Preview a datafeed
Generally available
All methods and paths for this operation:
This API returns the first "page" of search results from a datafeed. You can preview an existing datafeed or provide configuration details for a datafeed and anomaly detection job in the API. The preview shows the structure of the data that will be passed to the anomaly detection engine. IMPORTANT: When Elasticsearch security features are enabled, the preview uses the credentials of the user that called the API. However, when the datafeed starts it uses the roles of the last user that created or updated the datafeed. To get a preview that accurately reflects the behavior of the datafeed, use the appropriate credentials. You can also use secondary authorization headers to supply the credentials.
Required authorization
- Index privileges:
read
- Cluster privileges:
manage_ml
Path parameters
-
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. NOTE: If you use this path parameter, you cannot provide datafeed or anomaly detection job configuration details in the request body.
Query parameters
-
The start time from where the datafeed preview should begin
-
The end time when the datafeed preview should stop
Body
-
The datafeed definition to preview.
Hide datafeed_config attributes Show datafeed_config attributes object
-
If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data.
-
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.
Hide chunking_config attributes Show chunking_config attributes object
-
If the mode is
auto
, the chunk size is dynamically calculated; this is the recommended value when the datafeed does not use aggregations. If the mode ismanual
, chunking is applied according to the specifiedtime_span
; use this mode when the datafeed uses aggregations. If the mode isoff
, no chunking is applied.Values are
auto
,manual
, oroff
. -
The time span that each search will be querying. This setting is applicable only when the
mode
is set tomanual
.
-
-
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. The default value is the job identifier.
-
Specifies whether the datafeed checks for missing data and the size of the window. 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. This check runs only on real-time datafeeds.Hide delayed_data_check_config attributes Show delayed_data_check_config attributes object
-
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 defaultcheck_window
span calculation is based on the maximum of2h
or8 * bucket_span
. -
Specifies whether the datafeed periodically checks for delayed data.
-
-
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
. Whenfrequency
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. -
An array of index names. Wildcards are supported. If any indices are in remote clusters, the machine learning nodes must have the
remote_cluster_client
role. -
Specifies index expansion options that are used during search.
Hide indices_options attributes Show indices_options attributes object
-
If false, the request returns an error if any wildcard expression, index alias, or
_all
value targets only missing or closed indices. This behavior applies even if the request targets other open indices. For example, a request targetingfoo*,bar*
returns an error if an index starts withfoo
but no index starts withbar
. -
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
.Supported values include:
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 withopen
,closed
, orboth
.none
: Wildcard expressions are not accepted.
-
If true, missing or closed indices are not included in the response.
Default value is
false
. -
If true, concrete, expanded or aliased indices are ignored when frozen.
Default value is
true
.
-
-
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
timesmax_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. -
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.
External documentation -
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
and120s
. This randomness improves the query performance when there are multiple jobs running on the same node. -
Specifies runtime fields for the datafeed search.
Hide runtime_mappings attribute Show runtime_mappings attribute object
-
Hide * attributes Show * attributes object
-
For type
composite
-
For type
lookup
-
A custom format for
date
type runtime fields. -
For type
lookup
-
For type
lookup
-
For type
lookup
-
Painless script executed at query time.
-
Field type, which can be:
boolean
,composite
,date
,double
,geo_point
,ip
,keyword
,long
, orlookup
.Values are
boolean
,composite
,date
,double
,geo_point
,geo_shape
,ip
,keyword
,long
, orlookup
.
-
-
-
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.
Hide script_fields attribute Show script_fields attribute object
-
Hide * attributes Show * attributes object
-
-
The size parameter that is used in Elasticsearch searches when the datafeed does not use aggregations. The maximum value is the value of
index.max_result_window
, which is 10,000 by default.Default value is
1000
.
-
-
The configuration details for the anomaly detection job that is associated with the datafeed. If the
datafeed_config
object does not include ajob_id
that references an existing anomaly detection job, you must supply thisjob_config
object. If you include both ajob_id
and ajob_config
, the latter information is used. You cannot specify ajob_config
object unless you also supply adatafeed_config
object.Hide job_config attributes Show job_config attributes object
-
Advanced configuration option. Specifies whether this job can open when there is insufficient machine learning node capacity for it to be immediately assigned to a node.
Default value is
false
. -
The analysis configuration, which specifies how to analyze the data. After you create a job, you cannot change the analysis configuration; all the properties are informational.
