in Kibana?
When the data is sent, you receive information about the operational progress of the job.
For example:
----
{
"job_id":"farequote",
"processed_record_count":86275,
"processed_field_count":172550,
"input_bytes":8678202,
"input_field_count":258825,
"job_id":"it_ops_new_kpi",
"processed_record_count":21435,
"processed_field_count":64305,
"input_bytes":2589063,
"input_field_count":85740,
"invalid_date_count":0,
"missing_field_count":0,
"out_of_order_timestamp_count":0,
"empty_bucket_count":0,
"empty_bucket_count":16,
"sparse_bucket_count":0,
"bucket_count":1440,
"earliest_record_timestamp":1454803200000,
"latest_record_timestamp":1455235196000,
"last_data_time":1491436182038,
"input_record_count":86275
"bucket_count":2165,
"earliest_record_timestamp":1454020569000,
"latest_record_timestamp":1455318669000,
"last_data_time":1491952300658,
"latest_empty_bucket_timestamp":1454541600000,
"input_record_count":21435
}
----

9
docs/en/rest-api/ml/preview-datafeed.asciidoc
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@ -1,7 +1,8 @@
//lcawley: Verified example output 2017-04-11
[[ml-preview-datafeed]]
==== Preview Data Feeds
The preview data feed API allows you to preview a data feed.
The preview data feed API enables you to preview a data feed.
===== Request
@ -10,8 +11,7 @@ The preview data feed API allows you to preview a data feed.
===== Description
TBD
//How much data does it return?
//TBD: How much data does it return?
The API returns example data by using the current data feed settings.
===== Path Parameters
@ -52,14 +52,17 @@ The data that is returned for this example is as follows:
[
{
"@timestamp": 1454803200000,
"airline": "AAL",
"responsetime": 132.20460510253906
},
{
"@timestamp": 1454803200000,
"airline": "JZA",
"responsetime": 990.4628295898438
},
{
"@timestamp": 1454803200000,
"airline": "JBU",
"responsetime": 877.5927124023438
},
...

60
docs/en/rest-api/ml/put-datafeed.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-put-datafeed]]
==== Create Data Feeds
@ -7,7 +8,6 @@ The create data feed API enables you to instantiate a data feed.
`PUT _xpack/ml/datafeeds/<feed_id>`
===== Description
You must create a job before you create a data feed. You can associate only one
@ -20,36 +20,37 @@ data feed to each job.
===== Request Body
aggregations::
(+object+) TBD. For example: {"@timestamp": {"histogram": {"field": "@timestamp",
"interval": 30000,"offset": 0,"order": {"_key": "asc"},"keyed": false,
"min_doc_count": 0}, "aggregations": {"events_per_min": {"sum": {
"field": "events_per_min"}}}}}
`aggregations`::
(+object+) TBD.
frequency::
`chunking_config`::
(+object+) TBD.
For example: {"mode": "manual", "time_span": "30000000ms"}
`frequency`::
TBD: For example: "150s"
indexes (required)::
`indexes` (required)::
(+array+) An array of index names. For example: ["it_ops_metrics"]
job_id (required)::
`job_id` (required)::
(+string+) A numerical character string that uniquely identifies the job.
query::
`query`::
(+object+) The query that retrieves the data.
By default, this property has the following value: `{"match_all": {"boost": 1}}`.
query_delay::
`query_delay`::
TBD. For example: "60s"
scroll_size::
`scroll_size`::
TBD. For example, 1000
types (required)::
`types` (required)::
TBD. For example: ["network","sql","kpi"]
For more information about these properties,
see <<ml-datafeed-resource, Data Feed Resources>>.
see <<ml-datafeed-resource, Data Feed Resources>>.
////
===== Responses
@ -72,21 +73,13 @@ The following example creates the `datafeed-it-ops-kpi` data feed:
PUT _xpack/ml/datafeeds/datafeed-it-ops-kpi
{
"job_id": "it-ops-kpi",
"query":
{
"match_all":
{
"boost": 1
}
},
"indexes": [
"it_ops_metrics"
],
"types": [
"kpi",
"sql",
"network"
]
"indexes": ["it_ops_metrics"],
"types": ["kpi","network","sql"],
"query": {
"match_all": {
"boost": 1
}
}
}
--------------------------------------------------
// CONSOLE
@ -103,14 +96,17 @@ When the data feed is created, you receive the following results:
],
"types": [
"kpi",
"sql",
"network"
"network",
"sql"
],
"query": {
"match_all": {
"boost": 1
}
},
"scroll_size": 1000
"scroll_size": 1000,
"chunking_config": {
"mode": "auto"
}
}
----

