http://api.example.comThis documentation is derived from the main branch of the elasticsearch-specification repository. It is provided under license Attribution-NonCommercial-NoDerivatives 4.0 International.
Last update on Jul 9, 2025.
This API is provided under license Apache 2.0.
Elasticsearch APIs use key-based authentication. You must create an API key and use the encoded value in the request header. For example:
curl -X GET "${ES_URL}/_cat/indices?v=true" \
-H "Authorization: ApiKey ${API_KEY}"
For more information about where to find API keys for the Elasticsearch endpoint (${ES_URL}) for a project, go to Get started with Elasticsearch Serverless.
All methods and paths for this operation:
GET _application/analytics/my*
resp = client.search_application.get_behavioral_analytics(
name="my*",
)const response = await client.searchApplication.getBehavioralAnalytics({
name: "my*",
});response = client.search_application.get_behavioral_analytics(
name: "my*"
)$resp = $client->searchApplication()->getBehavioralAnalytics([
"name" => "my*",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_application/analytics/my*"client.searchApplication().getBehavioralAnalytics(g -> g
.name("my*")
);
{
"my_analytics_collection": {
"event_data_stream": {
"name": "behavioral_analytics-events-my_analytics_collection"
}
},
"my_analytics_collection2": {
"event_data_stream": {
"name": "behavioral_analytics-events-my_analytics_collection2"
}
}
}PUT _application/analytics/my_analytics_collection
resp = client.search_application.put_behavioral_analytics(
name="my_analytics_collection",
)const response = await client.searchApplication.putBehavioralAnalytics({
name: "my_analytics_collection",
});response = client.search_application.put_behavioral_analytics(
name: "my_analytics_collection"
)$resp = $client->searchApplication()->putBehavioralAnalytics([
"name" => "my_analytics_collection",
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_application/analytics/my_analytics_collection"client.searchApplication().putBehavioralAnalytics(p -> p
.name("my_analytics_collection")
);
DELETE _application/analytics/my_analytics_collection/
resp = client.search_application.delete_behavioral_analytics(
name="my_analytics_collection",
)const response = await client.searchApplication.deleteBehavioralAnalytics({
name: "my_analytics_collection",
});response = client.search_application.delete_behavioral_analytics(
name: "my_analytics_collection"
)$resp = $client->searchApplication()->deleteBehavioralAnalytics([
"name" => "my_analytics_collection",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_application/analytics/my_analytics_collection/"client.searchApplication().deleteBehavioralAnalytics(d -> d
.name("my_analytics_collection")
);
The compact and aligned text (CAT) APIs aim are intended only for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, it's recommend to use a corresponding JSON API.
All the cat commands accept a query string parameter help to see all the headers and info they provide, and the /_cat command alone lists all the available commands.
All methods and paths for this operation:
Get the cluster's index aliases, including filter and routing information. This API does not return data stream aliases.
IMPORTANT: CAT APIs are only intended for human consumption using the command line or the Kibana console. They are not intended for use by applications. For application consumption, use the aliases API.
view_index_metadataA comma-separated list of aliases to retrieve. Supports wildcards (*). To retrieve all aliases, omit this parameter or use * or _all.
List of columns to appear in the response. Supports simple wildcards.
List of columns that determine how the table should be sorted.
Sorting defaults to ascending and can be changed by setting :asc
or :desc as a suffix to the column name.
The 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.
It 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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
The period to wait for a connection to the master node.
If the master node is not available before the timeout expires, the request fails and returns an error.
To indicated that the request should never timeout, you can set it to -1.
Values are -1 or 0.
GET _cat/aliases?format=json&v=true
resp = client.cat.aliases(
format="json",
v=True,
)const response = await client.cat.aliases({
format: "json",
v: "true",
});response = client.cat.aliases(
format: "json",
v: "true"
)$resp = $client->cat()->aliases([
"format" => "json",
"v" => "true",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/aliases?format=json&v=true"client.cat().aliases();
[
{
"alias": "alias1",
"index": "test1",
"filter": "-",
"routing.index": "-",
"routing.search": "-",
"is_write_index": "true"
},
{
"alias": "alias1",
"index": "test1",
"filter": "*",
"routing.index": "-",
"routing.search": "-",
"is_write_index": "true"
},
{
"alias": "alias3",
"index": "test1",
"filter": "-",
"routing.index": "1",
"routing.search": "1",
"is_write_index": "true"
},
{
"alias": "alias4",
"index": "test1",
"filter": "-",
"routing.index": "2",
"routing.search": "1,2",
"is_write_index": "true"
}
]All methods and paths for this operation:
Get information about component templates in a cluster. Component templates are building blocks for constructing index templates that specify index mappings, settings, and aliases.
IMPORTANT: CAT APIs are only intended for human consumption using the command line or Kibana console. They are not intended for use by applications. For application consumption, use the get component template API.
monitorThe name of the component template. It accepts wildcard expressions. If it is omitted, all component templates are returned.
List of columns to appear in the response. Supports simple wildcards.
List of columns that determine how the table should be sorted.
Sorting defaults to ascending and can be changed by setting :asc
or :desc as a suffix to the column name.
If true, the request computes the list of selected nodes from the
local cluster state. If false the list of selected nodes are computed
from the cluster state of the master node. In both cases the coordinating
node will send requests for further information to each selected node.
The period to wait for a connection to the master node.
Values are -1 or 0.
GET _cat/component_templates/my-template-*?v=true&s=name&format=json
resp = client.cat.component_templates(
name="my-template-*",
v=True,
s="name",
format="json",
)const response = await client.cat.componentTemplates({
name: "my-template-*",
v: "true",
s: "name",
format: "json",
});response = client.cat.component_templates(
name: "my-template-*",
v: "true",
s: "name",
format: "json"
)$resp = $client->cat()->componentTemplates([
"name" => "my-template-*",
"v" => "true",
"s" => "name",
"format" => "json",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/component_templates/my-template-*?v=true&s=name&format=json"client.cat().componentTemplates();
[
{
"name": "my-template-1",
"version": "null",
"alias_count": "0",
"mapping_count": "0",
"settings_count": "1",
"metadata_count": "0",
"included_in": "[my-index-template]"
},
{
"name": "my-template-2",
"version": null,
"alias_count": "0",
"mapping_count": "3",
"settings_count": "0",
"metadata_count": "0",
"included_in": "[my-index-template]"
}
]All methods and paths for this operation:
Get quick access to a document count for a data stream, an index, or an entire cluster. The document count only includes live documents, not deleted documents which have not yet been removed by the merge process.
IMPORTANT: CAT APIs are only intended for human consumption using the command line or Kibana console. They are not intended for use by applications. For application consumption, use the count API.
readA comma-separated list of data streams, indices, and aliases used to limit the request.
It supports wildcards (*).
To target all data streams and indices, omit this parameter or use * or _all.
GET /_cat/count/my-index-000001?v=true&format=json
resp = client.cat.count(
index="my-index-000001",
v=True,
format="json",
)const response = await client.cat.count({
index: "my-index-000001",
v: "true",
format: "json",
});response = client.cat.count(
index: "my-index-000001",
v: "true",
format: "json"
)$resp = $client->cat()->count([
"index" => "my-index-000001",
"v" => "true",
"format" => "json",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/count/my-index-000001?v=true&format=json"client.cat().count();
[
{
"epoch": "1475868259",
"timestamp": "15:24:20",
"count": "120"
}
][
{
"epoch": "1475868259",
"timestamp": "15:24:20",
"count": "121"
}
]curl \
--request GET 'http://api.example.com/_cat' \
--header "Authorization: $API_KEY"All methods and paths for this operation:
Get high-level information about indices in a cluster, including backing indices for data streams.
Use this request to get the following information for each index in a cluster:
These metrics are retrieved directly from Lucene, which Elasticsearch uses internally to power indexing and search. As a result, all document counts include hidden nested documents. To get an accurate count of Elasticsearch documents, use the cat count or count APIs.
CAT APIs are only intended for human consumption using the command line or Kibana console. They are not intended for use by applications. For application consumption, use an index endpoint.
monitormonitorComma-separated list of data streams, indices, and aliases used to limit the request.
Supports wildcards (*). To target all data streams and indices, omit this parameter or use * or _all.
The unit used to display byte values.
Values are b, kb, mb, gb, tb, or pb.
The type of index that wildcard patterns can match.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
The health status used to limit returned indices. By default, the response includes indices of any health status.
Supported values include:
green (or GREEN): All shards are assigned.yellow (or YELLOW): All primary shards are assigned, but one or more replica shards are unassigned. If a node in the cluster fails, some data could be unavailable until that node is repaired.red (or RED): One or more primary shards are unassigned, so some data is unavailable. This can occur briefly during cluster startup as primary shards are assigned.Values are green, GREEN, yellow, YELLOW, red, or RED.
If true, the response includes information from segments that are not loaded into memory.
If true, the response only includes information from primary shards.
The unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
Period to wait for a connection to the master node.
Values are -1 or 0.
List of columns to appear in the response. Supports simple wildcards.
List of columns that determine how the table should be sorted.
Sorting defaults to ascending and can be changed by setting :asc
or :desc as a suffix to the column name.
GET /_cat/indices/my-index-*?v=true&s=index&format=json
resp = client.cat.indices(
index="my-index-*",
v=True,
s="index",
format="json",
)const response = await client.cat.indices({
index: "my-index-*",
v: "true",
s: "index",
format: "json",
});response = client.cat.indices(
index: "my-index-*",
v: "true",
s: "index",
format: "json"
)$resp = $client->cat()->indices([
"index" => "my-index-*",
"v" => "true",
"s" => "index",
"format" => "json",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/indices/my-index-*?v=true&s=index&format=json"client.cat().indices();
[
{
"health": "yellow",
"status": "open",
"index": "my-index-000001",
"uuid": "u8FNjxh8Rfy_awN11oDKYQ",
"pri": "1",
"rep": "1",
"docs.count": "1200",
"docs.deleted": "0",
"store.size": "88.1kb",
"pri.store.size": "88.1kb",
"dataset.size": "88.1kb"
},
{
"health": "green",
"status": "open",
"index": "my-index-000002",
"uuid": "nYFWZEO7TUiOjLQXBaYJpA ",
"pri": "1",
"rep": "0",
"docs.count": "0",
"docs.deleted": "0",
"store.size": "260b",
"pri.store.size": "260b",
"dataset.size": "260b"
}
]All methods and paths for this operation:
Get configuration and usage information about data frame analytics jobs.
IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get data frame analytics jobs statistics API.
monitor_mlWhether to ignore if a wildcard expression matches no configs. (This includes _all string or when no configs have been specified)
The unit in which to display byte values
Values are b, kb, mb, gb, tb, or pb.
Comma-separated list of column names to display.
Supported values include:
assignment_explanation (or ae): Contains messages relating to the selection of a node.create_time (or ct, createTime): The time when the data frame analytics job was created.description (or d): A description of a job.dest_index (or di, destIndex): Name of the destination index.failure_reason (or fr, failureReason): Contains messages about the reason why a data frame analytics job failed.id: Identifier for the data frame analytics job.model_memory_limit (or mml, modelMemoryLimit): The approximate maximum amount of memory resources that are permitted for
the data frame analytics job.node.address (or na, nodeAddress): The network address of the node that the data frame analytics job is
assigned to.node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that the data frame analytics job is assigned
to.node.id (or ni, nodeId): The unique identifier of the node that the data frame analytics job is
assigned to.node.name (or nn, nodeName): The name of the node that the data frame analytics job is assigned to.progress (or p): The progress report of the data frame analytics job by phase.source_index (or si, sourceIndex): Name of the source index.state (or s): Current state of the data frame analytics job.type (or t): The type of analysis that the data frame analytics job performs.version (or v): The Elasticsearch version number in which the data frame analytics job was
created.Comma-separated list of column names or column aliases used to sort the response.
Supported values include:
assignment_explanation (or ae): Contains messages relating to the selection of a node.create_time (or ct, createTime): The time when the data frame analytics job was created.description (or d): A description of a job.dest_index (or di, destIndex): Name of the destination index.failure_reason (or fr, failureReason): Contains messages about the reason why a data frame analytics job failed.id: Identifier for the data frame analytics job.model_memory_limit (or mml, modelMemoryLimit): The approximate maximum amount of memory resources that are permitted for
the data frame analytics job.node.address (or na, nodeAddress): The network address of the node that the data frame analytics job is
assigned to.node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that the data frame analytics job is assigned
to.node.id (or ni, nodeId): The unique identifier of the node that the data frame analytics job is
assigned to.node.name (or nn, nodeName): The name of the node that the data frame analytics job is assigned to.progress (or p): The progress report of the data frame analytics job by phase.source_index (or si, sourceIndex): Name of the source index.state (or s): Current state of the data frame analytics job.type (or t): The type of analysis that the data frame analytics job performs.version (or v): The Elasticsearch version number in which the data frame analytics job was
created.Unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
GET _cat/ml/data_frame/analytics?v=true&format=json
resp = client.cat.ml_data_frame_analytics(
v=True,
format="json",
)const response = await client.cat.mlDataFrameAnalytics({
v: "true",
format: "json",
});response = client.cat.ml_data_frame_analytics(
v: "true",
format: "json"
)$resp = $client->cat()->mlDataFrameAnalytics([
"v" => "true",
"format" => "json",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/ml/data_frame/analytics?v=true&format=json"client.cat().mlDataFrameAnalytics();
[
{
"id": "classifier_job_1",
"type": "classification",
"create_time": "2020-02-12T11:49:09.594Z",
"state": "stopped"
},
{
"id": "classifier_job_2",
"type": "classification",
"create_time": "2020-02-12T11:49:14.479Z",
"state": "stopped"
},
{
"id": "classifier_job_3",
"type": "classification",
"create_time": "2020-02-12T11:49:16.928Z",
"state": "stopped"
},
{
"id": "classifier_job_4",
"type": "classification",
"create_time": "2020-02-12T11:49:19.127Z",
"state": "stopped"
},
{
"id": "classifier_job_5",
"type": "classification",
"create_time": "2020-02-12T11:49:21.349Z",
"state": "stopped"
}
]All methods and paths for this operation:
Get configuration and usage information about datafeeds.
This API returns a maximum of 10,000 datafeeds.
If the Elasticsearch security features are enabled, you must have monitor_ml, monitor, manage_ml, or manage
cluster privileges to use this API.
IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get datafeed statistics API.
monitor_mlSpecifies what to do when the request:
_all string or no identifiers and there are no matches.If true, the API returns an empty datafeeds array when there are no matches and the subset of results when
there are partial matches. If false, the API returns a 404 status code when there are no matches or only
partial matches.
Comma-separated list of column names to display.
Supported values include:
ae (or assignment_explanation): For started datafeeds only, contains messages relating to the selection of
a node.bc (or buckets.count, bucketsCount): The number of buckets processed.id: A numerical character string that uniquely identifies the datafeed.na (or node.address, nodeAddress): For started datafeeds only, the network address of the node where the
datafeed is started.ne (or node.ephemeral_id, nodeEphemeralId): For started datafeeds only, the ephemeral ID of the node where the
datafeed is started.ni (or node.id, nodeId): For started datafeeds only, the unique identifier of the node where the
datafeed is started.nn (or node.name, nodeName): For started datafeeds only, the name of the node where the datafeed is
started.sba (or search.bucket_avg, searchBucketAvg): The average search time per bucket, in milliseconds.sc (or search.count, searchCount): The number of searches run by the datafeed.seah (or search.exp_avg_hour, searchExpAvgHour): The exponential average search time per hour, in milliseconds.st (or search.time, searchTime): The total time the datafeed spent searching, in milliseconds.s (or state): The status of the datafeed: starting, started, stopping, or stopped.
If starting, the datafeed has been requested to start but has not yet
started. If started, the datafeed is actively receiving data. If
stopping, the datafeed has been requested to stop gracefully and is
completing its final action. If stopped, the datafeed is stopped and will
not receive data until it is re-started.Comma-separated list of column names or column aliases used to sort the response.
Supported values include:
ae (or assignment_explanation): For started datafeeds only, contains messages relating to the selection of
a node.bc (or buckets.count, bucketsCount): The number of buckets processed.id: A numerical character string that uniquely identifies the datafeed.na (or node.address, nodeAddress): For started datafeeds only, the network address of the node where the
datafeed is started.ne (or node.ephemeral_id, nodeEphemeralId): For started datafeeds only, the ephemeral ID of the node where the
datafeed is started.ni (or node.id, nodeId): For started datafeeds only, the unique identifier of the node where the
datafeed is started.nn (or node.name, nodeName): For started datafeeds only, the name of the node where the datafeed is
started.sba (or search.bucket_avg, searchBucketAvg): The average search time per bucket, in milliseconds.sc (or search.count, searchCount): The number of searches run by the datafeed.seah (or search.exp_avg_hour, searchExpAvgHour): The exponential average search time per hour, in milliseconds.st (or search.time, searchTime): The total time the datafeed spent searching, in milliseconds.s (or state): The status of the datafeed: starting, started, stopping, or stopped.
If starting, the datafeed has been requested to start but has not yet
started. If started, the datafeed is actively receiving data. If
stopping, the datafeed has been requested to stop gracefully and is
completing its final action. If stopped, the datafeed is stopped and will
not receive data until it is re-started.The unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
GET _cat/ml/datafeeds?v=true&format=json
resp = client.cat.ml_datafeeds(
v=True,
format="json",
)const response = await client.cat.mlDatafeeds({
v: "true",
format: "json",
});response = client.cat.ml_datafeeds(
v: "true",
format: "json"
)$resp = $client->cat()->mlDatafeeds([
"v" => "true",
"format" => "json",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/ml/datafeeds?v=true&format=json"client.cat().mlDatafeeds();
[
{
"id": "datafeed-high_sum_total_sales",
"state": "stopped",
"buckets.count": "743",
"search.count": "7"
},
{
"id": "datafeed-low_request_rate",
"state": "stopped",
"buckets.count": "1457",
"search.count": "3"
},
{
"id": "datafeed-response_code_rates",
"state": "stopped",
"buckets.count": "1460",
"search.count": "18"
},
{
"id": "datafeed-url_scanning",
"state": "stopped",
"buckets.count": "1460",
"search.count": "18"
}
]All methods and paths for this operation:
Get configuration and usage information for anomaly detection jobs.
This API returns a maximum of 10,000 jobs.
If the Elasticsearch security features are enabled, you must have monitor_ml,
monitor, manage_ml, or manage cluster privileges to use this API.
IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get anomaly detection job statistics API.
monitor_mlSpecifies what to do when the request:
_all string or no identifiers and there are no matches.If true, the API returns an empty jobs array when there are no matches and the subset of results when there
are partial matches. If false, the API returns a 404 status code when there are no matches or only partial
matches.
The unit used to display byte values.
Values are b, kb, mb, gb, tb, or pb.
Comma-separated list of column names to display.
Supported values include:
assignment_explanation (or ae): For open anomaly detection jobs only, contains messages relating to the
selection of a node to run the job.buckets.count (or bc, bucketsCount): The number of bucket results produced by the job.buckets.time.exp_avg (or btea, bucketsTimeExpAvg): Exponential moving average of all bucket processing times, in milliseconds.buckets.time.exp_avg_hour (or bteah, bucketsTimeExpAvgHour): Exponentially-weighted moving average of bucket processing times calculated
in a 1 hour time window, in milliseconds.buckets.time.max (or btmax, bucketsTimeMax): Maximum among all bucket processing times, in milliseconds.buckets.time.min (or btmin, bucketsTimeMin): Minimum among all bucket processing times, in milliseconds.buckets.time.total (or btt, bucketsTimeTotal): Sum of all bucket processing times, in milliseconds.data.buckets (or db, dataBuckets): The number of buckets processed.data.earliest_record (or der, dataEarliestRecord): The timestamp of the earliest chronologically input document.data.empty_buckets (or deb, dataEmptyBuckets): The number of buckets which did not contain any data.data.input_bytes (or dib, dataInputBytes): The number of bytes of input data posted to the anomaly detection job.data.input_fields (or dif, dataInputFields): The total number of fields in input documents posted to the anomaly
detection job. This count includes fields that are not used in the analysis.
However, be aware that if you are using a datafeed, it extracts only the
required fields from the documents it retrieves before posting them to the job.data.input_records (or dir, dataInputRecords): The number of input documents posted to the anomaly detection job.data.invalid_dates (or did, dataInvalidDates): The number of input documents with either a missing date field or a date
that could not be parsed.data.last (or dl, dataLast): The timestamp at which data was last analyzed, according to server time.data.last_empty_bucket (or dleb, dataLastEmptyBucket): The timestamp of the last bucket that did not contain any data.data.last_sparse_bucket (or dlsb, dataLastSparseBucket): The timestamp of the last bucket that was considered sparse.data.latest_record (or dlr, dataLatestRecord): The timestamp of the latest chronologically input document.data.missing_fields (or dmf, dataMissingFields): The number of input documents that are missing a field that the anomaly
detection job is configured to analyze. Input documents with missing fields
are still processed because it is possible that not all fields are missing.data.out_of_order_timestamps (or doot, dataOutOfOrderTimestamps): The number of input documents that have a timestamp chronologically
preceding the start of the current anomaly detection bucket offset by the
latency window. This information is applicable only when you provide data
to the anomaly detection job by using the post data API. These out of order
documents are discarded, since jobs require time series data to be in
ascending chronological order.data.processed_fields (or dpf, dataProcessedFields): The total number of fields in all the documents that have been processed by
the anomaly detection job. Only fields that are specified in the detector
configuration object contribute to this count. The timestamp is not
included in this count.data.processed_records (or dpr, dataProcessedRecords): The number of input documents that have been processed by the anomaly
detection job. This value includes documents with missing fields, since
they are nonetheless analyzed. If you use datafeeds and have aggregations
in your search query, the processed record count is the number of
aggregation results processed, not the number of Elasticsearch documents.data.sparse_buckets (or dsb, dataSparseBuckets): The number of buckets that contained few data points compared to the
expected number of data points.forecasts.memory.avg (or fmavg, forecastsMemoryAvg): The average memory usage in bytes for forecasts related to the anomaly
detection job.forecasts.memory.max (or fmmax, forecastsMemoryMax): The maximum memory usage in bytes for forecasts related to the anomaly
detection job.forecasts.memory.min (or fmmin, forecastsMemoryMin): The minimum memory usage in bytes for forecasts related to the anomaly
detection job.forecasts.memory.total (or fmt, forecastsMemoryTotal): The total memory usage in bytes for forecasts related to the anomaly
detection job.forecasts.records.avg (or fravg, forecastsRecordsAvg): The average number of model_forecast` documents written for forecasts
related to the anomaly detection job.forecasts.records.max (or frmax, forecastsRecordsMax): The maximum number of model_forecast documents written for forecasts
related to the anomaly detection job.forecasts.records.min (or frmin, forecastsRecordsMin): The minimum number of model_forecast documents written for forecasts
related to the anomaly detection job.forecasts.records.total (or frt, forecastsRecordsTotal): The total number of model_forecast documents written for forecasts
related to the anomaly detection job.forecasts.time.avg (or ftavg, forecastsTimeAvg): The average runtime in milliseconds for forecasts related to the anomaly
detection job.forecasts.time.max (or ftmax, forecastsTimeMax): The maximum runtime in milliseconds for forecasts related to the anomaly
detection job.forecasts.time.min (or ftmin, forecastsTimeMin): The minimum runtime in milliseconds for forecasts related to the anomaly
detection job.forecasts.time.total (or ftt, forecastsTimeTotal): The total runtime in milliseconds for forecasts related to the anomaly
detection job.forecasts.total (or ft, forecastsTotal): The number of individual forecasts currently available for the job.id: Identifier for the anomaly detection job.model.bucket_allocation_failures (or mbaf, modelBucketAllocationFailures): The number of buckets for which new entities in incoming data were not
processed due to insufficient model memory.model.by_fields (or mbf, modelByFields): The number of by field values that were analyzed by the models. This value
is cumulative for all detectors in the job.model.bytes (or mb, modelBytes): The number of bytes of memory used by the models. This is the maximum value
since the last time the model was persisted. If the job is closed, this
value indicates the latest size.model.bytes_exceeded (or mbe, modelBytesExceeded): The number of bytes over the high limit for memory usage at the last
allocation failure.model.categorization_status (or mcs, modelCategorizationStatus): The status of categorization for the job: ok or warn. If ok,
categorization is performing acceptably well (or not being used at all). If
warn, categorization is detecting a distribution of categories that
suggests the input data is inappropriate for categorization. Problems could
be that there is only one category, more than 90% of categories are rare,
the number of categories is greater than 50% of the number of categorized
documents, there are no frequently matched categories, or more than 50% of
categories are dead.model.categorized_doc_count (or mcdc, modelCategorizedDocCount): The number of documents that have had a field categorized.model.dead_category_count (or mdcc, modelDeadCategoryCount): The number of categories created by categorization that will never be
assigned again because another category’s definition makes it a superset of
the dead category. Dead categories are a side effect of the way
categorization has no prior training.model.failed_category_count (or mdcc, modelFailedCategoryCount): The number of times that categorization wanted to create a new category but
couldn’t because the job had hit its model memory limit. This count does
not track which specific categories failed to be created. Therefore, you
cannot use this value to determine the number of unique categories that
were missed.model.frequent_category_count (or mfcc, modelFrequentCategoryCount): The number of categories that match more than 1% of categorized documents.model.log_time (or mlt, modelLogTime): The timestamp when the model stats were gathered, according to server time.model.memory_limit (or mml, modelMemoryLimit): The timestamp when the model stats were gathered, according to server time.model.memory_status (or mms, modelMemoryStatus): The status of the mathematical models: ok, soft_limit, or hard_limit.