Hide analysis_config attributes Show analysis_config attributes object
-
The size of the interval that the analysis is aggregated into, typically between
5m
and1h
. This value should be either a whole number of days or equate to a whole number of buckets in one day. If the anomaly detection job uses a datafeed with aggregations, this value must also be divisible by the interval of the date histogram aggregation. categorization_analyzer
string | object If
categorization_field_name
is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time ascategorization_filters
. The categorization analyzer specifies how thecategorization_field
is interpreted by the categorization process. Thecategorization_analyzer
field can be specified either as a string or as an object. If it is a string, it must refer to a built-in analyzer or one added by another plugin.One of: If
categorization_field_name
is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time ascategorization_filters
. The categorization analyzer specifies how thecategorization_field
is interpreted by the categorization process. Thecategorization_analyzer
field can be specified either as a string or as an object. If it is a string, it must refer to a built-in analyzer or one added by another plugin.If
categorization_field_name
is specified, you can also define the analyzer that is used to interpret the categorization field. This property cannot be used at the same time ascategorization_filters
. The categorization analyzer specifies how thecategorization_field
is interpreted by the categorization process. Thecategorization_analyzer
field can be specified either as a string or as an object. If it is a string, it must refer to a built-in analyzer or one added by another plugin.-
If this property is specified, the values of the specified field will be categorized. The resulting categories must be used in a detector by setting
by_field_name
,over_field_name
, orpartition_field_name
to the keywordmlcategory
. -
If
categorization_field_name
is specified, you can also define optional filters. This property expects an array of regular expressions. The expressions are used to filter out matching sequences from the categorization field values. You can use this functionality to fine tune the categorization by excluding sequences from consideration when categories are defined. For example, you can exclude SQL statements that appear in your log files. This property cannot be used at the same time ascategorization_analyzer
. If you only want to define simple regular expression filters that are applied prior to tokenization, setting this property is the easiest method. If you also want to customize the tokenizer or post-tokenization filtering, use thecategorization_analyzer
property instead and include the filters as pattern_replace character filters. The effect is exactly the same. -
Detector configuration objects specify which data fields a job analyzes. They also specify which analytical functions are used. You can specify multiple detectors for a job. If the detectors array does not contain at least one detector, no analysis can occur and an error is returned.
Hide detectors attributes Show detectors attributes object
-
Custom rules enable you to customize the way detectors operate. For example, a rule may dictate conditions under which results should be skipped. Kibana refers to custom rules as job rules.
-
A description of the detector.
-
A unique identifier for the detector. This identifier is based on the order of the detectors in the
analysis_config
, starting at zero. If you specify a value for this property, it is ignored. -
The analysis function that is used. For example,
count
,rare
,mean
,min
,max
, orsum
. -
Defines whether a new series is used as the null series when there is no value for the by or partition fields.
Default value is
false
.
-
A comma separated list of influencer field names. Typically these can be the by, over, or partition fields that are used in the detector configuration. You might also want to use a field name that is not specifically named in a detector, but is available as part of the input data. When you use multiple detectors, the use of influencers is recommended as it aggregates results for each influencer entity.
-
The size of the window in which to expect data that is out of time order. If you specify a non-zero value, it must be greater than or equal to one second. NOTE: Latency is applicable only when you send data by using the post data API.
-
Advanced configuration option. Affects the pruning of models that have not been updated for the given time duration. The value must be set to a multiple of the
bucket_span
. If set too low, important information may be removed from the model. For jobs created in 8.1 and later, the default value is the greater of30d
or 20 timesbucket_span
. -
This functionality is reserved for internal use. It is not supported for use in customer environments and is not subject to the support SLA of official GA features. If set to
true
, the analysis will automatically find correlations between metrics for a given by field value and report anomalies when those correlations cease to hold. For example, suppose CPU and memory usage on host A is usually highly correlated with the same metrics on host B. Perhaps this correlation occurs because they are running a load-balanced application. If you enable this property, anomalies will be reported when, for example, CPU usage on host A is high and the value of CPU usage on host B is low. That is to say, you’ll see an anomaly when the CPU of host A is unusual given the CPU of host B. To use themultivariate_by_fields
property, you must also specifyby_field_name
in your detector. -
Settings related to how categorization interacts with partition fields.
Hide per_partition_categorization attributes Show per_partition_categorization attributes object
-
To enable this setting, you must also set the
partition_field_name
property to the same value in every detector that uses the keywordmlcategory
. Otherwise, job creation fails. -
This setting can be set to true only if per-partition categorization is enabled. If true, both categorization and subsequent anomaly detection stops for partitions where the categorization status changes to warn. This setting makes it viable to have a job where it is expected that categorization works well for some partitions but not others; you do not pay the cost of bad categorization forever in the partitions where it works badly.