29
docs/en/rest-api/ml/put-job.asciidoc
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@ -1,7 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-put-job]]
==== Create Jobs
The create job API enables you to instantiate a {ml} job.
The create job API enables you to instantiate a job.
===== Request
@ -10,7 +11,6 @@ The create job API enables you to instantiate a {ml} job.
////
===== Description
TBD
////
===== Path Parameters
@ -20,23 +20,28 @@ TBD
===== Request Body
`description`::
(+string+) An optional description of the job.
`analysis_config`::
(+object+) The analysis configuration, which specifies how to analyze the data.
See <<ml-analysisconfig, analysis configuration objects>>.
`analysis_limits`::
Optionally specifies runtime limits for the job. See <<ml-apilimits,analysis limits>>.
`data_description`::
(+object+) Describes the format of the input data.
See <<ml-datadescription,data description objects>>.
`analysis_limits`::
Optionally specifies runtime limits for the job. See <<ml-apilimits,analysis limits>>.
`description`::
(+string+) An optional description of the job.
`model_snapshot_retention_days`::
(+long+) The time in days that model snapshots are retained for the job.
Older snapshots are deleted. The default value is 1 day.
`results_index_name`::
(+string+) TBD. For example, `shared`.
////
This expects data to be sent in JSON format using the POST `_data` API.
===== Responses
TBD
@ -83,8 +88,9 @@ When the job is created, you receive the following results:
----
{
"job_id": "it-ops-kpi",
"job_type": "anomaly_detector",
"description": "First simple job",
"create_time": 1491247016391,
"create_time": 1491948238874,
"analysis_config": {
"bucket_span": "5m",
"latency": "0ms",
@ -96,8 +102,7 @@ When the job is created, you receive the following results:
"detector_rules": []
}
],
"influencers": [],
"use_per_partition_normalization": false
"influencers": []
},
"data_description": {
"time_field": "@timestamp",