If ok, the models stayed below the configured value. If soft_limit, the
models used more than 60% of the configured memory limit and older unused
models will be pruned to free up space. Additionally, in categorization jobs
no further category examples will be stored. If hard_limit, the models
used more space than the configured memory limit. As a result, not all
incoming data was processed.model.over_fields (or mof, modelOverFields): The number of over field values that were analyzed by the models. This
value is cumulative for all detectors in the job.model.partition_fields (or mpf, modelPartitionFields): The number of partition field values that were analyzed by the models. This
value is cumulative for all detectors in the job.model.rare_category_count (or mrcc, modelRareCategoryCount): The number of categories that match just one categorized document.model.timestamp (or mt, modelTimestamp): The timestamp of the last record when the model stats were gathered.model.total_category_count (or mtcc, modelTotalCategoryCount): The number of categories created by categorization.node.address (or na, nodeAddress): The network address of the node that runs the job. This information is
available only for open jobs.node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that runs the job. This information is
available only for open jobs.node.id (or ni, nodeId): The unique identifier of the node that runs the job. This information is
available only for open jobs.node.name (or nn, nodeName): The name of the node that runs the job. This information is available only
for open jobs.opened_time (or ot): For open jobs only, the elapsed time for which the job has been open.state (or s): The status of the anomaly detection job: closed, closing, failed,
opened, or opening. If closed, the job finished successfully with its
model state persisted. The job must be opened before it can accept further
data. If closing, the job close action is in progress and has not yet
completed. A closing job cannot accept further data. If failed, the job
did not finish successfully due to an error. This situation can occur due
to invalid input data, a fatal error occurring during the analysis, or an
external interaction such as the process being killed by the Linux out of
memory (OOM) killer. If the job had irrevocably failed, it must be force
closed and then deleted. If the datafeed can be corrected, the job can be
closed and then re-opened. If opened, the job is available to receive and
process data. If opening, the job open action is in progress and has not
yet completed.Comma-separated list of column names or column aliases used to sort the response.
Supported values include:
assignment_explanation (or ae): For open anomaly detection jobs only, contains messages relating to the
selection of a node to run the job.buckets.count (or bc, bucketsCount): The number of bucket results produced by the job.buckets.time.exp_avg (or btea, bucketsTimeExpAvg): Exponential moving average of all bucket processing times, in milliseconds.buckets.time.exp_avg_hour (or bteah, bucketsTimeExpAvgHour): Exponentially-weighted moving average of bucket processing times calculated
in a 1 hour time window, in milliseconds.buckets.time.max (or btmax, bucketsTimeMax): Maximum among all bucket processing times, in milliseconds.buckets.time.min (or btmin, bucketsTimeMin): Minimum among all bucket processing times, in milliseconds.buckets.time.total (or btt, bucketsTimeTotal): Sum of all bucket processing times, in milliseconds.data.buckets (or db, dataBuckets): The number of buckets processed.data.earliest_record (or der, dataEarliestRecord): The timestamp of the earliest chronologically input document.data.empty_buckets (or deb, dataEmptyBuckets): The number of buckets which did not contain any data.data.input_bytes (or dib, dataInputBytes): The number of bytes of input data posted to the anomaly detection job.data.input_fields (or dif, dataInputFields): The total number of fields in input documents posted to the anomaly
detection job. This count includes fields that are not used in the analysis.
However, be aware that if you are using a datafeed, it extracts only the
required fields from the documents it retrieves before posting them to the job.data.input_records (or dir, dataInputRecords): The number of input documents posted to the anomaly detection job.data.invalid_dates (or did, dataInvalidDates): The number of input documents with either a missing date field or a date
that could not be parsed.data.last (or dl, dataLast): The timestamp at which data was last analyzed, according to server time.data.last_empty_bucket (or dleb, dataLastEmptyBucket): The timestamp of the last bucket that did not contain any data.data.last_sparse_bucket (or dlsb, dataLastSparseBucket): The timestamp of the last bucket that was considered sparse.data.latest_record (or dlr, dataLatestRecord): The timestamp of the latest chronologically input document.data.missing_fields (or dmf, dataMissingFields): The number of input documents that are missing a field that the anomaly
detection job is configured to analyze. Input documents with missing fields
are still processed because it is possible that not all fields are missing.data.out_of_order_timestamps (or doot, dataOutOfOrderTimestamps): The number of input documents that have a timestamp chronologically
preceding the start of the current anomaly detection bucket offset by the
latency window. This information is applicable only when you provide data
to the anomaly detection job by using the post data API. These out of order
documents are discarded, since jobs require time series data to be in
ascending chronological order.data.processed_fields (or dpf, dataProcessedFields): The total number of fields in all the documents that have been processed by
the anomaly detection job. Only fields that are specified in the detector
configuration object contribute to this count. The timestamp is not
included in this count.data.processed_records (or dpr, dataProcessedRecords): The number of input documents that have been processed by the anomaly
detection job. This value includes documents with missing fields, since
they are nonetheless analyzed. If you use datafeeds and have aggregations
in your search query, the processed record count is the number of
aggregation results processed, not the number of Elasticsearch documents.data.sparse_buckets (or dsb, dataSparseBuckets): The number of buckets that contained few data points compared to the
expected number of data points.forecasts.memory.avg (or fmavg, forecastsMemoryAvg): The average memory usage in bytes for forecasts related to the anomaly
detection job.forecasts.memory.max (or fmmax, forecastsMemoryMax): The maximum memory usage in bytes for forecasts related to the anomaly
detection job.forecasts.memory.min (or fmmin, forecastsMemoryMin): The minimum memory usage in bytes for forecasts related to the anomaly
detection job.forecasts.memory.total (or fmt, forecastsMemoryTotal): The total memory usage in bytes for forecasts related to the anomaly
detection job.forecasts.records.avg (or fravg, forecastsRecordsAvg): The average number of model_forecast` documents written for forecasts
related to the anomaly detection job.forecasts.records.max (or frmax, forecastsRecordsMax): The maximum number of model_forecast documents written for forecasts
related to the anomaly detection job.forecasts.records.min (or frmin, forecastsRecordsMin): The minimum number of model_forecast documents written for forecasts
related to the anomaly detection job.forecasts.records.total (or frt, forecastsRecordsTotal): The total number of model_forecast documents written for forecasts
related to the anomaly detection job.forecasts.time.avg (or ftavg, forecastsTimeAvg): The average runtime in milliseconds for forecasts related to the anomaly
detection job.forecasts.time.max (or ftmax, forecastsTimeMax): The maximum runtime in milliseconds for forecasts related to the anomaly
detection job.forecasts.time.min (or ftmin, forecastsTimeMin): The minimum runtime in milliseconds for forecasts related to the anomaly
detection job.forecasts.time.total (or ftt, forecastsTimeTotal): The total runtime in milliseconds for forecasts related to the anomaly
detection job.forecasts.total (or ft, forecastsTotal): The number of individual forecasts currently available for the job.id: Identifier for the anomaly detection job.model.bucket_allocation_failures (or mbaf, modelBucketAllocationFailures): The number of buckets for which new entities in incoming data were not
processed due to insufficient model memory.model.by_fields (or mbf, modelByFields): The number of by field values that were analyzed by the models. This value
is cumulative for all detectors in the job.model.bytes (or mb, modelBytes): The number of bytes of memory used by the models. This is the maximum value
since the last time the model was persisted. If the job is closed, this
value indicates the latest size.model.bytes_exceeded (or mbe, modelBytesExceeded): The number of bytes over the high limit for memory usage at the last
allocation failure.model.categorization_status (or mcs, modelCategorizationStatus): The status of categorization for the job: ok or warn. If ok,
categorization is performing acceptably well (or not being used at all). If
warn, categorization is detecting a distribution of categories that
suggests the input data is inappropriate for categorization. Problems could
be that there is only one category, more than 90% of categories are rare,
the number of categories is greater than 50% of the number of categorized
documents, there are no frequently matched categories, or more than 50% of
categories are dead.model.categorized_doc_count (or mcdc, modelCategorizedDocCount): The number of documents that have had a field categorized.model.dead_category_count (or mdcc, modelDeadCategoryCount): The number of categories created by categorization that will never be
assigned again because another category’s definition makes it a superset of
the dead category. Dead categories are a side effect of the way
categorization has no prior training.model.failed_category_count (or mdcc, modelFailedCategoryCount): The number of times that categorization wanted to create a new category but
couldn’t because the job had hit its model memory limit. This count does
not track which specific categories failed to be created. Therefore, you
cannot use this value to determine the number of unique categories that
were missed.model.frequent_category_count (or mfcc, modelFrequentCategoryCount): The number of categories that match more than 1% of categorized documents.model.log_time (or mlt, modelLogTime): The timestamp when the model stats were gathered, according to server time.model.memory_limit (or mml, modelMemoryLimit): The timestamp when the model stats were gathered, according to server time.model.memory_status (or mms, modelMemoryStatus): The status of the mathematical models: ok, soft_limit, or hard_limit.
If ok, the models stayed below the configured value. If soft_limit, the
models used more than 60% of the configured memory limit and older unused
models will be pruned to free up space. Additionally, in categorization jobs
no further category examples will be stored. If hard_limit, the models
used more space than the configured memory limit. As a result, not all
incoming data was processed.model.over_fields (or mof, modelOverFields): The number of over field values that were analyzed by the models. This
value is cumulative for all detectors in the job.model.partition_fields (or mpf, modelPartitionFields): The number of partition field values that were analyzed by the models. This
value is cumulative for all detectors in the job.model.rare_category_count (or mrcc, modelRareCategoryCount): The number of categories that match just one categorized document.model.timestamp (or mt, modelTimestamp): The timestamp of the last record when the model stats were gathered.model.total_category_count (or mtcc, modelTotalCategoryCount): The number of categories created by categorization.node.address (or na, nodeAddress): The network address of the node that runs the job. This information is
available only for open jobs.node.ephemeral_id (or ne, nodeEphemeralId): The ephemeral ID of the node that runs the job. This information is
available only for open jobs.node.id (or ni, nodeId): The unique identifier of the node that runs the job. This information is
available only for open jobs.node.name (or nn, nodeName): The name of the node that runs the job. This information is available only
for open jobs.opened_time (or ot): For open jobs only, the elapsed time for which the job has been open.state (or s): The status of the anomaly detection job: closed, closing, failed,
opened, or opening. If closed, the job finished successfully with its
model state persisted. The job must be opened before it can accept further
data. If closing, the job close action is in progress and has not yet
completed. A closing job cannot accept further data. If failed, the job
did not finish successfully due to an error. This situation can occur due
to invalid input data, a fatal error occurring during the analysis, or an
external interaction such as the process being killed by the Linux out of
memory (OOM) killer. If the job had irrevocably failed, it must be force
closed and then deleted. If the datafeed can be corrected, the job can be
closed and then re-opened. If opened, the job is available to receive and
process data. If opening, the job open action is in progress and has not
yet completed.The unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
GET _cat/ml/anomaly_detectors?h=id,s,dpr,mb&v=true&format=json
resp = client.cat.ml_jobs(
h="id,s,dpr,mb",
v=True,
format="json",
)const response = await client.cat.mlJobs({
h: "id,s,dpr,mb",
v: "true",
format: "json",
});response = client.cat.ml_jobs(
h: "id,s,dpr,mb",
v: "true",
format: "json"
)$resp = $client->cat()->mlJobs([
"h" => "id,s,dpr,mb",
"v" => "true",
"format" => "json",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/ml/anomaly_detectors?h=id,s,dpr,mb&v=true&format=json"client.cat().mlJobs();
[
{
"id": "high_sum_total_sales",
"s": "closed",
"dpr": "14022",
"mb": "1.5mb"
},
{
"id": "low_request_rate",
"s": "closed",
"dpr": "1216",
"mb": "40.5kb"
},
{
"id": "response_code_rates",
"s": "closed",
"dpr": "28146",
"mb": "132.7kb"
},
{
"id": "url_scanning",
"s": "closed",
"dpr": "28146",
"mb": "501.6kb"
}
]All methods and paths for this operation:
Get configuration and usage information about inference trained models.
IMPORTANT: CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get trained models statistics API.
monitor_mlSpecifies what to do when the request: contains wildcard expressions and there are no models that match; contains the _all string or no identifiers and there are no matches; contains wildcard expressions and there are only partial matches.
If true, the API returns an empty array when there are no matches and the subset of results when there are partial matches.
If false, the API returns a 404 status code when there are no matches or only partial matches.
The unit used to display byte values.
Values are b, kb, mb, gb, tb, or pb.
A comma-separated list of column names to display.
Supported values include:
create_time (or ct): The time when the trained model was created.created_by (or c, createdBy): Information on the creator of the trained model.data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only
displayed if it is still available.description (or d): The description of the trained model.heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.id: Identifier for the trained model.ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the
trained model.ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained
model.ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.license (or l): The license level of the trained model.operations (or o, modelOperations): The estimated number of operations to use the trained model. This number
helps measuring the computational complexity of the model.version (or v): The Elasticsearch version number in which the trained model was created.A comma-separated list of column names or aliases used to sort the response.
Supported values include:
create_time (or ct): The time when the trained model was created.created_by (or c, createdBy): Information on the creator of the trained model.data_frame_analytics_id (or df, dataFrameAnalytics, dfid): Identifier for the data frame analytics job that created the model. Only
displayed if it is still available.description (or d): The description of the trained model.heap_size (or hs, modelHeapSize): The estimated heap size to keep the trained model in memory.id: Identifier for the trained model.ingest.count (or ic, ingestCount): The total number of documents that are processed by the model.ingest.current (or icurr, ingestCurrent): The total number of document that are currently being handled by the
trained model.ingest.failed (or if, ingestFailed): The total number of failed ingest attempts with the trained model.ingest.pipelines (or ip, ingestPipelines): The total number of ingest pipelines that are referencing the trained
model.ingest.time (or it, ingestTime): The total time that is spent processing documents with the trained model.license (or l): The license level of the trained model.operations (or o, modelOperations): The estimated number of operations to use the trained model. This number
helps measuring the computational complexity of the model.version (or v): The Elasticsearch version number in which the trained model was created.Skips the specified number of transforms.
The maximum number of transforms to display.
Unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
GET _cat/ml/trained_models?v=true&format=json
resp = client.cat.ml_trained_models(
v=True,
format="json",
)const response = await client.cat.mlTrainedModels({
v: "true",
format: "json",
});response = client.cat.ml_trained_models(
v: "true",
format: "json"
)$resp = $client->cat()->mlTrainedModels([
"v" => "true",
"format" => "json",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/ml/trained_models?v=true&format=json"client.cat().mlTrainedModels();
[
{
"id": "ddddd-1580216177138",
"heap_size": "0b",
"operations": "196",
"create_time": "2025-03-25T00:01:38.662Z",
"type": "pytorch",
"ingest.pipelines": "0",
"data_frame.id": "__none__"
},
{
"id": "lang_ident_model_1",
"heap_size": "1mb",
"operations": "39629",
"create_time": "2019-12-05T12:28:34.594Z",
"type": "lang_ident",
"ingest.pipelines": "0",
"data_frame.id": "__none__"
}
]All methods and paths for this operation:
Get configuration and usage information about transforms.
CAT APIs are only intended for human consumption using the Kibana console or command line. They are not intended for use by applications. For application consumption, use the get transform statistics API.
monitor_transformA transform identifier or a wildcard expression. If you do not specify one of these options, the API returns information for all transforms.
Specifies what to do when the request: contains wildcard expressions and there are no transforms that match; contains the _all string or no identifiers and there are no matches; contains wildcard expressions and there are only partial matches.
If true, it returns an empty transforms array when there are no matches and the subset of results when there are partial matches.
If false, the request returns a 404 status code when there are no matches or only partial matches.
Skips the specified number of transforms.
Comma-separated list of column names to display.
Supported values include:
changes_last_detection_time (or cldt): The timestamp when changes were last detected in the source indices.checkpoint (or cp): The sequence number for the checkpoint.checkpoint_duration_time_exp_avg (or cdtea, checkpointTimeExpAvg): Exponential moving average of the duration of the checkpoint, in
milliseconds.checkpoint_progress (or c, checkpointProgress): The progress of the next checkpoint that is currently in progress.create_time (or ct, createTime): The time the transform was created.delete_time (or dtime): The amount of time spent deleting, in milliseconds.description (or d): The description of the transform.dest_index (or di, destIndex): The destination index for the transform. The mappings of the destination
index are deduced based on the source fields when possible. If alternate
mappings are required, use the Create index API prior to starting the
transform.documents_deleted (or docd): The number of documents that have been deleted from the destination index
due to the retention policy for this transform.documents_indexed (or doci): The number of documents that have been indexed into the destination index
for the transform.docs_per_second (or dps): Specifies a limit on the number of input documents per second. This setting
throttles the transform by adding a wait time between search requests. The
default value is null, which disables throttling.documents_processed (or docp): The number of documents that have been processed from the source index of
the transform.frequency (or f): The interval between checks for changes in the source indices when the
transform is running continuously. Also determines the retry interval in
the event of transient failures while the transform is searching or
indexing. The minimum value is 1s and the maximum is 1h. The default
value is 1m.id: Identifier for the transform.index_failure (or if): The number of indexing failures.index_time (or itime): The amount of time spent indexing, in milliseconds.index_total (or it): The number of index operations.indexed_documents_exp_avg (or idea): Exponential moving average of the number of new documents that have been
indexed.last_search_time (or lst, lastSearchTime): The timestamp of the last search in the source indices. This field is only
shown if the transform is running.max_page_search_size (or mpsz): Defines the initial page size to use for the composite aggregation for each
checkpoint. If circuit breaker exceptions occur, the page size is
dynamically adjusted to a lower value. The minimum value is 10 and the
maximum is 65,536. The default value is 500.pages_processed (or pp): The number of search or bulk index operations processed. Documents are
processed in batches instead of individually.pipeline (or p): The unique identifier for an ingest pipeline.processed_documents_exp_avg (or pdea): Exponential moving average of the number of documents that have been
processed.processing_time (or pt): The amount of time spent processing results, in milliseconds.reason (or r): If a transform has a failed state, this property provides details about
the reason for the failure.search_failure (or sf): The number of search failures.search_time (or stime): The amount of time spent searching, in milliseconds.search_total (or st): The number of search operations on the source index for the transform.source_index (or si, sourceIndex): The source indices for the transform. It can be a single index, an index
pattern (for example, "my-index-*"), an array of indices (for example,
["my-index-000001", "my-index-000002"]), or an array of index patterns
(for example, ["my-index-*", "my-other-index-*"]. For remote indices use
the syntax "remote_name:index_name". If any indices are in remote
clusters then the master node and at least one transform node must have the
remote_cluster_client node role.state (or s): The status of the transform, which can be one of the following values:
aborting: The transform is aborting.failed: The transform failed. For more information about the failure,
check the reason field.indexing: The transform is actively processing data and creating new
documents.started: The transform is running but not actively indexing data.stopped: The transform is stopped.stopping: The transform is stopping.transform_type (or tt): Indicates the type of transform: batch or continuous.
trigger_count (or tc): The number of times the transform has been triggered by the scheduler. For
example, the scheduler triggers the transform indexer to check for updates
or ingest new data at an interval specified in the frequency property.
version (or v): The version of Elasticsearch that existed on the node when the transform
was created.
Comma-separated list of column names or column aliases used to sort the response.
Supported values include:
changes_last_detection_time (or cldt): The timestamp when changes were last detected in the source indices.checkpoint (or cp): The sequence number for the checkpoint.checkpoint_duration_time_exp_avg (or cdtea, checkpointTimeExpAvg): Exponential moving average of the duration of the checkpoint, in
milliseconds.checkpoint_progress (or c, checkpointProgress): The progress of the next checkpoint that is currently in progress.create_time (or ct, createTime): The time the transform was created.delete_time (or dtime): The amount of time spent deleting, in milliseconds.description (or d): The description of the transform.dest_index (or di, destIndex): The destination index for the transform. The mappings of the destination
index are deduced based on the source fields when possible. If alternate
mappings are required, use the Create index API prior to starting the
transform.documents_deleted (or docd): The number of documents that have been deleted from the destination index
due to the retention policy for this transform.documents_indexed (or doci): The number of documents that have been indexed into the destination index
for the transform.docs_per_second (or dps): Specifies a limit on the number of input documents per second. This setting
throttles the transform by adding a wait time between search requests. The
default value is null, which disables throttling.documents_processed (or docp): The number of documents that have been processed from the source index of
the transform.frequency (or f): The interval between checks for changes in the source indices when the
transform is running continuously. Also determines the retry interval in
the event of transient failures while the transform is searching or
indexing. The minimum value is 1s and the maximum is 1h. The default
value is 1m.id: Identifier for the transform.index_failure (or if): The number of indexing failures.index_time (or itime): The amount of time spent indexing, in milliseconds.index_total (or it): The number of index operations.indexed_documents_exp_avg (or idea): Exponential moving average of the number of new documents that have been
indexed.last_search_time (or lst, lastSearchTime): The timestamp of the last search in the source indices. This field is only
shown if the transform is running.max_page_search_size (or mpsz): Defines the initial page size to use for the composite aggregation for each
checkpoint. If circuit breaker exceptions occur, the page size is
dynamically adjusted to a lower value. The minimum value is 10 and the
maximum is 65,536. The default value is 500.pages_processed (or pp): The number of search or bulk index operations processed. Documents are
processed in batches instead of individually.pipeline (or p): The unique identifier for an ingest pipeline.processed_documents_exp_avg (or pdea): Exponential moving average of the number of documents that have been
processed.processing_time (or pt): The amount of time spent processing results, in milliseconds.reason (or r): If a transform has a failed state, this property provides details about
the reason for the failure.search_failure (or sf): The number of search failures.search_time (or stime): The amount of time spent searching, in milliseconds.search_total (or st): The number of search operations on the source index for the transform.source_index (or si, sourceIndex): The source indices for the transform. It can be a single index, an index
pattern (for example, "my-index-*"), an array of indices (for example,
["my-index-000001", "my-index-000002"]), or an array of index patterns
(for example, ["my-index-*", "my-other-index-*"]. For remote indices use
the syntax "remote_name:index_name". If any indices are in remote
clusters then the master node and at least one transform node must have the
remote_cluster_client node role.state (or s): The status of the transform, which can be one of the following values:
aborting: The transform is aborting.failed: The transform failed. For more information about the failure,
check the reason field.indexing: The transform is actively processing data and creating new
documents.started: The transform is running but not actively indexing data.stopped: The transform is stopped.stopping: The transform is stopping.transform_type (or tt): Indicates the type of transform: batch or continuous.
trigger_count (or tc): The number of times the transform has been triggered by the scheduler. For
example, the scheduler triggers the transform indexer to check for updates
or ingest new data at an interval specified in the frequency property.
version (or v): The version of Elasticsearch that existed on the node when the transform
was created.
The unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
The maximum number of transforms to obtain.
GET /_cat/transforms?v=true&format=json
resp = client.cat.transforms(
v=True,
format="json",
)const response = await client.cat.transforms({
v: "true",
format: "json",
});response = client.cat.transforms(
v: "true",
format: "json"
)$resp = $client->cat()->transforms([
"v" => "true",
"format" => "json",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_cat/transforms?v=true&format=json"client.cat().transforms();
[
{
"id" : "ecommerce_transform",
"state" : "started",
"checkpoint" : "1",
"documents_processed" : "705",
"checkpoint_progress" : "100.00",
"changes_last_detection_time" : null
}
]Returns basic information about the cluster.
GET /_info/_all
resp = client.cluster.info(
target="_all",
)const response = await client.cluster.info({
target: "_all",
});response = client.cluster.info(
target: "_all"
)$resp = $client->cluster()->info([
"target" => "_all",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_info/_all"client.cluster().info(i -> i
.target("_all")
);
curl \
--request HEAD 'http://api.example.com/' \
--header "Authorization: $API_KEY"The connector and sync jobs APIs provide a convenient way to create and manage Elastic connectors and sync jobs in an internal index.
Connectors are Elasticsearch integrations for syncing content from third-party data sources, which can be deployed on Elastic Cloud or hosted on your own infrastructure.
This API provides an alternative to relying solely on Kibana UI for connector and sync job management. The API comes with a set of validations and assertions to ensure that the state representation in the internal index remains valid.
This API requires the manage_connector privilege or, for read-only endpoints, the monitor_connector privilege.
Update the last_seen field in the connector and set it to the current timestamp.
PUT _connector/my-connector/_check_in
resp = client.connector.check_in(
connector_id="my-connector",
)const response = await client.connector.checkIn({
connector_id: "my-connector",
});response = client.connector.check_in(
connector_id: "my-connector"
)$resp = $client->connector()->checkIn([
"connector_id" => "my-connector",
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector/my-connector/_check_in"client.connector().checkIn(c -> c
.connectorId("my-connector")
);
{
"result": "updated"
}GET _connector/my-connector-id
resp = client.connector.get(
connector_id="my-connector-id",
)const response = await client.connector.get({
connector_id: "my-connector-id",
});response = client.connector.get(
connector_id: "my-connector-id"
)$resp = $client->connector()->get([
"connector_id" => "my-connector-id",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector/my-connector-id"client.connector().get(g -> g
.connectorId("my-connector-id")
);
All methods and paths for this operation:
The unique identifier of the connector to be created or updated. ID is auto-generated if not provided.
PUT _connector/my-connector
{
"index_name": "search-google-drive",
"name": "My Connector",
"service_type": "google_drive"
}resp = client.connector.put(
connector_id="my-connector",
index_name="search-google-drive",
name="My Connector",
service_type="google_drive",
)const response = await client.connector.put({
connector_id: "my-connector",
index_name: "search-google-drive",
name: "My Connector",
service_type: "google_drive",
});response = client.connector.put(
connector_id: "my-connector",
body: {
"index_name": "search-google-drive",
"name": "My Connector",
"service_type": "google_drive"
}
)$resp = $client->connector()->put([
"connector_id" => "my-connector",
"body" => [
"index_name" => "search-google-drive",
"name" => "My Connector",
"service_type" => "google_drive",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"index_name":"search-google-drive","name":"My Connector","service_type":"google_drive"}' "$ELASTICSEARCH_URL/_connector/my-connector"client.connector().put(p -> p
.connectorId("my-connector")
.indexName("search-google-drive")
.name("My Connector")
.serviceType("google_drive")
);
{
"index_name": "search-google-drive",
"name": "My Connector",
"service_type": "google_drive"
}{
"index_name": "search-google-drive",
"name": "My Connector",
"description": "My Connector to sync data to Elastic index from Google Drive",
"service_type": "google_drive",
"language": "english"
}{
"result": "created",
"id": "my-connector"
}Removes a connector and associated sync jobs. This is a destructive action that is not recoverable. NOTE: This action doesn’t delete any API keys, ingest pipelines, or data indices associated with the connector. These need to be removed manually.
DELETE _connector/my-connector-id&delete_sync_jobs=true
resp = client.connector.delete(
connector_id="my-connector-id&delete_sync_jobs=true",
)const response = await client.connector.delete({
connector_id: "my-connector-id&delete_sync_jobs=true",
});response = client.connector.delete(
connector_id: "my-connector-id&delete_sync_jobs=true"
)$resp = $client->connector()->delete([
"connector_id" => "my-connector-id&delete_sync_jobs=true",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector/my-connector-id&delete_sync_jobs=true"client.connector().delete(d -> d
.connectorId("my-connector-id&delete_sync_jobs=true")
);
{
"acknowledged": true
}Starting offset (default: 0)
Specifies a max number of results to get
A comma-separated list of connector index names to fetch connector documents for
A comma-separated list of connector names to fetch connector documents for
A comma-separated list of connector service types to fetch connector documents for
A flag to indicate if the desired connector should be fetched, even if it was soft-deleted.
A wildcard query string that filters connectors with matching name, description or index name
GET _connector
resp = client.connector.list()const response = await client.connector.list();response = client.connector.list$resp = $client->connector()->list();curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector"client.connector().list(l -> l);
Connectors are Elasticsearch integrations that bring content from third-party data sources, which can be deployed on Elastic Cloud or hosted on your own infrastructure. Elastic managed connectors (Native connectors) are a managed service on Elastic Cloud. Self-managed connectors (Connector clients) are self-managed on your infrastructure.
curl \
--request POST 'http://api.example.com/_connector' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '{"description":"string","index_name":"string","is_native":true,"language":"string","name":"string","service_type":"string"}'Cancel a connector sync job, which sets the status to cancelling and updates cancellation_requested_at to the current time.
The connector service is then responsible for setting the status of connector sync jobs to cancelled.
PUT _connector/_sync_job/my-connector-sync-job-id/_cancel
resp = client.connector.sync_job_cancel(
connector_sync_job_id="my-connector-sync-job-id",
)const response = await client.connector.syncJobCancel({
connector_sync_job_id: "my-connector-sync-job-id",
});response = client.connector.sync_job_cancel(
connector_sync_job_id: "my-connector-sync-job-id"
)$resp = $client->connector()->syncJobCancel([
"connector_sync_job_id" => "my-connector-sync-job-id",
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector/_sync_job/my-connector-sync-job-id/_cancel"client.connector().syncJobCancel(s -> s
.connectorSyncJobId("my-connector-sync-job-id")
);
GET _connector/_sync_job/my-connector-sync-job
resp = client.connector.sync_job_get(
connector_sync_job_id="my-connector-sync-job",
)const response = await client.connector.syncJobGet({
connector_sync_job_id: "my-connector-sync-job",
});response = client.connector.sync_job_get(
connector_sync_job_id: "my-connector-sync-job"
)$resp = $client->connector()->syncJobGet([
"connector_sync_job_id" => "my-connector-sync-job",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector/_sync_job/my-connector-sync-job"client.connector().syncJobGet(s -> s
.connectorSyncJobId("my-connector-sync-job")
);
Remove a connector sync job and its associated data. This is a destructive action that is not recoverable.
DELETE _connector/_sync_job/my-connector-sync-job-id
resp = client.connector.sync_job_delete(
connector_sync_job_id="my-connector-sync-job-id",
)const response = await client.connector.syncJobDelete({
connector_sync_job_id: "my-connector-sync-job-id",
});response = client.connector.sync_job_delete(
connector_sync_job_id: "my-connector-sync-job-id"
)$resp = $client->connector()->syncJobDelete([
"connector_sync_job_id" => "my-connector-sync-job-id",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector/_sync_job/my-connector-sync-job-id"client.connector().syncJobDelete(s -> s
.connectorSyncJobId("my-connector-sync-job-id")
);
{
"acknowledged": true
}Get information about all stored connector sync jobs listed by their creation date in ascending order.
Starting offset (default: 0)
Specifies a max number of results to get
A sync job status to fetch connector sync jobs for
Values are canceling, canceled, completed, error, in_progress, pending, or suspended.
A connector id to fetch connector sync jobs for
A comma-separated list of job types to fetch the sync jobs for
Supported values include: full, incremental, access_control
Values are full, incremental, or access_control.
GET _connector/_sync_job?connector_id=my-connector-id&size=1
resp = client.connector.sync_job_list(
connector_id="my-connector-id",
size="1",
)const response = await client.connector.syncJobList({
connector_id: "my-connector-id",
size: 1,
});response = client.connector.sync_job_list(
connector_id: "my-connector-id",
size: "1"
)$resp = $client->connector()->syncJobList([
"connector_id" => "my-connector-id",
"size" => "1",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_connector/_sync_job?connector_id=my-connector-id&size=1"client.connector().syncJobList(s -> s
.connectorId("my-connector-id")
.size(1)
);
Create a connector sync job document in the internal index and initialize its counters and timestamps with default values.
POST _connector/_sync_job
{
"id": "connector-id",
"job_type": "full",
"trigger_method": "on_demand"
}resp = client.connector.sync_job_post(
id="connector-id",
job_type="full",
trigger_method="on_demand",
)const response = await client.connector.syncJobPost({
id: "connector-id",
job_type: "full",
trigger_method: "on_demand",
});response = client.connector.sync_job_post(
body: {
"id": "connector-id",
"job_type": "full",
"trigger_method": "on_demand"
}
)$resp = $client->connector()->syncJobPost([
"body" => [
"id" => "connector-id",
"job_type" => "full",
"trigger_method" => "on_demand",
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"id":"connector-id","job_type":"full","trigger_method":"on_demand"}' "$ELASTICSEARCH_URL/_connector/_sync_job"client.connector().syncJobPost(s -> s
.id("connector-id")
.jobType(SyncJobType.Full)
.triggerMethod(SyncJobTriggerMethod.OnDemand)
);
{
"id": "connector-id",
"job_type": "full",
"trigger_method": "on_demand"
}curl \
--request PUT 'http://api.example.com/_connector/{connector_id}/_filtering/_activate' \
--header "Authorization: $API_KEY"Update the api_key_id and api_key_secret_id fields of a connector.
You can specify the ID of the API key used for authorization and the ID of the connector secret where the API key is stored.
The connector secret ID is required only for Elastic managed (native) connectors.
Self-managed connectors (connector clients) do not use this field.
PUT _connector/my-connector/_api_key_id
{
"api_key_id": "my-api-key-id",
"api_key_secret_id": "my-connector-secret-id"
}resp = client.connector.update_api_key_id(
connector_id="my-connector",
api_key_id="my-api-key-id",
api_key_secret_id="my-connector-secret-id",
)const response = await client.connector.updateApiKeyId({
connector_id: "my-connector",
api_key_id: "my-api-key-id",
api_key_secret_id: "my-connector-secret-id",
});response = client.connector.update_api_key_id(
connector_id: "my-connector",
body: {
"api_key_id": "my-api-key-id",
"api_key_secret_id": "my-connector-secret-id"
}
)$resp = $client->connector()->updateApiKeyId([
"connector_id" => "my-connector",
"body" => [
"api_key_id" => "my-api-key-id",
"api_key_secret_id" => "my-connector-secret-id",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"api_key_id":"my-api-key-id","api_key_secret_id":"my-connector-secret-id"}' "$ELASTICSEARCH_URL/_connector/my-connector/_api_key_id"client.connector().updateApiKeyId(u -> u
.apiKeyId("my-api-key-id")
.apiKeySecretId("my-connector-secret-id")
.connectorId("my-connector")
);
{
"api_key_id": "my-api-key-id",
"api_key_secret_id": "my-connector-secret-id"
}{
"result": "updated"
}Update the configuration field in the connector document.
PUT _connector/my-spo-connector/_configuration
{
"values": {
"tenant_id": "my-tenant-id",
"tenant_name": "my-sharepoint-site",
"client_id": "foo",
"secret_value": "bar",
"site_collections": "*"
}
}resp = client.connector.update_configuration(
connector_id="my-spo-connector",
values={
"tenant_id": "my-tenant-id",
"tenant_name": "my-sharepoint-site",
"client_id": "foo",
"secret_value": "bar",
"site_collections": "*"
},
)const response = await client.connector.updateConfiguration({
connector_id: "my-spo-connector",
values: {
tenant_id: "my-tenant-id",
tenant_name: "my-sharepoint-site",
client_id: "foo",
secret_value: "bar",
site_collections: "*",
},
});response = client.connector.update_configuration(
connector_id: "my-spo-connector",
body: {
"values": {
"tenant_id": "my-tenant-id",
"tenant_name": "my-sharepoint-site",
"client_id": "foo",
"secret_value": "bar",
"site_collections": "*"
}
}
)$resp = $client->connector()->updateConfiguration([
"connector_id" => "my-spo-connector",
"body" => [
"values" => [
"tenant_id" => "my-tenant-id",
"tenant_name" => "my-sharepoint-site",
"client_id" => "foo",
"secret_value" => "bar",
"site_collections" => "*",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"values":{"tenant_id":"my-tenant-id","tenant_name":"my-sharepoint-site","client_id":"foo","secret_value":"bar","site_collections":"*"}}' "$ELASTICSEARCH_URL/_connector/my-spo-connector/_configuration"client.connector().updateConfiguration(u -> u
.connectorId("my-spo-connector")
.values(Map.of("tenant_id", JsonData.fromJson("\"my-tenant-id\""),"tenant_name", JsonData.fromJson("\"my-sharepoint-site\""),"secret_value", JsonData.fromJson("\"bar\""),"client_id", JsonData.fromJson("\"foo\""),"site_collections", JsonData.fromJson("\"*\"")))
);
{
"values": {
"tenant_id": "my-tenant-id",
"tenant_name": "my-sharepoint-site",
"client_id": "foo",
"secret_value": "bar",
"site_collections": "*"
}
}{
"values": {
"secret_value": "foo-bar"
}
}{
"result": "updated"
}Set the error field for the connector. If the error provided in the request body is non-null, the connector’s status is updated to error. Otherwise, if the error is reset to null, the connector status is updated to connected.
PUT _connector/my-connector/_error
{
"error": "Houston, we have a problem!"
}resp = client.connector.update_error(
connector_id="my-connector",
error="Houston, we have a problem!",
)const response = await client.connector.updateError({
connector_id: "my-connector",
error: "Houston, we have a problem!",
});response = client.connector.update_error(
connector_id: "my-connector",
body: {
"error": "Houston, we have a problem!"
}
)$resp = $client->connector()->updateError([
"connector_id" => "my-connector",
"body" => [
"error" => "Houston, we have a problem!",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"error":"Houston, we have a problem!"}' "$ELASTICSEARCH_URL/_connector/my-connector/_error"client.connector().updateError(u -> u
.connectorId("my-connector")
.error("Houston, we have a problem!")
);
{
"error": "Houston, we have a problem!"
}{
"result": "updated"
}Update the draft filtering configuration of a connector and marks the draft validation state as edited. The filtering draft is activated once validated by the running Elastic connector service. The filtering property is used to configure sync rules (both basic and advanced) for a connector.
PUT _connector/my-g-drive-connector/_filtering
{
"rules": [
{
"field": "file_extension",
"id": "exclude-txt-files",
"order": 0,
"policy": "exclude",
"rule": "equals",
"value": "txt"
},
{
"field": "_",
"id": "DEFAULT",
"order": 1,
"policy": "include",
"rule": "regex",
"value": ".*"
}
]
}resp = client.connector.update_filtering(
connector_id="my-g-drive-connector",
rules=[
{
"field": "file_extension",
"id": "exclude-txt-files",
"order": 0,
"policy": "exclude",
"rule": "equals",
"value": "txt"
},
{
"field": "_",
"id": "DEFAULT",
"order": 1,
"policy": "include",
"rule": "regex",
"value": ".*"
}
],
)const response = await client.connector.updateFiltering({
connector_id: "my-g-drive-connector",
rules: [
{
field: "file_extension",
id: "exclude-txt-files",
order: 0,
policy: "exclude",
rule: "equals",
value: "txt",
},
{
field: "_",
id: "DEFAULT",
order: 1,
policy: "include",
rule: "regex",
value: ".*",
},
],
});response = client.connector.update_filtering(
connector_id: "my-g-drive-connector",
body: {
"rules": [
{
"field": "file_extension",
"id": "exclude-txt-files",
"order": 0,
"policy": "exclude",
"rule": "equals",
"value": "txt"
},
{
"field": "_",
"id": "DEFAULT",
"order": 1,
"policy": "include",
"rule": "regex",
"value": ".*"
}
]
}
)$resp = $client->connector()->updateFiltering([
"connector_id" => "my-g-drive-connector",
"body" => [
"rules" => array(
[
"field" => "file_extension",
"id" => "exclude-txt-files",
"order" => 0,
"policy" => "exclude",
"rule" => "equals",
"value" => "txt",
],
[
"field" => "_",
"id" => "DEFAULT",
"order" => 1,
"policy" => "include",
"rule" => "regex",
"value" => ".*",
],
),
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"rules":[{"field":"file_extension","id":"exclude-txt-files","order":0,"policy":"exclude","rule":"equals","value":"txt"},{"field":"_","id":"DEFAULT","order":1,"policy":"include","rule":"regex","value":".*"}]}' "$ELASTICSEARCH_URL/_connector/my-g-drive-connector/_filtering"client.connector().updateFiltering(u -> u
.connectorId("my-g-drive-connector")
.rules(List.of(FilteringRule.of(f -> f
.field("file_extension")
.id("exclude-txt-files")
.order(0)
.policy(FilteringPolicy.Exclude)
.rule(FilteringRuleRule.Equals)
.value("txt")),FilteringRule.of(f -> f
.field("_")
.id("DEFAULT")
.order(1)
.policy(FilteringPolicy.Include)
.rule(FilteringRuleRule.Regex)
.value(".*"))))
);
{
"rules": [
{
"field": "file_extension",
"id": "exclude-txt-files",
"order": 0,
"policy": "exclude",
"rule": "equals",
"value": "txt"
},
{
"field": "_",
"id": "DEFAULT",
"order": 1,
"policy": "include",
"rule": "regex",
"value": ".*"
}
]
}{
"advanced_snippet": {
"value": [{
"tables": [
"users",
"orders"
],
"query": "SELECT users.id AS id, orders.order_id AS order_id FROM users JOIN orders ON users.id = orders.user_id"
}]
}
}{
"result": "updated"
}Update the draft filtering validation info for a connector.
curl \
--request PUT 'http://api.example.com/_connector/{connector_id}/_filtering/_validation' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '{"validation":{"errors":[{"ids":["string"],"messages":["string"]}],"state":"edited"}}'Update the index_name field of a connector, specifying the index where the data ingested by the connector is stored.
PUT _connector/my-connector/_index_name
{
"index_name": "data-from-my-google-drive"
}resp = client.connector.update_index_name(
connector_id="my-connector",
index_name="data-from-my-google-drive",
)const response = await client.connector.updateIndexName({
connector_id: "my-connector",
index_name: "data-from-my-google-drive",
});response = client.connector.update_index_name(
connector_id: "my-connector",
body: {
"index_name": "data-from-my-google-drive"
}
)$resp = $client->connector()->updateIndexName([
"connector_id" => "my-connector",
"body" => [
"index_name" => "data-from-my-google-drive",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"index_name":"data-from-my-google-drive"}' "$ELASTICSEARCH_URL/_connector/my-connector/_index_name"client.connector().updateIndexName(u -> u
.connectorId("my-connector")
.indexName("data-from-my-google-drive")
);
{
"index_name": "data-from-my-google-drive"
}{
"result": "updated"
}PUT _connector/my-connector/_name
{
"name": "Custom connector",
"description": "This is my customized connector"
}resp = client.connector.update_name(
connector_id="my-connector",
name="Custom connector",
description="This is my customized connector",
)const response = await client.connector.updateName({
connector_id: "my-connector",
name: "Custom connector",
description: "This is my customized connector",
});response = client.connector.update_name(
connector_id: "my-connector",
body: {
"name": "Custom connector",
"description": "This is my customized connector"
}
)$resp = $client->connector()->updateName([
"connector_id" => "my-connector",
"body" => [
"name" => "Custom connector",
"description" => "This is my customized connector",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"name":"Custom connector","description":"This is my customized connector"}' "$ELASTICSEARCH_URL/_connector/my-connector/_name"client.connector().updateName(u -> u
.connectorId("my-connector")
.description("This is my customized connector")
.name("Custom connector")
);
{
"name": "Custom connector",
"description": "This is my customized connector"
}{
"result": "updated"
}curl \
--request PUT 'http://api.example.com/_connector/{connector_id}/_native' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '{"is_native":true}'When you create a new connector, the configuration of an ingest pipeline is populated with default settings.
PUT _connector/my-connector/_pipeline
{
"pipeline": {
"extract_binary_content": true,
"name": "my-connector-pipeline",
"reduce_whitespace": true,
"run_ml_inference": true
}
}resp = client.connector.update_pipeline(
connector_id="my-connector",
pipeline={
"extract_binary_content": True,
"name": "my-connector-pipeline",
"reduce_whitespace": True,
"run_ml_inference": True
},
)const response = await client.connector.updatePipeline({
connector_id: "my-connector",
pipeline: {
extract_binary_content: true,
name: "my-connector-pipeline",
reduce_whitespace: true,
run_ml_inference: true,
},
});response = client.connector.update_pipeline(
connector_id: "my-connector",
body: {
"pipeline": {
"extract_binary_content": true,
"name": "my-connector-pipeline",
"reduce_whitespace": true,
"run_ml_inference": true
}
}
)$resp = $client->connector()->updatePipeline([
"connector_id" => "my-connector",
"body" => [
"pipeline" => [
"extract_binary_content" => true,
"name" => "my-connector-pipeline",
"reduce_whitespace" => true,
"run_ml_inference" => true,
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"pipeline":{"extract_binary_content":true,"name":"my-connector-pipeline","reduce_whitespace":true,"run_ml_inference":true}}' "$ELASTICSEARCH_URL/_connector/my-connector/_pipeline"client.connector().updatePipeline(u -> u
.connectorId("my-connector")
.pipeline(p -> p
.extractBinaryContent(true)
.name("my-connector-pipeline")
.reduceWhitespace(true)
.runMlInference(true)
)
);
{
"pipeline": {
"extract_binary_content": true,
"name": "my-connector-pipeline",
"reduce_whitespace": true,
"run_ml_inference": true
}
}{
"result": "updated"
}PUT _connector/my-connector/_scheduling
{
"scheduling": {
"access_control": {
"enabled": true,
"interval": "0 10 0 * * ?"