-
-
If this property is specified, the data that is fed to the job is expected to be pre-summarized. This property value is the name of the field that contains the count of raw data points that have been summarized. The same
summary_count_field_name
applies to all detectors in the job. NOTE: Thesummary_count_field_name
property cannot be used with themetric
function.
-
-
Limits can be applied for the resources required to hold the mathematical models in memory. These limits are approximate and can be set per job. They do not control the memory used by other processes, for example the Elasticsearch Java processes.
Hide analysis_limits attributes Show analysis_limits attributes object
-
The maximum number of examples stored per category in memory and in the results data store. If you increase this value, more examples are available, however it requires that you have more storage available. If you set this value to 0, no examples are stored. NOTE: The
categorization_examples_limit
applies only to analysis that uses categorization.Default value is
4
. model_memory_limit
number | string The approximate maximum amount of memory resources that are required for analytical processing. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled. If the
xpack.ml.max_model_memory_limit
setting has a value greater than 0 and less than 1024mb, that value is used instead of the default. The default value is relatively small to ensure that high resource usage is a conscious decision. If you have jobs that are expected to analyze high cardinality fields, you will likely need to use a higher value. If you specify a number instead of a string, the units are assumed to be MiB. Specifying a string is recommended for clarity. If you specify a byte size unit ofb
orkb
and the number does not equate to a discrete number of megabytes, it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you specify a value less than 1 MiB, an error occurs. If you specify a value for thexpack.ml.max_model_memory_limit
setting, an error occurs when you try to create jobs that havemodel_memory_limit
values greater than that setting value.One of: The approximate maximum amount of memory resources that are required for analytical processing. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled. If the
xpack.ml.max_model_memory_limit
setting has a value greater than 0 and less than 1024mb, that value is used instead of the default. The default value is relatively small to ensure that high resource usage is a conscious decision. If you have jobs that are expected to analyze high cardinality fields, you will likely need to use a higher value. If you specify a number instead of a string, the units are assumed to be MiB. Specifying a string is recommended for clarity. If you specify a byte size unit ofb
orkb
and the number does not equate to a discrete number of megabytes, it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you specify a value less than 1 MiB, an error occurs. If you specify a value for thexpack.ml.max_model_memory_limit
setting, an error occurs when you try to create jobs that havemodel_memory_limit
values greater than that setting value.The approximate maximum amount of memory resources that are required for analytical processing. Once this limit is approached, data pruning becomes more aggressive. Upon exceeding this limit, new entities are not modeled. If the
xpack.ml.max_model_memory_limit
setting has a value greater than 0 and less than 1024mb, that value is used instead of the default. The default value is relatively small to ensure that high resource usage is a conscious decision. If you have jobs that are expected to analyze high cardinality fields, you will likely need to use a higher value. If you specify a number instead of a string, the units are assumed to be MiB. Specifying a string is recommended for clarity. If you specify a byte size unit ofb
orkb
and the number does not equate to a discrete number of megabytes, it is rounded down to the closest MiB. The minimum valid value is 1 MiB. If you specify a value less than 1 MiB, an error occurs. If you specify a value for thexpack.ml.max_model_memory_limit
setting, an error occurs when you try to create jobs that havemodel_memory_limit
values greater than that setting value.
-
-
Advanced configuration option. The time between each periodic persistence of the model. The default value is a randomized value between 3 to 4 hours, which avoids all jobs persisting at exactly the same time. The smallest allowed value is 1 hour.
-
Advanced configuration option. Contains custom metadata about the job.
-
Advanced configuration option, which affects the automatic removal of old model snapshots for this job. It specifies a period of time (in days) after which only the first snapshot per day is retained. This period is relative to the timestamp of the most recent snapshot for this job.
Default value is
1
. -
The data description defines the format of the input data when you send data to the job by using the post data API. Note that when configure a datafeed, these properties are automatically set.
Hide data_description attributes Show data_description attributes object
-
Only JSON format is supported at this time.
-
The name of the field that contains the timestamp.