70
docs/en/rest-api/ml/resultsresource.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-results-resource]]
==== Results Resources
@ -63,10 +64,9 @@ A record object has the following properties:
`detector_index`::
(+number+) A unique identifier for the detector.
//`fieldName`::
// TBD: This field did not appear in my results, but it might be a valid property.
// (+string+) Certain functions require a field to operate on. For those functions,
// this is the name of the field to be analyzed.
`field_name`::
(+string+) Certain functions require a field to operate on.
For those functions, this is the name of the field to be analyzed.
`function`::
(+string+) The function in which the anomaly occurs.
@ -90,8 +90,9 @@ A record object has the following properties:
`job_id`::
(+string+) A numerical character string that uniquely identifies the job.
`kpi_indicator`::
(++) TBD. For example, ["online_purchases"]
//`kpi_indicator`::
// (++) TBD. For example, ["online_purchases"]
// I did not receive this in later tests. Is it still valid?
`partition_field_name`::
(+string+) The name of the partition field that was used in the analysis, if
@ -154,9 +155,6 @@ An influencer object has the following properties:
// Same as for buckets? i.e. (+unsigned integer+) The length of the bucket in seconds.
// This value is equal to the `bucket_span` value in the job configuration.
`job_id`::
(+string+) A numerical character string that uniquely identifies the job.
`influencer_score`::
(+number+) An anomaly score for the influencer in this bucket time interval.
The score is calculated based upon a sophisticated aggregation of the anomalies
@ -176,6 +174,9 @@ An influencer object has the following properties:
(+boolean+) If true, then this is an interim result.
In other words, it is calculated based on partial input data.
`job_id`::
(+string+) A numerical character string that uniquely identifies the job.
`kpi_indicator`::
(++) TBD. For example, "online_purchases".
@ -188,7 +189,54 @@ An influencer object has the following properties:
`result_type`::
(++) TBD. For example, "influencer".
//TBD: How is this different from the "bucket_influencer" type?
`sequence_num`::
(++) TBD. For example, 2.
`timestamp`::
(+date+) Influencers are produced in buckets. This value is the start time
of the bucket, specified in ISO 8601 format. For example, 1454943900000.
An bucket influencer object has the same following properties:
`anomaly_score`::
(+number+) TBD
//It is unclear how this differs from the influencer_score.
//An anomaly score for the influencer in this bucket time interval.
//The score is calculated based upon a sophisticated aggregation of the anomalies
//in the bucket for this entity. For example: 94.1386.
`bucket_span`::
(++) TBD. For example, 300.
////
// Same as for buckets? i.e. (+unsigned integer+) The length of the bucket in seconds.
// This value is equal to the `bucket_span` value in the job configuration.
////
`initial_anomaly_score`::
(++) TBD. For example, 83.3831.
`influencer_field_name`::
(+string+) The field name of the influencer.
`is_interim`::
(+boolean+) If true, then this is an interim result.
In other words, it is calculated based on partial input data.
`job_id`::
(+string+) A numerical character string that uniquely identifies the job.
`probability`::
(+number+) The probability that the influencer has this behavior.
This value is in the range 0 to 1. For example, 0.0000109783.
// For example, 0.03 means 3%. This value is held to a high precision of over
//300 decimal places. In scientific notation, a value of 3.24E-300 is highly
//unlikely and therefore highly anomalous.
`raw_anomaly_score`::
(++) TBD. For example, 2.32119.
`result_type`::
(++) TBD. For example, "bucket_influencer".
`sequence_num`::
(++) TBD. For example, 2.
@ -227,7 +275,7 @@ A bucket resource has the following properties:
`bucket_influencers`::
(+array+) An array of influencer objects.
For more information, see <<ml-results-influencers,influencers>>.
For more information, see <<ml-results-influencers,Influencers>>.
`bucket_span`::
(+unsigned integer+) The length of the bucket in seconds. This value is

3
docs/en/rest-api/ml/revert-snapshot.asciidoc
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@ -1,7 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-revert-snapshot]]
==== Revert Model Snapshots
The revert model snapshot API allows you to revert to a specific snapshot.
The revert model snapshot API enables you to revert to a specific snapshot.
===== Request

5
docs/en/rest-api/ml/snapshotresource.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-snapshot-resource]]
==== Model Snapshot Resources
@ -31,7 +32,7 @@ A model snapshot resource has the following properties:
(++) TBD. For example: 1455229800000.
`model_size_stats`::
(+object+) TBD.
(+object+) TBD. See <<ml-snapshot-stats,Model Size Statistics>>.
`retain`::
(+boolean+) TBD. For example: false.
@ -47,6 +48,8 @@ A model snapshot resource has the following properties:
(+date+) The creation timestamp for the snapshot, specified in ISO 8601 format.
For example: 1491852978000.
[float]
[[ml-snapshot-stats]]
===== Model Size Statistics
The `model_size_stats` object has the following properties:

32
docs/en/rest-api/ml/start-datafeed.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04
[[ml-start-datafeed]]
==== Start Data Feeds
@ -10,6 +11,9 @@ A data feed can be opened and closed multiple times throughout its lifecycle.
===== Description
NOTE: Before you can start a data feed, the job must be open. Otherwise, an error
occurs.
When you start a data feed, you can specify a start time. This allows you to
include a training period, providing you have this data available in {es}.
If you want to analyze from the beginning of a dataset, you can specify any date
@ -23,6 +27,19 @@ job analyzes data from the start time until the end time, at which point the
analysis stops. This scenario is useful for a one-off batch analysis. If you
do not specify an end time, the data feed runs continuously.
The `start` and `end` times can be specified by using one of the
following formats: +
- ISO 8601 format with milliseconds, for example `2017-01-22T06:00:00.000Z`
- ISO 8601 format without milliseconds, for example `2017-01-22T06:00:00+00:00`
- Seconds from the Epoch, for example `1390370400`
Date-time arguments using either of the ISO 8601 formats must have a time zone
designator, where Z is accepted as an abbreviation for UTC time.
NOTE: When a URL is expected (for example, in browsers), the `+` used in time
zone designators must be encoded as `%2B`.
If the system restarts, any jobs that had data feeds running are also restarted.
When a stopped data feed is restarted, it continues processing input data from
@ -33,9 +50,6 @@ because the job might not have completely processed all data for that millisecon
If you specify a `start` value that is earlier than the timestamp of the latest
processed record, that value is ignored.
NOTE: Before you can start a data feed, the job must be open. Otherwise, an error
occurs.
===== Path Parameters
`feed_id` (required)::
@ -51,18 +65,6 @@ occurs.
(+string+) The time that the data feed should begin. This value is inclusive.
The default value is an empty string.
These `start` and `end` times can be specified by using one of the
following formats:
* ISO 8601 format with milliseconds, for example `2017-01-22T06:00:00.000Z`
* ISO 8601 format without milliseconds, for example `2017-01-22T06:00:00+00:00`
* Seconds from the Epoch, for example `1390370400`
NOTE: When a URL is expected (for example, in browsers), the `+`` used in time
zone designators has to be encoded as `%2B`.
Date-time arguments using either of the ISO 8601 formats must have a time zone
designator, where Z is accepted as an abbreviation for UTC time.
`timeout`::
(+time+) Controls the amount of time to wait until a data feed starts.
The default value is 20 seconds.

2
docs/en/rest-api/ml/stop-datafeed.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-stop-datafeed]]
==== Stop Data Feeds
@ -11,7 +12,6 @@ A data feed can be opened and closed multiple times throughout its lifecycle.
////
===== Description
TBD
////
===== Path Parameters

97
docs/en/rest-api/ml/update-datafeed.asciidoc
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@ -1,7 +1,8 @@
//lcawley Verified example output 2017-04
[[ml-update-datafeed]]
==== Update Data Feeds
The update data feed API allows you to update certain properties of a data feed.
The update data feed API enables you to update certain properties of a data feed.
===== Request
@ -10,8 +11,6 @@ The update data feed API allows you to update certain properties of a data feed.
////
===== Description
TBD
////
===== Path Parameters
@ -22,29 +21,33 @@ TBD
The following properties can be updated after the data feed is created:
aggregations::
`aggregations`::
(+object+) TBD.
frequency::
`chunking_config`::
(+object+) TBD.
For example: {"mode": "manual", "time_span": "30000000ms"}
`frequency`::
TBD: For example: "150s"
indexes (required)::
`indexes` (required)::
(+array+) An array of index names. For example: ["it_ops_metrics"]
job_id::
`job_id`::
(+string+) A numerical character string that uniquely identifies the job.
query::
`query`::
(+object+) The query that retrieves the data.
By default, this property has the following value: `{"match_all": {"boost": 1}}`.
query_delay::
`query_delay`::
TBD. For example: "60s"
scroll_size::
`scroll_size`::
TBD. For example, 1000
types (required)::
`types` (required)::
TBD. For example: ["network","sql","kpi"]
For more information about these properties,
@ -68,30 +71,41 @@ The following example updates the `it-ops-kpi` job:
[source,js]
--------------------------------------------------
POST _xpack/ml/datafeeds/datafeed-it-ops-kpi3/_update
POST _xpack/ml/datafeeds/datafeed-it-ops-kpi/_update
{
"query_delay": "60s",
"frequency": "150s",
"aggregations": {
"@timestamp": {
"histogram": {
"field": "@timestamp",
"interval": 30000,
"offset": 0,
"order": {
"_key": "asc"
},
"keyed": false,
"min_doc_count": 0
},
"aggregations": {
"events_per_min": {
"sum": {
"field": "events_per_min"
"buckets": {
"date_histogram": {
"field": "@timestamp",
"interval": 30000,
"offset": 0,
"order": {
"_key": "asc"
},
"keyed": false,
"min_doc_count": 0
},
"aggregations": {
"events_per_min": {
"sum": {
"field": "events_per_min"
}
},
"@timestamp": {
"max": {
"field": "@timestamp"
}
}
}
}
}
}
},
"frequency": "160s"
},
"scroll_size": 1000,
"chunking_config": {
"mode": "manual",
"time_span": "30000000ms"
}
}
--------------------------------------------------
// CONSOLE
@ -102,12 +116,12 @@ When the data feed is updated, you receive the following results:
{
"datafeed_id": "datafeed-it-ops-kpi",
"job_id": "it-ops-kpi",
"query_delay": "1m",
"frequency": "160s",
...
"query_delay": "60s",
"frequency": "150s",
...
"aggregations": {
"@timestamp": {
"histogram": {
"buckets": {
"date_histogram": {
"field": "@timestamp",
"interval": 30000,
"offset": 0,
@ -122,10 +136,19 @@ When the data feed is updated, you receive the following results:
"sum": {
"field": "events_per_min"
}
},
"@timestamp": {
"max": {
"field": "@timestamp"
}
}
}
}
},
"scroll_size": 1000
"scroll_size": 1000,
"chunking_config": {
"mode": "manual",
"time_span": "30000000ms"
}
}
----