},
"full": {
"enabled": true,
"interval": "0 20 0 * * ?"
},
"incremental": {
"enabled": false,
"interval": "0 30 0 * * ?"
}
}
}resp = client.connector.update_scheduling(
connector_id="my-connector",
scheduling={
"access_control": {
"enabled": True,
"interval": "0 10 0 * * ?"
},
"full": {
"enabled": True,
"interval": "0 20 0 * * ?"
},
"incremental": {
"enabled": False,
"interval": "0 30 0 * * ?"
}
},
)const response = await client.connector.updateScheduling({
connector_id: "my-connector",
scheduling: {
access_control: {
enabled: true,
interval: "0 10 0 * * ?",
},
full: {
enabled: true,
interval: "0 20 0 * * ?",
},
incremental: {
enabled: false,
interval: "0 30 0 * * ?",
},
},
});response = client.connector.update_scheduling(
connector_id: "my-connector",
body: {
"scheduling": {
"access_control": {
"enabled": true,
"interval": "0 10 0 * * ?"
},
"full": {
"enabled": true,
"interval": "0 20 0 * * ?"
},
"incremental": {
"enabled": false,
"interval": "0 30 0 * * ?"
}
}
}
)$resp = $client->connector()->updateScheduling([
"connector_id" => "my-connector",
"body" => [
"scheduling" => [
"access_control" => [
"enabled" => true,
"interval" => "0 10 0 * * ?",
],
"full" => [
"enabled" => true,
"interval" => "0 20 0 * * ?",
],
"incremental" => [
"enabled" => false,
"interval" => "0 30 0 * * ?",
],
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"scheduling":{"access_control":{"enabled":true,"interval":"0 10 0 * * ?"},"full":{"enabled":true,"interval":"0 20 0 * * ?"},"incremental":{"enabled":false,"interval":"0 30 0 * * ?"}}}' "$ELASTICSEARCH_URL/_connector/my-connector/_scheduling"client.connector().updateScheduling(u -> u
.connectorId("my-connector")
.scheduling(s -> s
.accessControl(a -> a
.enabled(true)
.interval("0 10 0 * * ?")
)
.full(f -> f
.enabled(true)
.interval("0 20 0 * * ?")
)
.incremental(i -> i
.enabled(false)
.interval("0 30 0 * * ?")
)
)
);
{
"scheduling": {
"access_control": {
"enabled": true,
"interval": "0 10 0 * * ?"
},
"full": {
"enabled": true,
"interval": "0 20 0 * * ?"
},
"incremental": {
"enabled": false,
"interval": "0 30 0 * * ?"
}
}
}{
"scheduling": {
"full": {
"enabled": true,
"interval": "0 10 0 * * ?"
}
}
}{
"result": "updated"
}PUT _connector/my-connector/_service_type
{
"service_type": "sharepoint_online"
}resp = client.connector.update_service_type(
connector_id="my-connector",
service_type="sharepoint_online",
)const response = await client.connector.updateServiceType({
connector_id: "my-connector",
service_type: "sharepoint_online",
});response = client.connector.update_service_type(
connector_id: "my-connector",
body: {
"service_type": "sharepoint_online"
}
)$resp = $client->connector()->updateServiceType([
"connector_id" => "my-connector",
"body" => [
"service_type" => "sharepoint_online",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service_type":"sharepoint_online"}' "$ELASTICSEARCH_URL/_connector/my-connector/_service_type"client.connector().updateServiceType(u -> u
.connectorId("my-connector")
.serviceType("sharepoint_online")
);
{
"service_type": "sharepoint_online"
}{
"result": "updated"
}PUT _connector/my-connector/_status
{
"status": "needs_configuration"
}resp = client.connector.update_status(
connector_id="my-connector",
status="needs_configuration",
)const response = await client.connector.updateStatus({
connector_id: "my-connector",
status: "needs_configuration",
});response = client.connector.update_status(
connector_id: "my-connector",
body: {
"status": "needs_configuration"
}
)$resp = $client->connector()->updateStatus([
"connector_id" => "my-connector",
"body" => [
"status" => "needs_configuration",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"status":"needs_configuration"}' "$ELASTICSEARCH_URL/_connector/my-connector/_status"client.connector().updateStatus(u -> u
.connectorId("my-connector")
.status(ConnectorStatus.NeedsConfiguration)
);
{
"status": "needs_configuration"
}{
"result": "updated"
}Comma-separated list of data stream names used to limit the request.
Wildcard (*) expressions are supported. If omitted, all data streams are returned.
Type of data stream that wildcard patterns can match.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, returns all relevant default configurations for the index template.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Whether the maximum timestamp for each data stream should be calculated and returned.
GET _data_stream/my-data-stream
resp = client.indices.get_data_stream(
name="my-data-stream",
)const response = await client.indices.getDataStream({
name: "my-data-stream",
});response = client.indices.get_data_stream(
name: "my-data-stream"
)$resp = $client->indices()->getDataStream([
"name" => "my-data-stream",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_data_stream/my-data-stream"client.indices().getDataStream(g -> g
.name("my-data-stream")
);
{
"data_streams": [
{
"name": "my-data-stream",
"timestamp_field": {
"name": "@timestamp"
},
"indices": [
{
"index_name": ".ds-my-data-stream-2099.03.07-000001",
"index_uuid": "xCEhwsp8Tey0-FLNFYVwSg",
"prefer_ilm": true,
"ilm_policy": "my-lifecycle-policy",
"managed_by": "Index Lifecycle Management"
},
{
"index_name": ".ds-my-data-stream-2099.03.08-000002",
"index_uuid": "PA_JquKGSiKcAKBA8DJ5gw",
"prefer_ilm": true,
"ilm_policy": "my-lifecycle-policy",
"managed_by": "Index Lifecycle Management"
}
],
"generation": 2,
"_meta": {
"my-meta-field": "foo"
},
"status": "GREEN",
"next_generation_managed_by": "Index Lifecycle Management",
"prefer_ilm": true,
"template": "my-index-template",
"ilm_policy": "my-lifecycle-policy",
"hidden": false,
"system": false,
"allow_custom_routing": false,
"replicated": false,
"rollover_on_write": false
},
{
"name": "my-data-stream-two",
"timestamp_field": {
"name": "@timestamp"
},
"indices": [
{
"index_name": ".ds-my-data-stream-two-2099.03.08-000001",
"index_uuid": "3liBu2SYS5axasRt6fUIpA",
"prefer_ilm": true,
"ilm_policy": "my-lifecycle-policy",
"managed_by": "Index Lifecycle Management"
}
],
"generation": 1,
"_meta": {
"my-meta-field": "foo"
},
"status": "YELLOW",
"next_generation_managed_by": "Index Lifecycle Management",
"prefer_ilm": true,
"template": "my-index-template",
"ilm_policy": "my-lifecycle-policy",
"hidden": false,
"system": false,
"allow_custom_routing": false,
"replicated": false,
"rollover_on_write": false
}
]
}Name of the data stream, which must meet the following criteria:
Lowercase only;
Cannot include \, /, *, ?, ", <, >, |, ,, #, :, or a space character;
Cannot start with -, _, +, or .ds-;
Cannot be . or ..;
Cannot be longer than 255 bytes. Multi-byte characters count towards this limit faster.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
PUT _data_stream/logs-foo-bar
resp = client.indices.create_data_stream(
name="logs-foo-bar",
)const response = await client.indices.createDataStream({
name: "logs-foo-bar",
});response = client.indices.create_data_stream(
name: "logs-foo-bar"
)$resp = $client->indices()->createDataStream([
"name" => "logs-foo-bar",
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_data_stream/logs-foo-bar"client.indices().createDataStream(c -> c
.name("logs-foo-bar")
);
Comma-separated list of data streams to delete. Wildcard (*) expressions are supported.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Type of data stream that wildcard patterns can match. 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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
DELETE _data_stream/my-data-stream
resp = client.indices.delete_data_stream(
name="my-data-stream",
)const response = await client.indices.deleteDataStream({
name: "my-data-stream",
});response = client.indices.delete_data_stream(
name: "my-data-stream"
)$resp = $client->indices()->deleteDataStream([
"name" => "my-data-stream",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_data_stream/my-data-stream"client.indices().deleteDataStream(d -> d
.name("my-data-stream")
);
Get information about an index or data stream's current data stream lifecycle status, such as time since index creation, time since rollover, the lifecycle configuration managing the index, or any errors encountered during lifecycle execution.
GET .ds-metrics-2023.03.22-000001/_lifecycle/explain
resp = client.indices.explain_data_lifecycle(
index=".ds-metrics-2023.03.22-000001",
)const response = await client.indices.explainDataLifecycle({
index: ".ds-metrics-2023.03.22-000001",
});response = client.indices.explain_data_lifecycle(
index: ".ds-metrics-2023.03.22-000001"
)$resp = $client->indices()->explainDataLifecycle([
"index" => ".ds-metrics-2023.03.22-000001",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/.ds-metrics-2023.03.22-000001/_lifecycle/explain"client.indices().explainDataLifecycle(e -> e
.index(".ds-metrics-2023.03.22-000001")
);
{
"indices": {
".ds-metrics-2023.03.22-000001": {
"index" : ".ds-metrics-2023.03.22-000001",
"managed_by_lifecycle" : true,
"index_creation_date_millis" : 1679475563571,
"time_since_index_creation" : "843ms",
"rollover_date_millis" : 1679475564293,
"time_since_rollover" : "121ms",
"lifecycle" : { },
"generation_time" : "121ms"
}
}{
"indices": {
".ds-metrics-2023.03.22-000001": {
"index" : ".ds-metrics-2023.03.22-000001",
"managed_by_lifecycle" : true,
"index_creation_date_millis" : 1679475563571,
"time_since_index_creation" : "843ms",
"lifecycle" : {
"enabled": true
},
"error": "{\"type\":\"validation_exception\",\"reason\":\"Validation Failed: 1: this action would add [2] shards, but this cluster
currently has [4]/[3] maximum normal shards open;\"}"
}
}Get the data stream lifecycle configuration of one or more data streams.
Comma-separated list of data streams to limit the request.
Supports wildcards (*).
To target all data streams, omit this parameter or use * or _all.
Type of data stream that wildcard patterns can match.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, return all default settings in the response.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
GET /_data_stream/{name}/_lifecycle?human&pretty
resp = client.indices.get_data_lifecycle(
name="{name}",
human=True,
pretty=True,
)const response = await client.indices.getDataLifecycle({
name: "{name}",
human: "true",
pretty: "true",
});response = client.indices.get_data_lifecycle(
name: "{name}",
human: "true",
pretty: "true"
)$resp = $client->indices()->getDataLifecycle([
"name" => "{name}",
"human" => "true",
"pretty" => "true",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_data_stream/%7Bname%7D/_lifecycle?human&pretty"{
"data_streams": [
{
"name": "my-data-stream-1",
"lifecycle": {
"enabled": true,
"data_retention": "7d"
}
},
{
"name": "my-data-stream-2",
"lifecycle": {
"enabled": true,
"data_retention": "7d"
}
}
]
}Update the data stream lifecycle of the specified data streams.
Comma-separated list of data streams used to limit the request.
Supports wildcards (*).
To target all data streams use * or _all.
Type of data stream that wildcard patterns can match.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and
d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
If defined, it turns data stream lifecycle on/off (true/false) for this data stream. A data stream lifecycle
that's disabled (enabled: false) will have no effect on the data stream.
Default value is true.
PUT _data_stream/my-data-stream/_lifecycle
{
"data_retention": "7d"
}resp = client.indices.put_data_lifecycle(
name="my-data-stream",
data_retention="7d",
)const response = await client.indices.putDataLifecycle({
name: "my-data-stream",
data_retention: "7d",
});response = client.indices.put_data_lifecycle(
name: "my-data-stream",
body: {
"data_retention": "7d"
}
)$resp = $client->indices()->putDataLifecycle([
"name" => "my-data-stream",
"body" => [
"data_retention" => "7d",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"data_retention":"7d"}' "$ELASTICSEARCH_URL/_data_stream/my-data-stream/_lifecycle"client.indices().putDataLifecycle(p -> p
.dataRetention(d -> d
.time("7d")
)
.name("my-data-stream")
);
{
"data_retention": "7d"
}{
"downsampling": [
{
"after": "1d",
"fixed_interval": "10m"
},
{
"after": "7d",
"fixed_interval": "1d"
}
]
}{
"acknowledged": true
}Get the data stream options configuration of one or more data streams.
Comma-separated list of data streams to limit the request.
Supports wildcards (*).
To target all data streams, omit this parameter or use * or _all.
Type of data stream that wildcard patterns can match.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
curl \
--request GET 'http://api.example.com/_data_stream/{name}/_options' \
--header "Authorization: $API_KEY"Update the data stream options of the specified data streams.
Comma-separated list of data streams used to limit the request.
Supports wildcards (*).
To target all data streams use * or _all.
Type of data stream that wildcard patterns can match.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
curl \
--request PUT 'http://api.example.com/_data_stream/{name}/_options' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '{"failure_store":{"enabled":true,"lifecycle":{"data_retention":"string","enabled":true}}}'A comma-separated list of data streams or data stream patterns. Supports wildcards (*).
GET /_data_stream/my-data-stream/_settings
resp = client.indices.get_data_stream_settings(
name="my-data-stream",
)const response = await client.indices.getDataStreamSettings({
name: "my-data-stream",
});response = client.indices.get_data_stream_settings(
name: "my-data-stream"
)$resp = $client->indices()->getDataStreamSettings([
"name" => "my-data-stream",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_data_stream/my-data-stream/_settings"{
"data_streams": [
{
"name": "my-data-stream",
"settings": {
"index": {
"lifecycle": {
"name": "new-test-policy"
},
"number_of_shards": "11"
}
},
"effective_settings": {
"index": {
"lifecycle": {
"name": "new-test-policy"
},
"mode": "standard",
"number_of_shards": "11",
"number_of_replicas": "0"
}
}
}
]
}This API can be used to override settings on specific data streams. These overrides will take precedence over what is specified in the template that the data stream matches. To prevent your data stream from getting into an invalid state, only certain settings are allowed. If possible, the setting change is applied to all backing indices. Otherwise, it will be applied when the data stream is next rolled over.
manageIf true, the request does not actually change the settings on any data streams or indices. Instead, it
simulates changing the settings and reports back to the user what would have happened had these settings
actually been applied.
The period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
The period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
PUT /_data_stream/my-data-stream/_settings
{
"index.lifecycle.name" : "new-test-policy",
"index.number_of_shards": 11
}resp = client.indices.put_data_stream_settings(
name="my-data-stream",
settings={
"index.lifecycle.name": "new-test-policy",
"index.number_of_shards": 11
},
)const response = await client.indices.putDataStreamSettings({
name: "my-data-stream",
settings: {
"index.lifecycle.name": "new-test-policy",
"index.number_of_shards": 11,
},
});response = client.indices.put_data_stream_settings(
name: "my-data-stream",
body: {
"index.lifecycle.name": "new-test-policy",
"index.number_of_shards": 11
}
)$resp = $client->indices()->putDataStreamSettings([
"name" => "my-data-stream",
"body" => [
"index.lifecycle.name" => "new-test-policy",
"index.number_of_shards" => 11,
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"index.lifecycle.name":"new-test-policy","index.number_of_shards":11}' "$ELASTICSEARCH_URL/_data_stream/my-data-stream/_settings"{
"index.lifecycle.name" : "new-test-policy",
"index.number_of_shards": 11
}{
"data_streams": [
{
"name": "my-data-stream",
"applied_to_data_stream": true,
"settings": {
"index": {
"lifecycle": {
"name": "new-test-policy"
},
"number_of_shards": "11"
}
},
"effective_settings": {
"index": {
"lifecycle": {
"name": "new-test-policy"
},
"mode": "standard",
"number_of_shards": "11",
"number_of_replicas": "0"
}
},
"index_settings_results": {
"applied_to_data_stream_only": [
"index.number_of_shards"
],
"applied_to_data_stream_and_backing_indices": [
"index.lifecycle.name"
]
}
}
]
}{
"data_streams": [
{
"name": "my-data-stream",
"applied_to_data_stream": true,
"settings": {
"index": {
"lifecycle": {
"name": "new-test-policy"
},
"number_of_shards": "11"
}
},
"effective_settings": {
"index": {
"lifecycle": {
"name": "new-test-policy"
},
"mode": "standard",
"number_of_shards": "11",
"number_of_replicas": "0"
}
},
"index_settings_results": {
"applied_to_data_stream_only": [
"index.number_of_shards"
],
"applied_to_data_stream_and_backing_indices": [
"index.lifecycle.name"
],
"errors": [
{
"index": ".ds-my-data-stream-2025.05.28-000001",
"error": "index [.ds-my-data-stream-2025.05.28-000001] blocked by: [FORBIDDEN/9/index metadata (api)];"
}
]
}
}
]
}{
"data_streams": [
{
"name": "my-data-stream",
"applied_to_data_stream": false,
"error": "Cannot set the following settings on a data stream: [index.number_of_replicas]",
"settings": {},
"effective_settings": {},
"index_settings_results": {
"applied_to_data_stream_only": [],
"applied_to_data_stream_and_backing_indices": []
}
}
]
}Converts an index alias to a data stream.
You must have a matching index template that is data stream enabled.
The alias must meet the following criteria:
The alias must have a write index;
All indices for the alias must have a @timestamp field mapping of a date or date_nanos field type;
The alias must not have any filters;
The alias must not use custom routing.
If successful, the request removes the alias and creates a data stream with the same name.
The indices for the alias become hidden backing indices for the stream.
The write index for the alias becomes the write index for the stream.
managePeriod to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
POST _data_stream/_migrate/my-time-series-data
resp = client.indices.migrate_to_data_stream(
name="my-time-series-data",
)const response = await client.indices.migrateToDataStream({
name: "my-time-series-data",
});response = client.indices.migrate_to_data_stream(
name: "my-time-series-data"
)$resp = $client->indices()->migrateToDataStream([
"name" => "my-time-series-data",
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_data_stream/_migrate/my-time-series-data"client.indices().migrateToDataStream(m -> m
.name("my-time-series-data")
);
Performs one or more data stream modification actions in a single atomic operation.
POST _data_stream/_modify
{
"actions": [
{
"remove_backing_index": {
"data_stream": "my-data-stream",
"index": ".ds-my-data-stream-2023.07.26-000001"
}
},
{
"add_backing_index": {
"data_stream": "my-data-stream",
"index": ".ds-my-data-stream-2023.07.26-000001-downsample"
}
}
]
}resp = client.indices.modify_data_stream(
actions=[
{
"remove_backing_index": {
"data_stream": "my-data-stream",
"index": ".ds-my-data-stream-2023.07.26-000001"
}
},
{
"add_backing_index": {
"data_stream": "my-data-stream",
"index": ".ds-my-data-stream-2023.07.26-000001-downsample"
}
}
],
)const response = await client.indices.modifyDataStream({
actions: [
{
remove_backing_index: {
data_stream: "my-data-stream",
index: ".ds-my-data-stream-2023.07.26-000001",
},
},
{
add_backing_index: {
data_stream: "my-data-stream",
index: ".ds-my-data-stream-2023.07.26-000001-downsample",
},
},
],
});response = client.indices.modify_data_stream(
body: {
"actions": [
{
"remove_backing_index": {
"data_stream": "my-data-stream",
"index": ".ds-my-data-stream-2023.07.26-000001"
}
},
{
"add_backing_index": {
"data_stream": "my-data-stream",
"index": ".ds-my-data-stream-2023.07.26-000001-downsample"
}
}
]
}
)$resp = $client->indices()->modifyDataStream([
"body" => [
"actions" => array(
[
"remove_backing_index" => [
"data_stream" => "my-data-stream",
"index" => ".ds-my-data-stream-2023.07.26-000001",
],
],
[
"add_backing_index" => [
"data_stream" => "my-data-stream",
"index" => ".ds-my-data-stream-2023.07.26-000001-downsample",
],
],
),
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"actions":[{"remove_backing_index":{"data_stream":"my-data-stream","index":".ds-my-data-stream-2023.07.26-000001"}},{"add_backing_index":{"data_stream":"my-data-stream","index":".ds-my-data-stream-2023.07.26-000001-downsample"}}]}' "$ELASTICSEARCH_URL/_data_stream/_modify"client.indices().modifyDataStream(m -> m
.actions(List.of(Action.of(a -> a
.removeBackingIndex(r -> r
.dataStream("my-data-stream")
.index(".ds-my-data-stream-2023.07.26-000001")
)),Action.of(ac -> ac
.addBackingIndex(ad -> ad
.dataStream("my-data-stream")
.index(".ds-my-data-stream-2023.07.26-000001-downsample")
))))
);
{
"actions": [
{
"remove_backing_index": {
"data_stream": "my-data-stream",
"index": ".ds-my-data-stream-2023.07.26-000001"
}
},
{
"add_backing_index": {
"data_stream": "my-data-stream",
"index": ".ds-my-data-stream-2023.07.26-000001-downsample"
}
}
]
}All methods and paths for this operation:
Perform multiple index, create, delete, and update actions in a single request.
This reduces overhead and can greatly increase indexing speed.
If the Elasticsearch security features are enabled, you must have the following index privileges for the target data stream, index, or index alias:
create action, you must have the create_doc, create, index, or write index privilege. Data streams support only the create action.index action, you must have the create, index, or write index privilege.delete action, you must have the delete or write index privilege.update action, you must have the index or write index privilege.auto_configure, create_index, or manage index privilege.refresh parameter, you must have the maintenance or manage index privilege.Automatic data stream creation requires a matching index template with data stream enabled.
The actions are specified in the request body using a newline delimited JSON (NDJSON) structure:
action_and_meta_data\n
optional_source\n
action_and_meta_data\n
optional_source\n
....
action_and_meta_data\n
optional_source\n
The index and create actions expect a source on the next line and have the same semantics as the op_type parameter in the standard index API.
A create action fails if a document with the same ID already exists in the target
An index action adds or replaces a document as necessary.
NOTE: Data streams support only the create action.
To update or delete a document in a data stream, you must target the backing index containing the document.
An update action expects that the partial doc, upsert, and script and its options are specified on the next line.
A delete action does not expect a source on the next line and has the same semantics as the standard delete API.
NOTE: The final line of data must end with a newline character (\n).
Each newline character may be preceded by a carriage return (\r).
When sending NDJSON data to the _bulk endpoint, use a Content-Type header of application/json or application/x-ndjson.
Because this format uses literal newline characters (\n) as delimiters, make sure that the JSON actions and sources are not pretty printed.
If you provide a target in the request path, it is used for any actions that don't explicitly specify an _index argument.
A note on the format: the idea here is to make processing as fast as possible.
As some of the actions are redirected to other shards on other nodes, only action_meta_data is parsed on the receiving node side.
Client libraries using this protocol should try and strive to do something similar on the client side, and reduce buffering as much as possible.