-
The time format, which can be
epoch
,epoch_ms
, or a custom pattern. The valueepoch
refers to UNIX or Epoch time (the number of seconds since 1 Jan 1970). The valueepoch_ms
indicates that time is measured in milliseconds since the epoch. Theepoch
andepoch_ms
time formats accept either integer or real values. Custom patterns must conform to the Java DateTimeFormatter class. When you use date-time formatting patterns, it is recommended that you provide the full date, time and time zone. For example:yyyy-MM-dd'T'HH:mm:ssX
. If the pattern that you specify is not sufficient to produce a complete timestamp, job creation fails.Default value is
epoch
.
-
-
The datafeed, which retrieves data from Elasticsearch for analysis by the job. You can associate only one datafeed with each anomaly detection job.
Hide datafeed_config attributes Show datafeed_config attributes object
-
If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data.
-
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.
-
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. The default value is the job identifier.
-
Specifies whether the datafeed checks for missing data and the size of the window. 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. This check runs only on real-time datafeeds. -
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
. Whenfrequency
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. -
An array of index names. Wildcards are supported. If any indices are in remote clusters, the machine learning nodes must have the
remote_cluster_client
role. -
Specifies index expansion options that are used during search.
Hide indices_options attributes Show indices_options attributes object
-
If false, the request returns an error if any wildcard expression, index alias, or
_all
value targets only missing or closed indices. This behavior applies even if the request targets other open indices. For example, a request targetingfoo*,bar*
returns an error if an index starts withfoo
but no index starts withbar
. -
If true, missing or closed indices are not included in the response.
Default value is
false
. -
If true, concrete, expanded or aliased indices are ignored when frozen.
Default value is
true
.
-
-
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
timesmax_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. -
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.
External documentation -
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
and120s
. This randomness improves the query performance when there are multiple jobs running on the same node. -
Specifies runtime fields for the datafeed search.
-
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.
-
The size parameter that is used in Elasticsearch searches when the datafeed does not use aggregations. The maximum value is the value of
index.max_result_window
, which is 10,000 by default.Default value is
1000
.
-
-
A description of the job.
-
A list of job groups. A job can belong to no groups or many.
-
Identifier for the anomaly detection job. This identifier can contain lowercase alphanumeric characters (a-z and 0-9), hyphens, and underscores. It must start and end with alphanumeric characters.
-
Reserved for future use, currently set to
anomaly_detector
. -
This advanced configuration option stores model information along with the results. It provides a more detailed view into anomaly detection. Model plot provides a simplified and indicative view of the model and its bounds.
Hide model_plot_config attributes Show model_plot_config attributes object
-
If true, enables calculation and storage of the model change annotations for each entity that is being analyzed.
Default value is
true
. -
If true, enables calculation and storage of the model bounds for each entity that is being analyzed.
Default value is
false
. -
Limits data collection to this comma separated list of partition or by field values. If terms are not specified or it is an empty string, no filtering is applied. Wildcards are not supported. Only the specified terms can be viewed when using the Single Metric Viewer.
-
-
Advanced configuration option, which affects the automatic removal of old model snapshots for this job. It specifies the maximum period of time (in days) that snapshots are retained. This period is relative to the timestamp of the most recent snapshot for this job. The default value is
10
, which means snapshots ten days older than the newest snapshot are deleted.Default value is
10
. -
Advanced configuration option. The period over which adjustments to the score are applied, as new data is seen. The default value is the longer of 30 days or 100
bucket_spans
. -
A text string that affects the name of the machine learning results index. The default value is
shared
, which generates an index named.ml-anomalies-shared
. -
Advanced configuration option. The period of time (in days) that results are retained. Age is calculated relative to the timestamp of the latest bucket result. If this property has a non-null value, once per day at 00:30 (server time), results that are the specified number of days older than the latest bucket result are deleted from Elasticsearch. The default value is null, which means all results are retained. Annotations generated by the system also count as results for retention purposes; they are deleted after the same number of days as results. Annotations added by users are retained forever.
-
GET _ml/datafeeds/datafeed-high_sum_total_sales/_preview
resp = client.ml.preview_datafeed(
datafeed_id="datafeed-high_sum_total_sales",
)
const response = await client.ml.previewDatafeed({
datafeed_id: "datafeed-high_sum_total_sales",
});
response = client.ml.preview_datafeed(
datafeed_id: "datafeed-high_sum_total_sales"
)
$resp = $client->ml()->previewDatafeed([
"datafeed_id" => "datafeed-high_sum_total_sales",
]);
curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/datafeeds/datafeed-high_sum_total_sales/_preview"
client.ml().previewDatafeed(p -> p
.datafeedId("datafeed-high_sum_total_sales")
);