5
docs/en/rest-api/ml/update-job.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-update-job]]
==== Update Jobs
@ -59,7 +60,7 @@ The following example updates the `it-ops-kpi` job:
[source,js]
--------------------------------------------------
PUT _xpack/ml/anomaly_detectors/it-ops-kpi/_update
POST _xpack/ml/anomaly_detectors/it-ops-kpi/_update
{
"description":"New description",
"analysis_limits":{
@ -74,10 +75,12 @@ When the job is updated, you receive the following results:
----
{
"job_id": "it-ops-kpi",
"job_type": "anomaly_detector",
"description": "New description",
...
"analysis_limits": {
"model_memory_limit": 8192
},
...
}
----

3
docs/en/rest-api/ml/update-snapshot.asciidoc
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@ -1,7 +1,8 @@
//lcawley Verified example output 2017-04-11
[[ml-update-snapshot]]
==== Update Model Snapshots
The update model snapshot API allows you to update certain properties of a snapshot.
The update model snapshot API enables you to update certain properties of a snapshot.
===== Request

12
docs/en/rest-api/ml/validate-detector.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-valid-detector]]
==== Validate Detectors
@ -9,14 +10,11 @@ The validate detectors API validates detector configuration information.
===== Description
TBD
This API enables you validate the detector configuration before you create a job.
////
===== Path Parameters
`job_id` (required)::
(+string+) Identifier for the job
////
===== Request Body
@ -41,9 +39,9 @@ The following example validates detector configuration information:
--------------------------------------------------
POST _xpack/ml/anomaly_detectors/_validate/detector
{
"function":"metric",
"field_name":"responsetime",
"by_field_name":"airline"
"function": "metric",
"field_name": "responsetime",
"by_field_name": "airline"
}
--------------------------------------------------
// CONSOLE

32
docs/en/rest-api/ml/validate-job.asciidoc
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@ -1,3 +1,4 @@
//lcawley Verified example output 2017-04-11
[[ml-valid-job]]
==== Validate Jobs
@ -9,29 +10,16 @@ The validate jobs API validates job configuration information.
===== Description
TBD
This API enables you validate the job configuration before you create the job.
////
===== Path Parameters
`job_id` (required)::
(+string+) Identifier for the job
////
===== Request Body
`description`::
(+string+) An optional description of the job.
`analysis_config`::
(+object+) The analysis configuration, which specifies how to analyze the data.
See <<ml-analysisconfig, analysis configuration objects>>.
`data_description`::
(+object+) Describes the format of the input data.
See <<ml-datadescription,data description objects>>.
`analysis_limits`::
Optionally specifies runtime limits for the job. See <<ml-apilimits,analysis limits>>.
For a list of the properties that you can specify in the body of this API,
see <<ml-job-resource,Job Resources>>.
////
===== Responses
@ -56,14 +44,14 @@ POST _xpack/ml/anomaly_detectors/_validate
"bucket_span": "300S",
"detectors" :[
{
"function":"metric",
"field_name":"responsetime",
"by_field_name":"airline"}],
"influencers" : [ "airline" ]
"function": "metric",
"field_name": "responsetime",
"by_field_name": "airline"}],
"influencers": [ "airline" ]
},
"data_description" : {
"time_field":"time",
"time_format":"yyyy-MM-dd'T'HH:mm:ssX"
"time_field": "time",
"time_format": "yyyy-MM-dd'T'HH:mm:ssX"
}
}
--------------------------------------------------