There is no "correct" number of actions to perform in a single bulk request. Experiment with different settings to find the optimal size for your particular workload. Note that Elasticsearch limits the maximum size of a HTTP request to 100mb by default so clients must ensure that no request exceeds this size. It is not possible to index a single document that exceeds the size limit, so you must pre-process any such documents into smaller pieces before sending them to Elasticsearch. For instance, split documents into pages or chapters before indexing them, or store raw binary data in a system outside Elasticsearch and replace the raw data with a link to the external system in the documents that you send to Elasticsearch.
Client suppport for bulk requests
Some of the officially supported clients provide helpers to assist with bulk requests and reindexing:
esutil.BulkIndexerSearch::Elasticsearch::Client::5_0::Bulk and Search::Elasticsearch::Client::5_0::Scrollelasticsearch.helpers.*client.helpers.*BulkAllObservableSubmitting bulk requests with cURL
If you're providing text file input to curl, you must use the --data-binary flag instead of plain -d.
The latter doesn't preserve newlines. For example:
$ cat requests
{ "index" : { "_index" : "test", "_id" : "1" } }
{ "field1" : "value1" }
$ curl -s -H "Content-Type: application/x-ndjson" -XPOST localhost:9200/_bulk --data-binary "@requests"; echo
{"took":7, "errors": false, "items":[{"index":{"_index":"test","_id":"1","_version":1,"result":"created","forced_refresh":false}}]}
Optimistic concurrency control
Each index and delete action within a bulk API call may include the if_seq_no and if_primary_term parameters in their respective action and meta data lines.
The if_seq_no and if_primary_term parameters control how operations are run, based on the last modification to existing documents. See Optimistic concurrency control for more details.
Versioning
Each bulk item can include the version value using the version field.
It automatically follows the behavior of the index or delete operation based on the _version mapping.
It also support the version_type.
Routing
Each bulk item can include the routing value using the routing field.
It automatically follows the behavior of the index or delete operation based on the _routing mapping.
NOTE: Data streams do not support custom routing unless they were created with the allow_custom_routing setting enabled in the template.
Wait for active shards
When making bulk calls, you can set the wait_for_active_shards parameter to require a minimum number of shard copies to be active before starting to process the bulk request.
Refresh
Control when the changes made by this request are visible to search.
NOTE: Only the shards that receive the bulk request will be affected by refresh.
Imagine a _bulk?refresh=wait_for request with three documents in it that happen to be routed to different shards in an index with five shards.
The request will only wait for those three shards to refresh.
The other two shards that make up the index do not participate in the _bulk request at all.
You might want to disable the refresh interval temporarily to improve indexing throughput for large bulk requests. Refer to the linked documentation for step-by-step instructions using the index settings API.
True or false if to include the document source in the error message in case of parsing errors.
If true, the response will include the ingest pipelines that were run for each index or create.
The pipeline identifier to use to preprocess incoming documents.
If the index has a default ingest pipeline specified, setting the value to _none turns off the default ingest pipeline for this request.
If a final pipeline is configured, it will always run regardless of the value of this parameter.
If true, Elasticsearch refreshes the affected shards to make this operation visible to search.
If wait_for, wait for a refresh to make this operation visible to search.
If false, do nothing with refreshes.
Valid values: true, false, wait_for.
Values are true, false, or wait_for.
A custom value that is used to route operations to a specific shard.
Indicates whether to return the _source field (true or false) or contains a list of fields to return.
A comma-separated list of source fields to exclude from the response.
You can also use this parameter to exclude fields from the subset specified in _source_includes query parameter.
If the _source parameter is false, this parameter is ignored.
A comma-separated list of source fields to include in the response.
If this parameter is specified, only these source fields are returned.
You can exclude fields from this subset using the _source_excludes query parameter.
If the _source parameter is false, this parameter is ignored.
The period each action waits for the following operations: automatic index creation, dynamic mapping updates, and waiting for active shards.
The default is 1m (one minute), which guarantees Elasticsearch waits for at least the timeout before failing.
The actual wait time could be longer, particularly when multiple waits occur.
Values are -1 or 0.
The number of shard copies that must be active before proceeding with the operation.
Set to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
The default is 1, which waits for each primary shard to be active.
Values are all or index-setting.
If true, the request's actions must target an index alias.
If true, the request's actions must target a data stream (existing or to be created).
POST _bulk
{ "index" : { "_index" : "test", "_id" : "1" } }
{ "field1" : "value1" }
{ "delete" : { "_index" : "test", "_id" : "2" } }
{ "create" : { "_index" : "test", "_id" : "3" } }
{ "field1" : "value3" }
{ "update" : {"_id" : "1", "_index" : "test"} }
{ "doc" : {"field2" : "value2"} }resp = client.bulk(
operations=[
{
"index": {
"_index": "test",
"_id": "1"
}
},
{
"field1": "value1"
},
{
"delete": {
"_index": "test",
"_id": "2"
}
},
{
"create": {
"_index": "test",
"_id": "3"
}
},
{
"field1": "value3"
},
{
"update": {
"_id": "1",
"_index": "test"
}
},
{
"doc": {
"field2": "value2"
}
}
],
)const response = await client.bulk({
operations: [
{
index: {
_index: "test",
_id: "1",
},
},
{
field1: "value1",
},
{
delete: {
_index: "test",
_id: "2",
},
},
{
create: {
_index: "test",
_id: "3",
},
},
{
field1: "value3",
},
{
update: {
_id: "1",
_index: "test",
},
},
{
doc: {
field2: "value2",
},
},
],
});response = client.bulk(
body: [
{
"index": {
"_index": "test",
"_id": "1"
}
},
{
"field1": "value1"
},
{
"delete": {
"_index": "test",
"_id": "2"
}
},
{
"create": {
"_index": "test",
"_id": "3"
}
},
{
"field1": "value3"
},
{
"update": {
"_id": "1",
"_index": "test"
}
},
{
"doc": {
"field2": "value2"
}
}
]
)$resp = $client->bulk([
"body" => array(
[
"index" => [
"_index" => "test",
"_id" => "1",
],
],
[
"field1" => "value1",
],
[
"delete" => [
"_index" => "test",
"_id" => "2",
],
],
[
"create" => [
"_index" => "test",
"_id" => "3",
],
],
[
"field1" => "value3",
],
[
"update" => [
"_id" => "1",
"_index" => "test",
],
],
[
"doc" => [
"field2" => "value2",
],
],
),
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '[{"index":{"_index":"test","_id":"1"}},{"field1":"value1"},{"delete":{"_index":"test","_id":"2"}},{"create":{"_index":"test","_id":"3"}},{"field1":"value3"},{"update":{"_id":"1","_index":"test"}},{"doc":{"field2":"value2"}}]' "$ELASTICSEARCH_URL/_bulk"{ "index" : { "_index" : "test", "_id" : "1" } }
{ "field1" : "value1" }
{ "delete" : { "_index" : "test", "_id" : "2" } }
{ "create" : { "_index" : "test", "_id" : "3" } }
{ "field1" : "value3" }
{ "update" : {"_id" : "1", "_index" : "test"} }
{ "doc" : {"field2" : "value2"} }{ "update" : {"_id" : "1", "_index" : "index1", "retry_on_conflict" : 3} }
{ "doc" : {"field" : "value"} }
{ "update" : { "_id" : "0", "_index" : "index1", "retry_on_conflict" : 3} }
{ "script" : { "source": "ctx._source.counter += params.param1", "lang" : "painless", "params" : {"param1" : 1}}, "upsert" : {"counter" : 1}}
{ "update" : {"_id" : "2", "_index" : "index1", "retry_on_conflict" : 3} }
{ "doc" : {"field" : "value"}, "doc_as_upsert" : true }
{ "update" : {"_id" : "3", "_index" : "index1", "_source" : true} }
{ "doc" : {"field" : "value"} }
{ "update" : {"_id" : "4", "_index" : "index1"} }
{ "doc" : {"field" : "value"}, "_source": true}{ "update": {"_id": "5", "_index": "index1"} }
{ "doc": {"my_field": "foo"} }
{ "update": {"_id": "6", "_index": "index1"} }
{ "doc": {"my_field": "foo"} }
{ "create": {"_id": "7", "_index": "index1"} }
{ "my_field": "foo" }{ "index" : { "_index" : "my_index", "_id" : "1", "dynamic_templates": {"work_location": "geo_point"}} }
{ "field" : "value1", "work_location": "41.12,-71.34", "raw_location": "41.12,-71.34"}
{ "create" : { "_index" : "my_index", "_id" : "2", "dynamic_templates": {"home_location": "geo_point"}} }
{ "field" : "value2", "home_location": "41.12,-71.34"}{
"took": 30,
"errors": false,
"items": [
{
"index": {
"_index": "test",
"_id": "1",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 201,
"_seq_no" : 0,
"_primary_term": 1
}
},
{
"delete": {
"_index": "test",
"_id": "2",
"_version": 1,
"result": "not_found",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 404,
"_seq_no" : 1,
"_primary_term" : 2
}
},
{
"create": {
"_index": "test",
"_id": "3",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 201,
"_seq_no" : 2,
"_primary_term" : 3
}
},
{
"update": {
"_index": "test",
"_id": "1",
"_version": 2,
"result": "updated",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"status": 200,
"_seq_no" : 3,
"_primary_term" : 4
}
}
]
}{
"took": 486,
"errors": true,
"items": [
{
"update": {
"_index": "index1",
"_id": "5",
"status": 404,
"error": {
"type": "document_missing_exception",
"reason": "[5]: document missing",
"index_uuid": "aAsFqTI0Tc2W0LCWgPNrOA",
"shard": "0",
"index": "index1"
}
}
},
{
"update": {
"_index": "index1",
"_id": "6",
"status": 404,
"error": {
"type": "document_missing_exception",
"reason": "[6]: document missing",
"index_uuid": "aAsFqTI0Tc2W0LCWgPNrOA",
"shard": "0",
"index": "index1"
}
}
},
{
"create": {
"_index": "index1",
"_id": "7",
"_version": 1,
"result": "created",
"_shards": {
"total": 2,
"successful": 1,
"failed": 0
},
"_seq_no": 0,
"_primary_term": 1,
"status": 201
}
}
]
}{
"items": [
{
"update": {
"error": {
"type": "document_missing_exception",
"reason": "[5]: document missing",
"index_uuid": "aAsFqTI0Tc2W0LCWgPNrOA",
"shard": "0",
"index": "index1"
}
}
},
{
"update": {
"error": {
"type": "document_missing_exception",
"reason": "[6]: document missing",
"index_uuid": "aAsFqTI0Tc2W0LCWgPNrOA",
"shard": "0",
"index": "index1"
}
}
}
]
}All methods and paths for this operation:
You can index a new JSON document with the /<target>/_doc/ or /<target>/_create/<_id> APIs
Using _create guarantees that the document is indexed only if it does not already exist.
It returns a 409 response when a document with a same ID already exists in the index.
To update an existing document, you must use the /<target>/_doc/ API.
If the Elasticsearch security features are enabled, you must have the following index privileges for the target data stream, index, or index alias:
PUT /<target>/_create/<_id> or POST /<target>/_create/<_id> request formats, you must have the create_doc, create, index, or write index privilege.auto_configure, create_index, or manage index privilege.Automatic data stream creation requires a matching index template with data stream enabled.
Automatically create data streams and indices
If the request's target doesn't exist and matches an index template with a data_stream definition, the index operation automatically creates the data stream.
If the target doesn't exist and doesn't match a data stream template, the operation automatically creates the index and applies any matching index templates.
NOTE: Elasticsearch includes several built-in index templates. To avoid naming collisions with these templates, refer to index pattern documentation.
If no mapping exists, the index operation creates a dynamic mapping. By default, new fields and objects are automatically added to the mapping if needed.
Automatic index creation is controlled by the action.auto_create_index setting.
If it is true, any index can be created automatically.
You can modify this setting to explicitly allow or block automatic creation of indices that match specified patterns or set it to false to turn off automatic index creation entirely.
Specify a comma-separated list of patterns you want to allow or prefix each pattern with + or - to indicate whether it should be allowed or blocked.
When a list is specified, the default behaviour is to disallow.
NOTE: The action.auto_create_index setting affects the automatic creation of indices only.
It does not affect the creation of data streams.
Routing
By default, shard placement — or routing — is controlled by using a hash of the document's ID value.
For more explicit control, the value fed into the hash function used by the router can be directly specified on a per-operation basis using the routing parameter.
When setting up explicit mapping, you can also use the _routing field to direct the index operation to extract the routing value from the document itself.
This does come at the (very minimal) cost of an additional document parsing pass.
If the _routing mapping is defined and set to be required, the index operation will fail if no routing value is provided or extracted.
NOTE: Data streams do not support custom routing unless they were created with the allow_custom_routing setting enabled in the template.
Distributed
The index operation is directed to the primary shard based on its route and performed on the actual node containing this shard. After the primary shard completes the operation, if needed, the update is distributed to applicable replicas.
Active shards
To improve the resiliency of writes to the system, indexing operations can be configured to wait for a certain number of active shard copies before proceeding with the operation.
If the requisite number of active shard copies are not available, then the write operation must wait and retry, until either the requisite shard copies have started or a timeout occurs.
By default, write operations only wait for the primary shards to be active before proceeding (that is to say wait_for_active_shards is 1).
This default can be overridden in the index settings dynamically by setting index.write.wait_for_active_shards.
To alter this behavior per operation, use the wait_for_active_shards request parameter.
Valid values are all or any positive integer up to the total number of configured copies per shard in the index (which is number_of_replicas+1).
Specifying a negative value or a number greater than the number of shard copies will throw an error.
For example, suppose you have a cluster of three nodes, A, B, and C and you create an index index with the number of replicas set to 3 (resulting in 4 shard copies, one more copy than there are nodes).
If you attempt an indexing operation, by default the operation will only ensure the primary copy of each shard is available before proceeding.
This means that even if B and C went down and A hosted the primary shard copies, the indexing operation would still proceed with only one copy of the data.
If wait_for_active_shards is set on the request to 3 (and all three nodes are up), the indexing operation will require 3 active shard copies before proceeding.
This requirement should be met because there are 3 active nodes in the cluster, each one holding a copy of the shard.
However, if you set wait_for_active_shards to all (or to 4, which is the same in this situation), the indexing operation will not proceed as you do not have all 4 copies of each shard active in the index.
The operation will timeout unless a new node is brought up in the cluster to host the fourth copy of the shard.
It is important to note that this setting greatly reduces the chances of the write operation not writing to the requisite number of shard copies, but it does not completely eliminate the possibility, because this check occurs before the write operation starts.
After the write operation is underway, it is still possible for replication to fail on any number of shard copies but still succeed on the primary.
The _shards section of the API response reveals the number of shard copies on which replication succeeded and failed.
createThe name of the data stream or index to target.
If the target doesn't exist and matches the name or wildcard (*) pattern of an index template with a data_stream definition, this request creates the data stream.
If the target doesn't exist and doesn’t match a data stream template, this request creates the index.
A unique identifier for the document.
To automatically generate a document ID, use the POST /<target>/_doc/ request format.
Only perform the operation if the document has this primary term.
Only perform the operation if the document has this sequence number.
True or false if to include the document source in the error message in case of parsing errors.
Set to create to only index the document if it does not already exist (put if absent).
If a document with the specified _id already exists, the indexing operation will fail.
The behavior is the same as using the <index>/_create endpoint.
If a document ID is specified, this paramater defaults to index.
Otherwise, it defaults to create.
If the request targets a data stream, an op_type of create is required.
Supported values include:
index: Overwrite any documents that already exist.create: Only index documents that do not already exist.Values are index or create.
The ID of the pipeline to use to preprocess incoming documents.
If the index has a default ingest pipeline specified, setting the value to _none turns off the default ingest pipeline for this request.
If a final pipeline is configured, it will always run regardless of the value of this parameter.
If true, Elasticsearch refreshes the affected shards to make this operation visible to search.
If wait_for, it waits for a refresh to make this operation visible to search.
If false, it does nothing with refreshes.
Values are true, false, or wait_for.
If true, the destination must be an index alias.
If true, the request's actions must target a data stream (existing or to be created).
A custom value that is used to route operations to a specific shard.
The period the request waits for the following operations: automatic index creation, dynamic mapping updates, waiting for active shards. Elasticsearch waits for at least the specified timeout period before failing. The actual wait time could be longer, particularly when multiple waits occur.
This parameter is useful for situations where the primary shard assigned to perform the operation might not be available when the operation runs. Some reasons for this might be that the primary shard is currently recovering from a gateway or undergoing relocation. By default, the operation will wait on the primary shard to become available for at least 1 minute before failing and responding with an error. The actual wait time could be longer, particularly when multiple waits occur.
Values are -1 or 0.
The explicit version number for concurrency control. It must be a non-negative long number.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
The number of shard copies that must be active before proceeding with the operation.
You can set it to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
The default value of 1 means it waits for each primary shard to be active.
Values are all or index-setting.
PUT my-index-000001/_create/1
{
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
}resp = client.create(
index="my-index-000001",
id="1",
document={
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
},
)const response = await client.create({
index: "my-index-000001",
id: 1,
document: {
"@timestamp": "2099-11-15T13:12:00",
message: "GET /search HTTP/1.1 200 1070000",
user: {
id: "kimchy",
},
},
});response = client.create(
index: "my-index-000001",
id: "1",
body: {
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
}
)$resp = $client->create([
"index" => "my-index-000001",
"id" => "1",
"body" => [
"@timestamp" => "2099-11-15T13:12:00",
"message" => "GET /search HTTP/1.1 200 1070000",
"user" => [
"id" => "kimchy",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"@timestamp":"2099-11-15T13:12:00","message":"GET /search HTTP/1.1 200 1070000","user":{"id":"kimchy"}}' "$ELASTICSEARCH_URL/my-index-000001/_create/1"client.create(c -> c
.id("1")
.index("my-index-000001")
.document(JsonData.fromJson("{\"@timestamp\":\"2099-11-15T13:12:00\",\"message\":\"GET /search HTTP/1.1 200 1070000\",\"user\":{\"id\":\"kimchy\"}}"))
);
{
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
}{
"_index": "my-index-000001",
"_id": "1",
"_version": 1,
"result": "created",
"_shards": {
"total": 1,
"successful": 1,
"failed": 0
},
"_seq_no": 0,
"_primary_term": 1
}Get a document and its source or stored fields from an index.
By default, this API is realtime and is not affected by the refresh rate of the index (when data will become visible for search).
In the case where stored fields are requested with the stored_fields parameter and the document has been updated but is not yet refreshed, the API will have to parse and analyze the source to extract the stored fields.
To turn off realtime behavior, set the realtime parameter to false.
Source filtering
By default, the API returns the contents of the _source field unless you have used the stored_fields parameter or the _source field is turned off.
You can turn off _source retrieval by using the _source parameter:
GET my-index-000001/_doc/0?_source=false
If you only need one or two fields from the _source, use the _source_includes or _source_excludes parameters to include or filter out particular fields.
This can be helpful with large documents where partial retrieval can save on network overhead
Both parameters take a comma separated list of fields or wildcard expressions.
For example:
GET my-index-000001/_doc/0?_source_includes=*.id&_source_excludes=entities
If you only want to specify includes, you can use a shorter notation:
GET my-index-000001/_doc/0?_source=*.id
Routing
If routing is used during indexing, the routing value also needs to be specified to retrieve a document. For example:
GET my-index-000001/_doc/2?routing=user1
This request gets the document with ID 2, but it is routed based on the user. The document is not fetched if the correct routing is not specified.
Distributed
The GET operation is hashed into a specific shard ID. It is then redirected to one of the replicas within that shard ID and returns the result. The replicas are the primary shard and its replicas within that shard ID group. This means that the more replicas you have, the better your GET scaling will be.
Versioning support
You can use the version parameter to retrieve the document only if its current version is equal to the specified one.
Internally, Elasticsearch has marked the old document as deleted and added an entirely new document. The old version of the document doesn't disappear immediately, although you won't be able to access it. Elasticsearch cleans up deleted documents in the background as you continue to index more data.
readThe node or shard the operation should be performed on. By default, the operation is randomized between the shard replicas.
If it is set to _local, the operation will prefer to be run on a local allocated shard when possible.
If it is set to a custom value, the value is used to guarantee that the same shards will be used for the same custom value.
This can help with "jumping values" when hitting different shards in different refresh states.
A sample value can be something like the web session ID or the user name.
If true, the request is real-time as opposed to near-real-time.
If true, the request refreshes the relevant shards before retrieving the document.
Setting it to true should be done after careful thought and verification that this does not cause a heavy load on the system (and slow down indexing).
A custom value used to route operations to a specific shard.
Indicates whether to return the _source field (true or false) or lists the fields to return.
A comma-separated list of source fields to exclude from the response.
You can also use this parameter to exclude fields from the subset specified in _source_includes query parameter.
If the _source parameter is false, this parameter is ignored.
Whether vectors should be excluded from _source
A comma-separated list of source fields to include in the response.
If this parameter is specified, only these source fields are returned.
You can exclude fields from this subset using the _source_excludes query parameter.
If the _source parameter is false, this parameter is ignored.
A comma-separated list of stored fields to return as part of a hit.
If no fields are specified, no stored fields are included in the response.
If this field is specified, the _source parameter defaults to false.
Only leaf fields can be retrieved with the stored_fields option.
Object fields can't be returned; if specified, the request fails.
The version number for concurrency control. It must match the current version of the document for the request to succeed.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
GET my-index-000001/_doc/1?stored_fields=tags,counter
resp = client.get(
index="my-index-000001",
id="1",
stored_fields="tags,counter",
)const response = await client.get({
index: "my-index-000001",
id: 1,
stored_fields: "tags,counter",
});response = client.get(
index: "my-index-000001",
id: "1",
stored_fields: "tags,counter"
)$resp = $client->get([
"index" => "my-index-000001",
"id" => "1",
"stored_fields" => "tags,counter",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001/_doc/1?stored_fields=tags,counter"{
"_index": "my-index-000001",
"_id": "0",
"_version": 1,
"_seq_no": 0,
"_primary_term": 1,
"found": true,
"_source": {
"@timestamp": "2099-11-15T14:12:12",
"http": {
"request": {
"method": "get"
},
"response": {
"status_code": 200,
"bytes": 1070000
},
"version": "1.1"
},
"source": {
"ip": "127.0.0.1"
},
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
}
}{
"_index": "my-index-000001",
"_id": "1",
"_version": 1,
"_seq_no" : 22,
"_primary_term" : 1,
"found": true,
"fields": {
"tags": [
"production"
]
}
}{
"_index": "my-index-000001",
"_id": "2",
"_version": 1,
"_seq_no" : 13,
"_primary_term" : 1,
"_routing": "user1",
"found": true,
"fields": {
"tags": [
"env2"
]
}
}All methods and paths for this operation:
Add a JSON document to the specified data stream or index and make it searchable. If the target is an index and the document already exists, the request updates the document and increments its version.
NOTE: You cannot use this API to send update requests for existing documents in a data stream.
If the Elasticsearch security features are enabled, you must have the following index privileges for the target data stream, index, or index alias:
PUT /<target>/_doc/<_id> request format, you must have the create, index, or write index privilege.POST /<target>/_doc/ request format, you must have the create_doc, create, index, or write index privilege.auto_configure, create_index, or manage index privilege.Automatic data stream creation requires a matching index template with data stream enabled.
NOTE: Replica shards might not all be started when an indexing operation returns successfully.
By default, only the primary is required. Set wait_for_active_shards to change this default behavior.
Automatically create data streams and indices
If the request's target doesn't exist and matches an index template with a data_stream definition, the index operation automatically creates the data stream.
If the target doesn't exist and doesn't match a data stream template, the operation automatically creates the index and applies any matching index templates.
NOTE: Elasticsearch includes several built-in index templates. To avoid naming collisions with these templates, refer to index pattern documentation.
If no mapping exists, the index operation creates a dynamic mapping. By default, new fields and objects are automatically added to the mapping if needed.
Automatic index creation is controlled by the action.auto_create_index setting.
If it is true, any index can be created automatically.
You can modify this setting to explicitly allow or block automatic creation of indices that match specified patterns or set it to false to turn off automatic index creation entirely.
Specify a comma-separated list of patterns you want to allow or prefix each pattern with + or - to indicate whether it should be allowed or blocked.
When a list is specified, the default behaviour is to disallow.
NOTE: The action.auto_create_index setting affects the automatic creation of indices only.
It does not affect the creation of data streams.
Optimistic concurrency control
Index operations can be made conditional and only be performed if the last modification to the document was assigned the sequence number and primary term specified by the if_seq_no and if_primary_term parameters.
If a mismatch is detected, the operation will result in a VersionConflictException and a status code of 409.
Routing
By default, shard placement — or routing — is controlled by using a hash of the document's ID value.
For more explicit control, the value fed into the hash function used by the router can be directly specified on a per-operation basis using the routing parameter.
When setting up explicit mapping, you can also use the _routing field to direct the index operation to extract the routing value from the document itself.
This does come at the (very minimal) cost of an additional document parsing pass.
If the _routing mapping is defined and set to be required, the index operation will fail if no routing value is provided or extracted.
NOTE: Data streams do not support custom routing unless they were created with the allow_custom_routing setting enabled in the template.
Distributed
The index operation is directed to the primary shard based on its route and performed on the actual node containing this shard. After the primary shard completes the operation, if needed, the update is distributed to applicable replicas.
Active shards
To improve the resiliency of writes to the system, indexing operations can be configured to wait for a certain number of active shard copies before proceeding with the operation.
If the requisite number of active shard copies are not available, then the write operation must wait and retry, until either the requisite shard copies have started or a timeout occurs.
By default, write operations only wait for the primary shards to be active before proceeding (that is to say wait_for_active_shards is 1).
This default can be overridden in the index settings dynamically by setting index.write.wait_for_active_shards.
To alter this behavior per operation, use the wait_for_active_shards request parameter.
Valid values are all or any positive integer up to the total number of configured copies per shard in the index (which is number_of_replicas+1).
Specifying a negative value or a number greater than the number of shard copies will throw an error.
For example, suppose you have a cluster of three nodes, A, B, and C and you create an index index with the number of replicas set to 3 (resulting in 4 shard copies, one more copy than there are nodes).
If you attempt an indexing operation, by default the operation will only ensure the primary copy of each shard is available before proceeding.
This means that even if B and C went down and A hosted the primary shard copies, the indexing operation would still proceed with only one copy of the data.
If wait_for_active_shards is set on the request to 3 (and all three nodes are up), the indexing operation will require 3 active shard copies before proceeding.
This requirement should be met because there are 3 active nodes in the cluster, each one holding a copy of the shard.
However, if you set wait_for_active_shards to all (or to 4, which is the same in this situation), the indexing operation will not proceed as you do not have all 4 copies of each shard active in the index.
The operation will timeout unless a new node is brought up in the cluster to host the fourth copy of the shard.
It is important to note that this setting greatly reduces the chances of the write operation not writing to the requisite number of shard copies, but it does not completely eliminate the possibility, because this check occurs before the write operation starts.
After the write operation is underway, it is still possible for replication to fail on any number of shard copies but still succeed on the primary.
The _shards section of the API response reveals the number of shard copies on which replication succeeded and failed.
No operation (noop) updates
When updating a document by using this API, a new version of the document is always created even if the document hasn't changed.
If this isn't acceptable use the _update API with detect_noop set to true.
The detect_noop option isn't available on this API because it doesn’t fetch the old source and isn't able to compare it against the new source.
There isn't a definitive rule for when noop updates aren't acceptable. It's a combination of lots of factors like how frequently your data source sends updates that are actually noops and how many queries per second Elasticsearch runs on the shard receiving the updates.
Versioning
Each indexed document is given a version number.
By default, internal versioning is used that starts at 1 and increments with each update, deletes included.
Optionally, the version number can be set to an external value (for example, if maintained in a database).
To enable this functionality, version_type should be set to external.
The value provided must be a numeric, long value greater than or equal to 0, and less than around 9.2e+18.
NOTE: Versioning is completely real time, and is not affected by the near real time aspects of search operations. If no version is provided, the operation runs without any version checks.
When using the external version type, the system checks to see if the version number passed to the index request is greater than the version of the currently stored document. If true, the document will be indexed and the new version number used. If the value provided is less than or equal to the stored document's version number, a version conflict will occur and the index operation will fail. For example:
PUT my-index-000001/_doc/1?version=2&version_type=external
{
"user": {
"id": "elkbee"
}
}
In this example, the operation will succeed since the supplied version of 2 is higher than the current document version of 1.
If the document was already updated and its version was set to 2 or higher, the indexing command will fail and result in a conflict (409 HTTP status code).
A nice side effect is that there is no need to maintain strict ordering of async indexing operations run as a result of changes to a source database, as long as version numbers from the source database are used.
Even the simple case of updating the Elasticsearch index using data from a database is simplified if external versioning is used, as only the latest version will be used if the index operations arrive out of order.
## Required authorization
* Index privileges: `index`
The name of the data stream or index to target.
If the target doesn't exist and matches the name or wildcard (*) pattern of an index template with a data_stream definition, this request creates the data stream.
If the target doesn't exist and doesn't match a data stream template, this request creates the index.
You can check for existing targets with the resolve index API.
A unique identifier for the document.
To automatically generate a document ID, use the POST /<target>/_doc/ request format and omit this parameter.
Only perform the operation if the document has this primary term.
Only perform the operation if the document has this sequence number.
True or false if to include the document source in the error message in case of parsing errors.
Set to create to only index the document if it does not already exist (put if absent).
If a document with the specified _id already exists, the indexing operation will fail.
The behavior is the same as using the <index>/_create endpoint.
If a document ID is specified, this paramater defaults to index.
Otherwise, it defaults to create.
If the request targets a data stream, an op_type of create is required.
Supported values include:
index: Overwrite any documents that already exist.create: Only index documents that do not already exist.Values are index or create.
The ID of the pipeline to use to preprocess incoming documents.
If the index has a default ingest pipeline specified, then setting the value to _none disables the default ingest pipeline for this request.
If a final pipeline is configured it will always run, regardless of the value of this parameter.
If true, Elasticsearch refreshes the affected shards to make this operation visible to search.
If wait_for, it waits for a refresh to make this operation visible to search.
If false, it does nothing with refreshes.
Values are true, false, or wait_for.
A custom value that is used to route operations to a specific shard.
The period the request waits for the following operations: automatic index creation, dynamic mapping updates, waiting for active shards.
This parameter is useful for situations where the primary shard assigned to perform the operation might not be available when the operation runs. Some reasons for this might be that the primary shard is currently recovering from a gateway or undergoing relocation. By default, the operation will wait on the primary shard to become available for at least 1 minute before failing and responding with an error. The actual wait time could be longer, particularly when multiple waits occur.
Values are -1 or 0.
An explicit version number for concurrency control. It must be a non-negative long number.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
The number of shard copies that must be active before proceeding with the operation.
You can set it to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
The default value of 1 means it waits for each primary shard to be active.
Values are all or index-setting.
If true, the destination must be an index alias.
If true, the request's actions must target a data stream (existing or to be created).
POST my-index-000001/_doc/
{
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
}resp = client.index(
index="my-index-000001",
document={
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
},
)const response = await client.index({
index: "my-index-000001",
document: {
"@timestamp": "2099-11-15T13:12:00",
message: "GET /search HTTP/1.1 200 1070000",
user: {
id: "kimchy",
},
},
});response = client.index(
index: "my-index-000001",
body: {
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
}
)$resp = $client->index([
"index" => "my-index-000001",
"body" => [
"@timestamp" => "2099-11-15T13:12:00",
"message" => "GET /search HTTP/1.1 200 1070000",
"user" => [
"id" => "kimchy",
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"@timestamp":"2099-11-15T13:12:00","message":"GET /search HTTP/1.1 200 1070000","user":{"id":"kimchy"}}' "$ELASTICSEARCH_URL/my-index-000001/_doc/"client.index(i -> i
.index("my-index-000001")
.document(JsonData.fromJson("{\"@timestamp\":\"2099-11-15T13:12:00\",\"message\":\"GET /search HTTP/1.1 200 1070000\",\"user\":{\"id\":\"kimchy\"}}"))
);
{
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
}{
"@timestamp": "2099-11-15T13:12:00",
"message": "GET /search HTTP/1.1 200 1070000",
"user": {
"id": "kimchy"
}
}{
"_shards": {
"total": 2,
"failed": 0,
"successful": 2
},
"_index": "my-index-000001",
"_id": "W0tpsmIBdwcYyG50zbta",
"_version": 1,
"_seq_no": 0,
"_primary_term": 1,
"result": "created"
}{
"_shards": {
"total": 2,
"failed": 0,
"successful": 2
},
"_index": "my-index-000001",
"_id": "1",
"_version": 1,
"_seq_no": 0,
"_primary_term": 1,
"result": "created"
}Remove a JSON document from the specified index.
NOTE: You cannot send deletion requests directly to a data stream. To delete a document in a data stream, you must target the backing index containing the document.
Optimistic concurrency control
Delete operations can be made conditional and only be performed if the last modification to the document was assigned the sequence number and primary term specified by the if_seq_no and if_primary_term parameters.
If a mismatch is detected, the operation will result in a VersionConflictException and a status code of 409.
Versioning
Each document indexed is versioned.
When deleting a document, the version can be specified to make sure the relevant document you are trying to delete is actually being deleted and it has not changed in the meantime.
Every write operation run on a document, deletes included, causes its version to be incremented.
The version number of a deleted document remains available for a short time after deletion to allow for control of concurrent operations.
The length of time for which a deleted document's version remains available is determined by the index.gc_deletes index setting.
Routing
If routing is used during indexing, the routing value also needs to be specified to delete a document.
If the _routing mapping is set to required and no routing value is specified, the delete API throws a RoutingMissingException and rejects the request.
For example:
DELETE /my-index-000001/_doc/1?routing=shard-1
This request deletes the document with ID 1, but it is routed based on the user. The document is not deleted if the correct routing is not specified.
Distributed
The delete operation gets hashed into a specific shard ID. It then gets redirected into the primary shard within that ID group and replicated (if needed) to shard replicas within that ID group.
deleteOnly perform the operation if the document has this primary term.
Only perform the operation if the document has this sequence number.
If true, Elasticsearch refreshes the affected shards to make this operation visible to search.
If wait_for, it waits for a refresh to make this operation visible to search.
If false, it does nothing with refreshes.
Values are true, false, or wait_for.
A custom value used to route operations to a specific shard.
The period to wait for active shards.
This parameter is useful for situations where the primary shard assigned to perform the delete operation might not be available when the delete operation runs. Some reasons for this might be that the primary shard is currently recovering from a store or undergoing relocation. By default, the delete operation will wait on the primary shard to become available for up to 1 minute before failing and responding with an error.
Values are -1 or 0.
An explicit version number for concurrency control. It must match the current version of the document for the request to succeed.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
The minimum number of shard copies that must be active before proceeding with the operation.
You can set it to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
The default value of 1 means it waits for each primary shard to be active.
Values are all or index-setting.
DELETE /my-index-000001/_doc/1
resp = client.delete(
index="my-index-000001",
id="1",
)const response = await client.delete({
index: "my-index-000001",
id: 1,
});response = client.delete(
index: "my-index-000001",
id: "1"
)$resp = $client->delete([
"index" => "my-index-000001",
"id" => "1",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001/_doc/1"client.delete(d -> d
.id("1")
.index("my-index-000001")
);
{
"_shards": {
"total": 2,
"failed": 0,
"successful": 2
},
"_index": "my-index-000001",
"_id": "1",
"_version": 2,
"_primary_term": 1,
"_seq_no": 5,
"result": "deleted"
}Verify that a document exists.
For example, check to see if a document with the _id 0 exists:
HEAD my-index-000001/_doc/0
If the document exists, the API returns a status code of 200 - OK.
If the document doesn’t exist, the API returns 404 - Not Found.
Versioning support
You can use the version parameter to check the document only if its current version is equal to the specified one.
Internally, Elasticsearch has marked the old document as deleted and added an entirely new document. The old version of the document doesn't disappear immediately, although you won't be able to access it. Elasticsearch cleans up deleted documents in the background as you continue to index more data.
A comma-separated list of data streams, indices, and aliases.
It supports wildcards (*).
A unique document identifier.
The node or shard the operation should be performed on. By default, the operation is randomized between the shard replicas.
If it is set to _local, the operation will prefer to be run on a local allocated shard when possible.
If it is set to a custom value, the value is used to guarantee that the same shards will be used for the same custom value.
This can help with "jumping values" when hitting different shards in different refresh states.
A sample value can be something like the web session ID or the user name.
If true, the request is real-time as opposed to near-real-time.
If true, the request refreshes the relevant shards before retrieving the document.
Setting it to true should be done after careful thought and verification that this does not cause a heavy load on the system (and slow down indexing).
A custom value used to route operations to a specific shard.
Indicates whether to return the _source field (true or false) or lists the fields to return.
A comma-separated list of source fields to exclude from the response.
You can also use this parameter to exclude fields from the subset specified in _source_includes query parameter.
If the _source parameter is false, this parameter is ignored.
A comma-separated list of source fields to include in the response.
If this parameter is specified, only these source fields are returned.
You can exclude fields from this subset using the _source_excludes query parameter.
If the _source parameter is false, this parameter is ignored.
A comma-separated list of stored fields to return as part of a hit.
If no fields are specified, no stored fields are included in the response.
If this field is specified, the _source parameter defaults to false.
Explicit version number for concurrency control. The specified version must match the current version of the document for the request to succeed.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
HEAD my-index-000001/_doc/0
resp = client.exists(
index="my-index-000001",
id="0",
)const response = await client.exists({
index: "my-index-000001",
id: 0,
});response = client.exists(
index: "my-index-000001",
id: "0"
)$resp = $client->exists([
"index" => "my-index-000001",
"id" => "0",
]);curl --head -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001/_doc/0"client.exists(e -> e
.id("0")
.index("my-index-000001")
);
Deletes documents that match the specified query.
If the Elasticsearch security features are enabled, you must have the following index privileges for the target data stream, index, or alias:
readdelete or writeYou can specify the query criteria in the request URI or the request body using the same syntax as the search API. When you submit a delete by query request, Elasticsearch gets a snapshot of the data stream or index when it begins processing the request and deletes matching documents using internal versioning. If a document changes between the time that the snapshot is taken and the delete operation is processed, it results in a version conflict and the delete operation fails.
NOTE: Documents with a version equal to 0 cannot be deleted using delete by query because internal versioning does not support 0 as a valid version number.
While processing a delete by query request, Elasticsearch performs multiple search requests sequentially to find all of the matching documents to delete. A bulk delete request is performed for each batch of matching documents. If a search or bulk request is rejected, the requests are retried up to 10 times, with exponential back off. If the maximum retry limit is reached, processing halts and all failed requests are returned in the response. Any delete requests that completed successfully still stick, they are not rolled back.
You can opt to count version conflicts instead of halting and returning by setting conflicts to proceed.
Note that if you opt to count version conflicts the operation could attempt to delete more documents from the source than max_docs until it has successfully deleted max_docs documents, or it has gone through every document in the source query.
Throttling delete requests
To control the rate at which delete by query issues batches of delete operations, you can set requests_per_second to any positive decimal number.
This pads each batch with a wait time to throttle the rate.
Set requests_per_second to -1 to disable throttling.
Throttling uses a wait time between batches so that the internal scroll requests can be given a timeout that takes the request padding into account.
The padding time is the difference between the batch size divided by the requests_per_second and the time spent writing.
By default the batch size is 1000, so if requests_per_second is set to 500:
target_time = 1000 / 500 per second = 2 seconds
wait_time = target_time - write_time = 2 seconds - .5 seconds = 1.5 seconds
Since the batch is issued as a single _bulk request, large batch sizes cause Elasticsearch to create many requests and wait before starting the next set.
This is "bursty" instead of "smooth".
Slicing
Delete by query supports sliced scroll to parallelize the delete process. This can improve efficiency and provide a convenient way to break the request down into smaller parts.
Setting slices to auto lets Elasticsearch choose the number of slices to use.
This setting will use one slice per shard, up to a certain limit.
If there are multiple source data streams or indices, it will choose the number of slices based on the index or backing index with the smallest number of shards.
Adding slices to the delete by query operation creates sub-requests which means it has some quirks:
slices will rethrottle the unfinished sub-request proportionally.slices will cancel each sub-request.slices each sub-request won't get a perfectly even portion of the documents. All documents will be addressed, but some slices may be larger than others. Expect larger slices to have a more even distribution.requests_per_second and max_docs on a request with slices are distributed proportionally to each sub-request. Combine that with the earlier point about distribution being uneven and you should conclude that using max_docs with slices might not result in exactly max_docs documents being deleted.If you're slicing manually or otherwise tuning automatic slicing, keep in mind that:
slices hurts performance. Setting slices higher than the number of shards generally does not improve efficiency and adds overhead.Whether query or delete performance dominates the runtime depends on the documents being reindexed and cluster resources.
Cancel a delete by query operation
Any delete by query can be canceled using the task cancel API. For example:
POST _tasks/r1A2WoRbTwKZ516z6NEs5A:36619/_cancel
The task ID can be found by using the get tasks API.
Cancellation should happen quickly but might take a few seconds. The get task status API will continue to list the delete by query task until this task checks that it has been cancelled and terminates itself.
read,deleteA comma-separated list of data streams, indices, and aliases to search.
It supports wildcards (*).
To search all data streams or indices, omit this parameter or use * or _all.
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 targeting foo*,bar* returns an error if an index starts with foo but no index starts with bar.
Analyzer to use for the query string.
This parameter can be used only when the q query string parameter is specified.
If true, wildcard and prefix queries are analyzed.
This parameter can be used only when the q query string parameter is specified.
What to do if delete by query hits version conflicts: abort or proceed.
Supported values include:
abort: Stop reindexing if there are conflicts.proceed: Continue reindexing even if there are conflicts.Values are abort or proceed.
The default operator for query string query: AND or OR.
This parameter can be used only when the q query string parameter is specified.
Values are and, AND, or, or OR.
The field to use as default where no field prefix is given in the query string.
This parameter can be used only when the q query string parameter is specified.
The 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.
It 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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Skips the specified number of documents.
If true, format-based query failures (such as providing text to a numeric field) in the query string will be ignored.
This parameter can be used only when the q query string parameter is specified.
The maximum number of documents to process.
Defaults to all documents.
When set to a value less then or equal to scroll_size, a scroll will not be used to retrieve the results for the operation.
The node or shard the operation should be performed on. It is random by default.
If true, Elasticsearch refreshes all shards involved in the delete by query after the request completes.
This is different than the delete API's refresh parameter, which causes just the shard that received the delete request to be refreshed.
Unlike the delete API, it does not support wait_for.
If true, the request cache is used for this request.
Defaults to the index-level setting.
The throttle for this request in sub-requests per second.
A custom value used to route operations to a specific shard.
A query in the Lucene query string syntax.
The period to retain the search context for scrolling.
Values are -1 or 0.
The size of the scroll request that powers the operation.
The explicit timeout for each search request. It defaults to no timeout.
Values are -1 or 0.
The type of the search operation.
Available options include query_then_fetch and dfs_query_then_fetch.
Supported values include:
query_then_fetch: Documents are scored using local term and document frequencies for the shard. This is usually faster but less accurate.dfs_query_then_fetch: Documents are scored using global term and document frequencies across all shards. This is usually slower but more accurate.Values are query_then_fetch or dfs_query_then_fetch.
The number of slices this task should be divided into.
Value is auto.
A comma-separated list of <field>:<direction> pairs.
The specific tag of the request for logging and statistical purposes.
The maximum number of documents to collect for each shard. If a query reaches this limit, Elasticsearch terminates the query early. Elasticsearch collects documents before sorting.
Use with caution. Elasticsearch applies this parameter to each shard handling the request. When possible, let Elasticsearch perform early termination automatically. Avoid specifying this parameter for requests that target data streams with backing indices across multiple data tiers.
The period each deletion request waits for active shards.
Values are -1 or 0.
If true, returns the document version as part of a hit.
The number of shard copies that must be active before proceeding with the operation.
Set to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
The timeout value controls how long each write request waits for unavailable shards to become available.
Values are all or index-setting.
If true, the request blocks until the operation is complete.
If false, Elasticsearch performs some preflight checks, launches the request, and returns a task you can use to cancel or get the status of the task. Elasticsearch creates a record of this task as a document at .tasks/task/${taskId}. When you are done with a task, you should delete the task document so Elasticsearch can reclaim the space.
The maximum number of documents to delete.
An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
POST /my-index-000001,my-index-000002/_delete_by_query
{
"query": {
"match_all": {}
}
}resp = client.delete_by_query(
index="my-index-000001,my-index-000002",
query={
"match_all": {}
},
)const response = await client.deleteByQuery({
index: "my-index-000001,my-index-000002",
query: {
match_all: {},
},
});response = client.delete_by_query(
index: "my-index-000001,my-index-000002",
body: {
"query": {
"match_all": {}
}
}
)$resp = $client->deleteByQuery([
"index" => "my-index-000001,my-index-000002",
"body" => [
"query" => [
"match_all" => new ArrayObject([]),
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"query":{"match_all":{}}}' "$ELASTICSEARCH_URL/my-index-000001,my-index-000002/_delete_by_query"client.deleteByQuery(d -> d
.index(List.of("my-index-000001","my-index-000002"))
.query(q -> q
.matchAll(m -> m)
)
);
{
"query": {
"match_all": {}
}
}{
"query": {
"term": {
"user.id": "kimchy"
}
},
"max_docs": 1
}{
"slice": {
"id": 0,
"max": 2
},
"query": {
"range": {
"http.response.bytes": {
"lt": 2000000
}
}
}
}{
"query": {
"range": {
"http.response.bytes": {
"lt": 2000000
}
}
}
}{
"took" : 147,
"timed_out": false,
"total": 119,
"deleted": 119,
"batches": 1,
"version_conflicts": 0,
"noops": 0,
"retries": {
"bulk": 0,
"search": 0
},
"throttled_millis": 0,
"requests_per_second": -1.0,
"throttled_until_millis": 0,
"failures" : [ ]
}Get the source of a document. For example:
GET my-index-000001/_source/1
You can use the source filtering parameters to control which parts of the _source are returned:
GET my-index-000001/_source/1/?_source_includes=*.id&_source_excludes=entities
readThe node or shard the operation should be performed on. By default, the operation is randomized between the shard replicas.
If true, the request is real-time as opposed to near-real-time.
If true, the request refreshes the relevant shards before retrieving the document.
Setting it to true should be done after careful thought and verification that this does not cause a heavy load on the system (and slow down indexing).
A custom value used to route operations to a specific shard.
Indicates whether to return the _source field (true or false) or lists the fields to return.
A comma-separated list of source fields to exclude in the response.
A comma-separated list of source fields to include in the response.
The version number for concurrency control. It must match the current version of the document for the request to succeed.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
GET my-index-000001/_source/1
resp = client.get_source(
index="my-index-000001",
id="1",
)const response = await client.getSource({
index: "my-index-000001",
id: 1,
});response = client.get_source(
index: "my-index-000001",
id: "1"
)$resp = $client->getSource([
"index" => "my-index-000001",
"id" => "1",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001/_source/1"client.getSource(g -> g
.id("1")
.index("my-index-000001")
);
Check whether a document source exists in an index. For example:
HEAD my-index-000001/_source/1
A document's source is not available if it is disabled in the mapping.
readA comma-separated list of data streams, indices, and aliases.
It supports wildcards (*).
A unique identifier for the document.
The node or shard the operation should be performed on. By default, the operation is randomized between the shard replicas.
If true, the request is real-time as opposed to near-real-time.
If true, the request refreshes the relevant shards before retrieving the document.
Setting it to true should be done after careful thought and verification that this does not cause a heavy load on the system (and slow down indexing).
A custom value used to route operations to a specific shard.
Indicates whether to return the _source field (true or false) or lists the fields to return.
A comma-separated list of source fields to exclude in the response.
A comma-separated list of source fields to include in the response.
The version number for concurrency control. It must match the current version of the document for the request to succeed.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
HEAD my-index-000001/_source/1
resp = client.exists_source(
index="my-index-000001",
id="1",
)const response = await client.existsSource({
index: "my-index-000001",
id: 1,
});response = client.exists_source(
index: "my-index-000001",
id: "1"
)$resp = $client->existsSource([
"index" => "my-index-000001",
"id" => "1",
]);curl --head -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001/_source/1"client.existsSource(e -> e
.id("1")
.index("my-index-000001")
);
All methods and paths for this operation:
Get multiple JSON documents by ID from one or more indices. If you specify an index in the request URI, you only need to specify the document IDs in the request body. To ensure fast responses, this multi get (mget) API responds with partial results if one or more shards fail.
Filter source fields
By default, the _source field is returned for every document (if stored).
Use the _source and _source_include or source_exclude attributes to filter what fields are returned for a particular document.
You can include the _source, _source_includes, and _source_excludes query parameters in the request URI to specify the defaults to use when there are no per-document instructions.
Get stored fields
Use the stored_fields attribute to specify the set of stored fields you want to retrieve.
Any requested fields that are not stored are ignored.
You can include the stored_fields query parameter in the request URI to specify the defaults to use when there are no per-document instructions.
readName of the index to retrieve documents from when ids are specified, or when a document in the docs array does not specify an index.
Specifies the node or shard the operation should be performed on. Random by default.
If true, the request is real-time as opposed to near-real-time.
If true, the request refreshes relevant shards before retrieving documents.
Custom value used to route operations to a specific shard.
True or false to return the _source field or not, or a list of fields to return.
A comma-separated list of source fields to exclude from the response.
You can also use this parameter to exclude fields from the subset specified in _source_includes query parameter.
A comma-separated list of source fields to include in the response.
If this parameter is specified, only these source fields are returned. You can exclude fields from this subset using the _source_excludes query parameter.
If the _source parameter is false, this parameter is ignored.
If true, retrieves the document fields stored in the index rather than the document _source.
GET /my-index-000001/_mget
{
"docs": [
{
"_id": "1"
},
{
"_id": "2"
}
]
}resp = client.mget(
index="my-index-000001",
docs=[
{
"_id": "1"
},
{
"_id": "2"
}
],
)const response = await client.mget({
index: "my-index-000001",
docs: [
{
_id: "1",
},
{
_id: "2",
},
],
});response = client.mget(
index: "my-index-000001",
body: {
"docs": [
{
"_id": "1"
},
{
"_id": "2"
}
]
}
)$resp = $client->mget([
"index" => "my-index-000001",
"body" => [
"docs" => array(
[
"_id" => "1",
],
[
"_id" => "2",
],
),
],
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"docs":[{"_id":"1"},{"_id":"2"}]}' "$ELASTICSEARCH_URL/my-index-000001/_mget"client.mget(m -> m
.docs(List.of(MultiGetOperation.of(mu -> mu
.id("1")),MultiGetOperation.of(mu -> mu
.id("2"))))
.index("my-index-000001")
);
{
"docs": [
{
"_id": "1"
},
{
"_id": "2"
}
]
}{
"docs": [
{
"_index": "test",
"_id": "1",
"_source": false
},
{
"_index": "test",
"_id": "2",
"_source": [ "field3", "field4" ]
},
{
"_index": "test",
"_id": "3",
"_source": {
"include": [ "user" ],
"exclude": [ "user.location" ]
}
}
]
}{
"docs": [
{
"_index": "test",
"_id": "1",
"stored_fields": [ "field1", "field2" ]
},
{
"_index": "test",
"_id": "2",
"stored_fields": [ "field3", "field4" ]
}
]
}{
"docs": [
{
"_index": "test",
"_id": "1",
"routing": "key2"
},
{
"_index": "test",
"_id": "2"
}
]
}All methods and paths for this operation:
Get multiple term vectors with a single request.
You can specify existing documents by index and ID or provide artificial documents in the body of the request.
You can specify the index in the request body or request URI.
The response contains a docs array with all the fetched termvectors.
Each element has the structure provided by the termvectors API.
Artificial documents
You can also use mtermvectors to generate term vectors for artificial documents provided in the body of the request.
The mapping used is determined by the specified _index.
readA comma-separated list of documents ids. You must define ids as parameter or set "ids" or "docs" in the request body
A comma-separated list or wildcard expressions of fields to include in the statistics.
It is used as the default list unless a specific field list is provided in the completion_fields or fielddata_fields parameters.
If true, the response includes the document count, sum of document frequencies, and sum of total term frequencies.
If true, the response includes term offsets.
If true, the response includes term payloads.
If true, the response includes term positions.
The node or shard the operation should be performed on. It is random by default.
If true, the request is real-time as opposed to near-real-time.
A custom value used to route operations to a specific shard.
If true, the response includes term frequency and document frequency.
If true, returns the document version as part of a hit.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
POST /my-index-000001/_mtermvectors
{
"docs": [
{
"_id": "2",
"fields": [
"message"
],
"term_statistics": true
},
{
"_id": "1"
}
]
}resp = client.mtermvectors(
index="my-index-000001",
docs=[
{
"_id": "2",
"fields": [
"message"
],
"term_statistics": True
},
{
"_id": "1"
}
],
)const response = await client.mtermvectors({
index: "my-index-000001",
docs: [
{
_id: "2",
fields: ["message"],
term_statistics: true,
},
{
_id: "1",
},
],
});response = client.mtermvectors(
index: "my-index-000001",
body: {
"docs": [
{
"_id": "2",
"fields": [
"message"
],
"term_statistics": true
},
{
"_id": "1"
}
]
}
)$resp = $client->mtermvectors([
"index" => "my-index-000001",
"body" => [
"docs" => array(
[
"_id" => "2",
"fields" => array(
"message",
),
"term_statistics" => true,
],
[
"_id" => "1",
],
),
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"docs":[{"_id":"2","fields":["message"],"term_statistics":true},{"_id":"1"}]}' "$ELASTICSEARCH_URL/my-index-000001/_mtermvectors"client.mtermvectors(m -> m
.docs(List.of(MultiTermVectorsOperation.of(mu -> mu
.id("2")
.fields("message")
.termStatistics(true)),MultiTermVectorsOperation.of(mu -> mu
.id("1"))))
.index("my-index-000001")
);
{
"docs": [
{
"_id": "2",
"fields": [
"message"
],
"term_statistics": true
},
{
"_id": "1"
}
]
}{
"ids": [ "1", "2" ],
"fields": [
"message"
],
"term_statistics": true
}{
"docs": [
{
"_index": "my-index-000001",
"doc" : {
"message" : "test test test"
}
},
{
"_index": "my-index-000001",
"doc" : {
"message" : "Another test ..."
}
}
]
}Copy documents from a source to a destination. You can copy all documents to the destination index or reindex a subset of the documents. The source can be any existing index, alias, or data stream. The destination must differ from the source. For example, you cannot reindex a data stream into itself.
IMPORTANT: Reindex requires _source to be enabled for all documents in the source.
The destination should be configured as wanted before calling the reindex API.
Reindex does not copy the settings from the source or its associated template.
Mappings, shard counts, and replicas, for example, must be configured ahead of time.
If the Elasticsearch security features are enabled, you must have the following security privileges:
read index privilege for the source data stream, index, or alias.write index privilege for the destination data stream, index, or index alias.auto_configure, create_index, or manage index privilege for the destination data stream, index, or alias.source.remote.user must have the monitor cluster privilege and the read index privilege for the source data stream, index, or alias.If reindexing from a remote cluster, you must explicitly allow the remote host in the reindex.remote.whitelist setting.
Automatic data stream creation requires a matching index template with data stream enabled.
The dest element can be configured like the index API to control optimistic concurrency control.
Omitting version_type or setting it to internal causes Elasticsearch to blindly dump documents into the destination, overwriting any that happen to have the same ID.
Setting version_type to external causes Elasticsearch to preserve the version from the source, create any documents that are missing, and update any documents that have an older version in the destination than they do in the source.
Setting op_type to create causes the reindex API to create only missing documents in the destination.
All existing documents will cause a version conflict.
IMPORTANT: Because data streams are append-only, any reindex request to a destination data stream must have an op_type of create.
A reindex can only add new documents to a destination data stream.
It cannot update existing documents in a destination data stream.
By default, version conflicts abort the reindex process.
To continue reindexing if there are conflicts, set the conflicts request body property to proceed.
In this case, the response includes a count of the version conflicts that were encountered.
Note that the handling of other error types is unaffected by the conflicts property.
Additionally, if you opt to count version conflicts, the operation could attempt to reindex more documents from the source than max_docs until it has successfully indexed max_docs documents into the target or it has gone through every document in the source query.
Refer to the linked documentation for examples of how to reindex documents.
read,writeIf true, the request refreshes affected shards to make this operation visible to search.
The throttle for this request in sub-requests per second. By default, there is no throttle.
The period of time that a consistent view of the index should be maintained for scrolled search.
Values are -1 or 0.
The number of slices this task should be divided into. It defaults to one slice, which means the task isn't sliced into subtasks.
Reindex supports sliced scroll to parallelize the reindexing process. This parallelization can improve efficiency and provide a convenient way to break the request down into smaller parts.
NOTE: Reindexing from remote clusters does not support manual or automatic slicing.
If set to auto, Elasticsearch chooses the number of slices to use.
This setting will use one slice per shard, up to a certain limit.
If there are multiple sources, it will choose the number of slices based on the index or backing index with the smallest number of shards.
Value is auto.
The period each indexing waits for automatic index creation, dynamic mapping updates, and waiting for active shards. By default, Elasticsearch waits for at least one minute before failing. The actual wait time could be longer, particularly when multiple waits occur.
Values are -1 or 0.
The number of shard copies that must be active before proceeding with the operation.
Set it to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
The default value is one, which means it waits for each primary shard to be active.
Values are all or index-setting.
If true, the request blocks until the operation is complete.
If true, the destination must be an index alias.
Values are abort or proceed.
The maximum number of documents to reindex.
By default, all documents are reindexed.
If it is a value less then or equal to scroll_size, a scroll will not be used to retrieve the results for the operation.
If conflicts is set to proceed, the reindex operation could attempt to reindex more documents from the source than max_docs until it has successfully indexed max_docs documents into the target or it has gone through every document in the source query.
POST _reindex
{
"source": {
"index": ["my-index-000001", "my-index-000002"]
},
"dest": {
"index": "my-new-index-000002"
}
}resp = client.reindex(
source={
"index": [
"my-index-000001",
"my-index-000002"
]
},
dest={
"index": "my-new-index-000002"
},
)const response = await client.reindex({
source: {
index: ["my-index-000001", "my-index-000002"],
},
dest: {
index: "my-new-index-000002",
},
});response = client.reindex(
body: {
"source": {
"index": [
"my-index-000001",
"my-index-000002"
]
},
"dest": {
"index": "my-new-index-000002"
}
}
)$resp = $client->reindex([
"body" => [
"source" => [
"index" => array(
"my-index-000001",
"my-index-000002",
),
],
"dest" => [
"index" => "my-new-index-000002",
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"source":{"index":["my-index-000001","my-index-000002"]},"dest":{"index":"my-new-index-000002"}}' "$ELASTICSEARCH_URL/_reindex"client.reindex(r -> r
.dest(d -> d
.index("my-new-index-000002")
)
.source(s -> s
.index(List.of("my-index-000001","my-index-000002"))
)
);
{
"source": {
"index": ["my-index-000001", "my-index-000002"]
},
"dest": {
"index": "my-new-index-000002"
}
}{
"source": {
"index": "metricbeat-*"
},
"dest": {
"index": "metricbeat"
},
"script": {
"lang": "painless",
"source": "ctx._index = 'metricbeat-' + (ctx._index.substring('metricbeat-'.length(), ctx._index.length())) + '-1'"
}
}{
"max_docs": 10,
"source": {
"index": "my-index-000001",
"query": {
"function_score" : {
"random_score" : {},
"min_score" : 0.9
}
}
},
"dest": {
"index": "my-new-index-000001"
}
}{
"source": {
"index": "my-index-000001"
},
"dest": {
"index": "my-new-index-000001",
"version_type": "external"
},
"script": {
"source": "if (ctx._source.foo == 'bar') {ctx._version++; ctx._source.remove('foo')}",
"lang": "painless"
}
}{
"source": {
"remote": {
"host": "http://otherhost:9200",
"username": "user",
"password": "pass"
},
"index": "my-index-000001",
"query": {
"match": {
"test": "data"
}
}
},
"dest": {
"index": "my-new-index-000001"
}
}{
"source": {
"index": "my-index-000001",
"slice": {
"id": 0,
"max": 2
}
},
"dest": {
"index": "my-new-index-000001"
}
}{
"source": {
"index": "my-index-000001"
},
"dest": {
"index": "my-new-index-000001"
}
}{
"source": {
"index": "source",
"query": {
"match": {
"company": "cat"
}
}
},
"dest": {
"index": "dest",
"routing": "=cat"
}
}{
"source": {
"index": "source"
},
"dest": {
"index": "dest",
"pipeline": "some_ingest_pipeline"
}
}{
"source": {
"index": "my-index-000001",
"query": {
"term": {
"user.id": "kimchy"
}
}
},
"dest": {
"index": "my-new-index-000001"
}
}{
"max_docs": 1,
"source": {
"index": "my-index-000001"
},
"dest": {
"index": "my-new-index-000001"
}
}{
"source": {
"index": "my-index-000001",
"_source": ["user.id", "_doc"]
},
"dest": {
"index": "my-new-index-000001"
}
}{
"source": {
"index": "my-index-000001"
},
"dest": {
"index": "my-new-index-000001"
},
"script": {
"source": "ctx._source.tag = ctx._source.remove(\"flag\")"
}
}All methods and paths for this operation:
Get information and statistics about terms in the fields of a particular document.
You can retrieve term vectors for documents stored in the index or for artificial documents passed in the body of the request.
You can specify the fields you are interested in through the fields parameter or by adding the fields to the request body.
For example:
GET /my-index-000001/_termvectors/1?fields=message
Fields can be specified using wildcards, similar to the multi match query.
Term vectors are real-time by default, not near real-time.
This can be changed by setting realtime parameter to false.
You can request three types of values: term information, term statistics, and field statistics. By default, all term information and field statistics are returned for all fields but term statistics are excluded.
Term information
positions: true)offsets: true)payloads: true), as base64 encoded bytesIf the requested information wasn't stored in the index, it will be computed on the fly if possible. Additionally, term vectors could be computed for documents not even existing in the index, but instead provided by the user.
Start and end offsets assume UTF-16 encoding is being used. If you want to use these offsets in order to get the original text that produced this token, you should make sure that the string you are taking a sub-string of is also encoded using UTF-16.
Behaviour
The term and field statistics are not accurate.
Deleted documents are not taken into account.
The information is only retrieved for the shard the requested document resides in.
The term and field statistics are therefore only useful as relative measures whereas the absolute numbers have no meaning in this context.
By default, when requesting term vectors of artificial documents, a shard to get the statistics from is randomly selected.
Use routing only to hit a particular shard.
Refer to the linked documentation for detailed examples of how to use this API.
readThe name of the index that contains the document.
A unique identifier for the document.
A comma-separated list or wildcard expressions of fields to include in the statistics.
It is used as the default list unless a specific field list is provided in the completion_fields or fielddata_fields parameters.
If true, the response includes:
If true, the response includes term offsets.
If true, the response includes term payloads.
If true, the response includes term positions.
The node or shard the operation should be performed on. It is random by default.
If true, the request is real-time as opposed to near-real-time.
A custom value that is used to route operations to a specific shard.
If true, the response includes:
By default these values are not returned since term statistics can have a serious performance impact.
If true, returns the document version as part of a hit.
The version type.
Supported values include:
internal: Use internal versioning that starts at 1 and increments with each update or delete.external: Only index the document if the specified version is strictly higher than the version of the stored document or if there is no existing document.external_gte: Only index the document if the specified version is equal or higher than the version of the stored document or if there is no existing document.
NOTE: The external_gte version type is meant for special use cases and should be used with care.
If used incorrectly, it can result in loss of data.force: This option is deprecated because it can cause primary and replica shards to diverge.Values are internal, external, external_gte, or force.
An artificial document (a document not present in the index) for which you want to retrieve term vectors.
Override the default per-field analyzer. This is useful in order to generate term vectors in any fashion, especially when using artificial documents. When providing an analyzer for a field that already stores term vectors, the term vectors will be regenerated.
If true, the response includes:
Default value is true.
If true, the response includes term offsets.
Default value is true.
If true, the response includes term payloads.
Default value is true.
If true, the response includes term positions.
Default value is true.
If true, the response includes:
By default these values are not returned since term statistics can have a serious performance impact.
Default value is false.
Values are internal, external, external_gte, or force.
GET /my-index-000001/_termvectors/1
{
"fields" : ["text"],
"offsets" : true,
"payloads" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}resp = client.termvectors(
index="my-index-000001",
id="1",
fields=[
"text"
],
offsets=True,
payloads=True,
positions=True,
term_statistics=True,
field_statistics=True,
)const response = await client.termvectors({
index: "my-index-000001",
id: 1,
fields: ["text"],
offsets: true,
payloads: true,
positions: true,
term_statistics: true,
field_statistics: true,
});response = client.termvectors(
index: "my-index-000001",
id: "1",
body: {
"fields": [
"text"
],
"offsets": true,
"payloads": true,
"positions": true,
"term_statistics": true,
"field_statistics": true
}
)$resp = $client->termvectors([
"index" => "my-index-000001",
"id" => "1",
"body" => [
"fields" => array(
"text",
),
"offsets" => true,
"payloads" => true,
"positions" => true,
"term_statistics" => true,
"field_statistics" => true,
],
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"fields":["text"],"offsets":true,"payloads":true,"positions":true,"term_statistics":true,"field_statistics":true}' "$ELASTICSEARCH_URL/my-index-000001/_termvectors/1"client.termvectors(t -> t
.fieldStatistics(true)
.fields("text")
.id("1")
.index("my-index-000001")
.offsets(true)
.payloads(true)
.positions(true)
.termStatistics(true)
);
{
"fields" : ["text"],
"offsets" : true,
"payloads" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}{
"doc" : {
"fullname" : "John Doe",
"text" : "test test test"
},
"fields": ["fullname"],
"per_field_analyzer" : {
"fullname": "keyword"
}
}{
"doc": {
"plot": "When wealthy industrialist Tony Stark is forced to build an armored suit after a life-threatening incident, he ultimately decides to use its technology to fight against evil."
},
"term_statistics": true,
"field_statistics": true,
"positions": false,
"offsets": false,
"filter": {
"max_num_terms": 3,
"min_term_freq": 1,
"min_doc_freq": 1
}
}{
"fields" : ["text", "some_field_without_term_vectors"],
"offsets" : true,
"positions" : true,
"term_statistics" : true,
"field_statistics" : true
}{
"doc" : {
"fullname" : "John Doe",
"text" : "test test test"
}
}{
"_index": "my-index-000001",
"_id": "1",
"_version": 1,
"found": true,
"took": 6,
"term_vectors": {
"text": {
"field_statistics": {
"sum_doc_freq": 4,
"doc_count": 2,
"sum_ttf": 6
},
"terms": {
"test": {
"doc_freq": 2,
"ttf": 4,
"term_freq": 3,
"tokens": [
{
"position": 0,
"start_offset": 0,
"end_offset": 4,
"payload": "d29yZA=="
},
{
"position": 1,
"start_offset": 5,
"end_offset": 9,
"payload": "d29yZA=="
},
{
"position": 2,
"start_offset": 10,
"end_offset": 14,
"payload": "d29yZA=="
}
]
}
}
}
}
}{
"_index": "my-index-000001",
"_version": 0,
"found": true,
"took": 6,
"term_vectors": {
"fullname": {
"field_statistics": {
"sum_doc_freq": 2,
"doc_count": 4,
"sum_ttf": 4
},
"terms": {
"John Doe": {
"term_freq": 1,
"tokens": [
{
"position": 0,
"start_offset": 0,
"end_offset": 8
}
]
}
}
}
}
}{
"_index": "imdb",
"_version": 0,
"found": true,
"term_vectors": {
"plot": {
"field_statistics": {
"sum_doc_freq": 3384269,
"doc_count": 176214,
"sum_ttf": 3753460
},
"terms": {
"armored": {
"doc_freq": 27,
"ttf": 27,
"term_freq": 1,
"score": 9.74725
},
"industrialist": {
"doc_freq": 88,
"ttf": 88,
"term_freq": 1,
"score": 8.590818
},
"stark": {
"doc_freq": 44,
"ttf": 47,
"term_freq": 1,
"score": 9.272792
}
}
}
}
}Update a document by running a script or passing a partial document.
If the Elasticsearch security features are enabled, you must have the index or write index privilege for the target index or index alias.
The script can update, delete, or skip modifying the document. The API also supports passing a partial document, which is merged into the existing document. To fully replace an existing document, use the index API. This operation:
The document must still be reindexed, but using this API removes some network roundtrips and reduces chances of version conflicts between the GET and the index operation.
The _source field must be enabled to use this API.
In addition to _source, you can access the following variables through the ctx map: _index, _type, _id, _version, _routing, and _now (the current timestamp).
For usage examples such as partial updates, upserts, and scripted updates, see the External documentation.
writeThe name of the target index. By default, the index is created automatically if it doesn't exist.
A unique identifier for the document to be updated.
Only perform the operation if the document has this primary term.
Only perform the operation if the document has this sequence number.
True or false if to include the document source in the error message in case of parsing errors.
The script language.
If 'true', Elasticsearch refreshes the affected shards to make this operation visible to search. If 'wait_for', it waits for a refresh to make this operation visible to search. If 'false', it does nothing with refreshes.
Values are true, false, or wait_for.
If true, the destination must be an index alias.
The number of times the operation should be retried when a conflict occurs.
A custom value used to route operations to a specific shard.
The period to wait for the following operations: dynamic mapping updates and waiting for active shards. Elasticsearch waits for at least the timeout period before failing. The actual wait time could be longer, particularly when multiple waits occur.
Values are -1 or 0.
The number of copies of each shard that must be active before proceeding with the operation.
Set to 'all' or any positive integer up to the total number of shards in the index (number_of_replicas+1).
The default value of 1 means it waits for each primary shard to be active.
Values are all or index-setting.
If false, source retrieval is turned off.
You can also specify a comma-separated list of the fields you want to retrieve.
The source fields you want to exclude.
The source fields you want to retrieve.
If true, the result in the response is set to noop (no operation) when there are no changes to the document.
Default value is true.
A partial update to an existing document.
If both doc and script are specified, doc is ignored.
If true, use the contents of 'doc' as the value of 'upsert'.
NOTE: Using ingest pipelines with doc_as_upsert is not supported.
Default value is false.
If true, run the script whether or not the document exists.
Default value is false.
If the document does not already exist, the contents of 'upsert' are inserted as a new document. If the document exists, the 'script' is run.
POST test/_update/1
{
"script" : {
"source": "ctx._source.counter += params.count",
"lang": "painless",
"params" : {
"count" : 4
}
}
}resp = client.update(
index="test",
id="1",
script={
"source": "ctx._source.counter += params.count",
"lang": "painless",
"params": {
"count": 4
}
},
)const response = await client.update({
index: "test",
id: 1,
script: {
source: "ctx._source.counter += params.count",
lang: "painless",
params: {
count: 4,
},
},
});response = client.update(
index: "test",
id: "1",
body: {
"script": {
"source": "ctx._source.counter += params.count",
"lang": "painless",
"params": {
"count": 4
}
}
}
)$resp = $client->update([
"index" => "test",
"id" => "1",
"body" => [
"script" => [
"source" => "ctx._source.counter += params.count",
"lang" => "painless",
"params" => [
"count" => 4,
],
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"script":{"source":"ctx._source.counter += params.count","lang":"painless","params":{"count":4}}}' "$ELASTICSEARCH_URL/test/_update/1"client.update(u -> u
.id("1")
.index("test")
.script(s -> s
.source(so -> so
.scriptString("ctx._source.counter += params.count")
)
.params("count", JsonData.fromJson("4"))
.lang("painless")
)
,Void.class);
{
"script" : {
"source": "ctx._source.counter += params.count",
"lang": "painless",
"params" : {
"count" : 4
}
}
}{
"scripted_upsert": true,
"script": {
"source": """
if ( ctx.op == 'create' ) {
ctx._source.counter = params.count
} else {
ctx._source.counter += params.count
}
""",
"params": {
"count": 4
}
},
"upsert": {}
}{
"doc": {
"name": "new_name"
},
"doc_as_upsert": true
}{
"script": {
"source": "ctx._source.tags.add(params.tag)",
"lang": "painless",
"params": {
"tag": "blue"
}
}
}{
"script": {
"source": "if (ctx._source.tags.contains(params.tag)) { ctx._source.tags.remove(ctx._source.tags.indexOf(params.tag)) }",
"lang": "painless",
"params": {
"tag": "blue"
}
}
}{
"script" : "ctx._source.new_field = 'value_of_new_field'"
}{
"script" : "ctx._source.remove('new_field')"
}{
"script": "ctx._source['my-object'].remove('my-subfield')"
}{
"script": {
"source": "if (ctx._source.tags.contains(params.tag)) { ctx.op = 'delete' } else { ctx.op = 'noop' }",
"lang": "painless",
"params": {
"tag": "green"
}
}
}{
"doc": {
"name": "new_name"
}
}{
"script": {
"source": "ctx._source.counter += params.count",
"lang": "painless",
"params": {
"count": 4
}
},
"upsert": {
"counter": 1
}
}{
"_shards": {
"total": 0,
"successful": 0,
"failed": 0
},
"_index": "test",
"_id": "1",
"_version": 2,
"_primary_term": 1,
"_seq_no": 1,
"result": "noop"
}Updates documents that match the specified query. If no query is specified, performs an update on every document in the data stream or index without modifying the source, which is useful for picking up mapping changes.
If the Elasticsearch security features are enabled, you must have the following index privileges for the target data stream, index, or alias:
readindex or writeYou can specify the query criteria in the request URI or the request body using the same syntax as the search API.
When you submit an update by query request, Elasticsearch gets a snapshot of the data stream or index when it begins processing the request and updates matching documents using internal versioning.
When the versions match, the document is updated and the version number is incremented.
If a document changes between the time that the snapshot is taken and the update operation is processed, it results in a version conflict and the operation fails.
You can opt to count version conflicts instead of halting and returning by setting conflicts to proceed.
Note that if you opt to count version conflicts, the operation could attempt to update more documents from the source than max_docs until it has successfully updated max_docs documents or it has gone through every document in the source query.
NOTE: Documents with a version equal to 0 cannot be updated using update by query because internal versioning does not support 0 as a valid version number.
While processing an update by query request, Elasticsearch performs multiple search requests sequentially to find all of the matching documents. A bulk update request is performed for each batch of matching documents. Any query or update failures cause the update by query request to fail and the failures are shown in the response. Any update requests that completed successfully still stick, they are not rolled back.
Refreshing shards
Specifying the refresh parameter refreshes all shards once the request completes.
This is different to the update API's refresh parameter, which causes only the shard
that received the request to be refreshed. Unlike the update API, it does not support
wait_for.
Running update by query asynchronously
If the request contains wait_for_completion=false, Elasticsearch
performs some preflight checks, launches the request, and returns a
task you can use to cancel or get the status of the task.
Elasticsearch creates a record of this task as a document at .tasks/task/${taskId}.
Waiting for active shards
wait_for_active_shards controls how many copies of a shard must be active
before proceeding with the request. See wait_for_active_shards
for details. timeout controls how long each write request waits for unavailable
shards to become available. Both work exactly the way they work in the
Bulk API. Update by query uses scrolled searches, so you can also
specify the scroll parameter to control how long it keeps the search context
alive, for example ?scroll=10m. The default is 5 minutes.
Throttling update requests
To control the rate at which update by query issues batches of update operations, you can set requests_per_second to any positive decimal number.
This pads each batch with a wait time to throttle the rate.
Set requests_per_second to -1 to turn off throttling.
Throttling uses a wait time between batches so that the internal scroll requests can be given a timeout that takes the request padding into account.
The padding time is the difference between the batch size divided by the requests_per_second and the time spent writing.
By default the batch size is 1000, so if requests_per_second is set to 500:
target_time = 1000 / 500 per second = 2 seconds
wait_time = target_time - write_time = 2 seconds - .5 seconds = 1.5 seconds
Since the batch is issued as a single _bulk request, large batch sizes cause Elasticsearch to create many requests and wait before starting the next set. This is "bursty" instead of "smooth".
Slicing
Update by query supports sliced scroll to parallelize the update process. This can improve efficiency and provide a convenient way to break the request down into smaller parts.
Setting slices to auto chooses a reasonable number for most data streams and indices.
This setting will use one slice per shard, up to a certain limit.
If there are multiple source data streams or indices, it will choose the number of slices based on the index or backing index with the smallest number of shards.
Adding slices to _update_by_query just automates the manual process of creating sub-requests, which means it has some quirks:
slices only contains the status of completed slices.slices will rethrottle the unfinished sub-request proportionally.requests_per_second and max_docs on a request with slices are distributed proportionally to each sub-request. Combine that with the point above about distribution being uneven and you should conclude that using max_docs with slices might not result in exactly max_docs documents being updated.If you're slicing manually or otherwise tuning automatic slicing, keep in mind that:
Whether query or update performance dominates the runtime depends on the documents being reindexed and cluster resources.
Refer to the linked documentation for examples of how to update documents using the _update_by_query API:
read,writeA comma-separated list of data streams, indices, and aliases to search.
It supports wildcards (*).
To search all data streams or indices, omit this parameter or use * or _all.
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 targeting foo*,bar* returns an error if an index starts with foo but no index starts with bar.
The analyzer to use for the query string.
This parameter can be used only when the q query string parameter is specified.
If true, wildcard and prefix queries are analyzed.
This parameter can be used only when the q query string parameter is specified.
The preferred behavior when update by query hits version conflicts: abort or proceed.
Supported values include:
abort: Stop reindexing if there are conflicts.proceed: Continue reindexing even if there are conflicts.Values are abort or proceed.
The default operator for query string query: AND or OR.
This parameter can be used only when the q query string parameter is specified.
Values are and, AND, or, or OR.
The field to use as default where no field prefix is given in the query string.
This parameter can be used only when the q query string parameter is specified.
The 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.
It 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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Skips the specified number of documents.
If true, format-based query failures (such as providing text to a numeric field) in the query string will be ignored.
This parameter can be used only when the q query string parameter is specified.
The maximum number of documents to process.
It defaults to all documents.
When set to a value less then or equal to scroll_size then a scroll will not be used to retrieve the results for the operation.
The ID of the pipeline to use to preprocess incoming documents.
If the index has a default ingest pipeline specified, then setting the value to _none disables the default ingest pipeline for this request.
If a final pipeline is configured it will always run, regardless of the value of this parameter.
The node or shard the operation should be performed on. It is random by default.
A query in the Lucene query string syntax.
If true, Elasticsearch refreshes affected shards to make the operation visible to search after the request completes.
This is different than the update API's refresh parameter, which causes just the shard that received the request to be refreshed.
If true, the request cache is used for this request.
It defaults to the index-level setting.
The throttle for this request in sub-requests per second.
A custom value used to route operations to a specific shard.
The period to retain the search context for scrolling.
Values are -1 or 0.
The size of the scroll request that powers the operation.
An explicit timeout for each search request. By default, there is no timeout.
Values are -1 or 0.
The type of the search operation. Available options include query_then_fetch and dfs_query_then_fetch.
Supported values include:
query_then_fetch: Documents are scored using local term and document frequencies for the shard. This is usually faster but less accurate.dfs_query_then_fetch: Documents are scored using global term and document frequencies across all shards. This is usually slower but more accurate.Values are query_then_fetch or dfs_query_then_fetch.
The number of slices this task should be divided into.
Value is auto.
A comma-separated list of : pairs.
The specific tag of the request for logging and statistical purposes.
The maximum number of documents to collect for each shard. If a query reaches this limit, Elasticsearch terminates the query early. Elasticsearch collects documents before sorting.
IMPORTANT: Use with caution. Elasticsearch applies this parameter to each shard handling the request. When possible, let Elasticsearch perform early termination automatically. Avoid specifying this parameter for requests that target data streams with backing indices across multiple data tiers.
The period each update request waits for the following operations: dynamic mapping updates, waiting for active shards. By default, it is one minute. This guarantees Elasticsearch waits for at least the timeout before failing. The actual wait time could be longer, particularly when multiple waits occur.
Values are -1 or 0.
If true, returns the document version as part of a hit.
Should the document increment the version number (internal) on hit or not (reindex)
The number of shard copies that must be active before proceeding with the operation.
Set to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
The timeout parameter controls how long each write request waits for unavailable shards to become available.
Both work exactly the way they work in the bulk API.
Values are all or index-setting.
If true, the request blocks until the operation is complete.
If false, Elasticsearch performs some preflight checks, launches the request, and returns a task ID that you can use to cancel or get the status of the task.
Elasticsearch creates a record of this task as a document at .tasks/task/${taskId}.
The maximum number of documents to update.
An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
Values are abort or proceed.
POST my-index-000001/_update_by_query?conflicts=proceed
{
"query": {
"term": {
"user.id": "kimchy"
}
}
}resp = client.update_by_query(
index="my-index-000001",
conflicts="proceed",
query={
"term": {
"user.id": "kimchy"
}
},
)const response = await client.updateByQuery({
index: "my-index-000001",
conflicts: "proceed",
query: {
term: {
"user.id": "kimchy",
},
},
});response = client.update_by_query(
index: "my-index-000001",
conflicts: "proceed",
body: {
"query": {
"term": {
"user.id": "kimchy"
}
}
}
)$resp = $client->updateByQuery([
"index" => "my-index-000001",
"conflicts" => "proceed",
"body" => [
"query" => [
"term" => [
"user.id" => "kimchy",
],
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"query":{"term":{"user.id":"kimchy"}}}' "$ELASTICSEARCH_URL/my-index-000001/_update_by_query?conflicts=proceed"client.updateByQuery(u -> u
.conflicts(Conflicts.Proceed)
.index("my-index-000001")
.query(q -> q
.term(t -> t
.field("user.id")
.value(FieldValue.of("kimchy"))
)
)
);
{
"query": {
"term": {
"user.id": "kimchy"
}
}
}{
"script": {
"source": "ctx._source.count++",
"lang": "painless"
},
"query": {
"term": {
"user.id": "kimchy"
}
}
}{
"slice": {
"id": 0,
"max": 2
},
"script": {
"source": "ctx._source['extra'] = 'test'"
}
}{
"script": {
"source": "ctx._source['extra'] = 'test'"
}
}All methods and paths for this operation:
Returns information about an enrich policy.
Comma-separated list of enrich policy names used to limit the request. To return information for all enrich policies, omit this parameter.
GET /_enrich/policy/my-policy
resp = client.enrich.get_policy(
name="my-policy",
)const response = await client.enrich.getPolicy({
name: "my-policy",
});response = client.enrich.get_policy(
name: "my-policy"
)$resp = $client->enrich()->getPolicy([
"name" => "my-policy",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_enrich/policy/my-policy"client.enrich().getPolicy(g -> g
.name("my-policy")
);
PUT /_enrich/policy/postal_policy
{
"geo_match": {
"indices": "postal_codes",
"match_field": "location",
"enrich_fields": [ "location", "postal_code" ]
}
}resp = client.enrich.put_policy(
name="postal_policy",
geo_match={
"indices": "postal_codes",
"match_field": "location",
"enrich_fields": [
"location",
"postal_code"
]
},
)const response = await client.enrich.putPolicy({
name: "postal_policy",
geo_match: {
indices: "postal_codes",
match_field: "location",
enrich_fields: ["location", "postal_code"],
},
});response = client.enrich.put_policy(
name: "postal_policy",
body: {
"geo_match": {
"indices": "postal_codes",
"match_field": "location",
"enrich_fields": [
"location",
"postal_code"
]
}
}
)$resp = $client->enrich()->putPolicy([
"name" => "postal_policy",
"body" => [
"geo_match" => [
"indices" => "postal_codes",
"match_field" => "location",
"enrich_fields" => array(
"location",
"postal_code",
),
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"geo_match":{"indices":"postal_codes","match_field":"location","enrich_fields":["location","postal_code"]}}' "$ELASTICSEARCH_URL/_enrich/policy/postal_policy"client.enrich().putPolicy(p -> p
.geoMatch(g -> g
.enrichFields(List.of("location","postal_code"))
.indices("postal_codes")
.matchField("location")
)
.name("postal_policy")
);
{
"geo_match": {
"indices": "postal_codes",
"match_field": "location",
"enrich_fields": [ "location", "postal_code" ]
}
}Deletes an existing enrich policy and its enrich index.
DELETE /_enrich/policy/my-policy
resp = client.enrich.delete_policy(
name="my-policy",
)const response = await client.enrich.deletePolicy({
name: "my-policy",
});response = client.enrich.delete_policy(
name: "my-policy"
)$resp = $client->enrich()->deletePolicy([
"name" => "my-policy",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_enrich/policy/my-policy"client.enrich().deletePolicy(d -> d
.name("my-policy")
);
Create the enrich index for an existing enrich policy.
PUT /_enrich/policy/my-policy/_execute?wait_for_completion=false
resp = client.enrich.execute_policy(
name="my-policy",
wait_for_completion=False,
)const response = await client.enrich.executePolicy({
name: "my-policy",
wait_for_completion: "false",
});response = client.enrich.execute_policy(
name: "my-policy",
wait_for_completion: "false"
)$resp = $client->enrich()->executePolicy([
"name" => "my-policy",
"wait_for_completion" => "false",
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_enrich/policy/my-policy/_execute?wait_for_completion=false"client.enrich().executePolicy(e -> e
.name("my-policy")
.waitForCompletion(false)
);
Event Query Language (EQL) is a query language for event-based time series data, such as logs, metrics, and traces.
Get the current status and available results for an async EQL search or a stored synchronous EQL search.
Period for which the search and its results are stored on the cluster. Defaults to the keep_alive value set by the search’s EQL search API request.
Values are -1 or 0.
Timeout duration to wait for the request to finish. Defaults to no timeout, meaning the request waits for complete search results.
Values are -1 or 0.
GET /_eql/search/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=?wait_for_completion_timeout=2s
resp = client.eql.get(
id="FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
wait_for_completion_timeout="2s",
)const response = await client.eql.get({
id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
wait_for_completion_timeout: "2s",
});response = client.eql.get(
id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
wait_for_completion_timeout: "2s"
)$resp = $client->eql()->get([
"id" => "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
"wait_for_completion_timeout" => "2s",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_eql/search/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=?wait_for_completion_timeout=2s"client.eql().get(g -> g
.id("FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=")
.waitForCompletionTimeout(w -> w
.offset(2)
)
);
Delete an async EQL search or a stored synchronous EQL search. The API also deletes results for the search.
DELETE /_eql/search/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=
resp = client.eql.delete(
id="FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
)const response = await client.eql.delete({
id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
});response = client.eql.delete(
id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE="
)$resp = $client->eql()->delete([
"id" => "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_eql/search/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE="client.eql().delete(d -> d
.id("FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=")
);
GET /_eql/search/status/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=
resp = client.eql.get_status(
id="FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
)const response = await client.eql.getStatus({
id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
});response = client.eql.get_status(
id: "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE="
)$resp = $client->eql()->getStatus([
"id" => "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_eql/search/status/FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE="client.eql().getStatus(g -> g
.id("FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=")
);
{
"id": "FmNJRUZ1YWZCU3dHY1BIOUhaenVSRkEaaXFlZ3h4c1RTWFNocDdnY2FSaERnUTozNDE=",
"is_running" : true,
"is_partial" : true,
"start_time_in_millis" : 1611690235000,
"expiration_time_in_millis" : 1611690295000
}All methods and paths for this operation:
Returns search results for an Event Query Language (EQL) query. EQL assumes each document in a data stream or index corresponds to an event.
If true, returns partial results if there are shard failures. If false, returns an error with no partial results.
If true, sequence queries will return partial results in case of shard failures. If false, they will return no results at all. This flag has effect only if allow_partial_search_results is true.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Period for which the search and its results are stored on the cluster.
Values are -1 or 0.
If true, the search and its results are stored on the cluster.
Timeout duration to wait for the request to finish. Defaults to no timeout, meaning the request waits for complete search results.
Values are -1 or 0.
EQL query you wish to run.
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
Query, written in Query DSL, used to filter the events on which the EQL query runs.
An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and
d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and
d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
Allow query execution also in case of shard failures. If true, the query will keep running and will return results based on the available shards. For sequences, the behavior can be further refined using allow_partial_sequence_results
Default value is true.
This flag applies only to sequences and has effect only if allow_partial_search_results=true. If true, the sequence query will return results based on the available shards, ignoring the others. If false, the sequence query will return successfully, but will always have empty results.
Default value is false.
Values are tail or head.
By default, the response of a sample query contains up to 10 samples, with one sample per unique set of join keys. Use the size
parameter to get a smaller or larger set of samples. To retrieve more than one sample per set of join keys, use the
max_samples_per_key parameter. Pipes are not supported for sample queries.
Default value is 1.
GET /my-data-stream/_eql/search
{
"query": """
process where (process.name == "cmd.exe" and process.pid != 2013)
"""
}resp = client.eql.search(
index="my-data-stream",
query="\n process where (process.name == \"cmd.exe\" and process.pid != 2013)\n ",
)const response = await client.eql.search({
index: "my-data-stream",
query:
'\n process where (process.name == "cmd.exe" and process.pid != 2013)\n ',
});response = client.eql.search(
index: "my-data-stream",
body: {
"query": "\n process where (process.name == \"cmd.exe\" and process.pid != 2013)\n "
}
)$resp = $client->eql()->search([
"index" => "my-data-stream",
"body" => [
"query" => "\n process where (process.name == \"cmd.exe\" and process.pid != 2013)\n ",
],
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"query":"\n process where (process.name == \"cmd.exe\" and process.pid != 2013)\n "}' "$ELASTICSEARCH_URL/my-data-stream/_eql/search"client.eql().search(s -> s
.index("my-data-stream")
.query(" process where (process.name == \"cmd.exe\" and process.pid != 2013) ")
);
{
"query": """
process where (process.name == "cmd.exe" and process.pid != 2013)
"""
}{
"query": """
sequence by process.pid
[ file where file.name == "cmd.exe" and process.pid != 2013 ]
[ process where stringContains(process.executable, "regsvr32") ]
"""
}{
"is_partial": false,
"is_running": false,
"took": 6,
"timed_out": false,
"hits": {
"total": {
"value": 1,
"relation": "eq"
},
"sequences": [
{
"join_keys": [
2012
],
"events": [
{
"_index": ".ds-my-data-stream-2099.12.07-000001",
"_id": "AtOJ4UjUBAAx3XR5kcCM",
"_source": {
"@timestamp": "2099-12-06T11:04:07.000Z",
"event": {
"category": "file",
"id": "dGCHwoeS",
"sequence": 2
},
"file": {
"accessed": "2099-12-07T11:07:08.000Z",
"name": "cmd.exe",
"path": "C:\\Windows\\System32\\cmd.exe",
"type": "file",
"size": 16384
},
"process": {
"pid": 2012,
"name": "cmd.exe",
"executable": "C:\\Windows\\System32\\cmd.exe"
}
}
},
{
"_index": ".ds-my-data-stream-2099.12.07-000001",
"_id": "OQmfCaduce8zoHT93o4H",
"_source": {
"@timestamp": "2099-12-07T11:07:09.000Z",
"event": {
"category": "process",
"id": "aR3NWVOs",
"sequence": 4
},
"process": {
"pid": 2012,
"name": "regsvr32.exe",
"command_line": "regsvr32.exe /s /u /i:https://...RegSvr32.sct scrobj.dll",
"executable": "C:\\Windows\\System32\\regsvr32.exe"
}
}
}
]
}
]
}
}The Elasticsearch Query Language (ES|QL) provides a powerful way to filter, transform, and analyze data stored in Elasticsearch, and in the future in other runtimes.
curl \
--request GET 'http://api.example.com/_query/queries/{id}' \
--header "Authorization: $API_KEY"curl \
--request GET 'http://api.example.com/_query/queries' \
--header "Authorization: $API_KEY"Get search results for an ES|QL (Elasticsearch query language) query.
A short version of the Accept header, e.g. json, yaml.
Values are csv, json, tsv, txt, yaml, cbor, smile, or arrow.
The character to use between values within a CSV row. Only valid for the CSV format.
Should columns that are entirely null be removed from the columns and values portion of the results?
Defaults to false. If true then the response will include an extra section under the name all_columns which has the name of all columns.
If true, partial results will be returned if there are shard failures, but the query can continue to execute on other clusters and shards.
If false, the query will fail if there are any failures.
To override the default behavior, you can set the esql.query.allow_partial_results cluster setting to false.
By default, ES|QL returns results as rows. For example, FROM returns each individual document as one row. For the JSON, YAML, CBOR and smile formats, ES|QL can return the results in a columnar fashion where one row represents all the values of a certain column in the results.
An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
To avoid any attempts of hacking or code injection, extract the values in a separate list of parameters. Use question mark placeholders (?) in the query string for each of the parameters.
If provided and true the response will include an extra profile object
with information on how the query was executed. This information is for human debugging
and its format can change at any time but it can give some insight into the performance
of each part of the query.
The ES|QL query API accepts an ES|QL query string in the query parameter, runs it, and returns the results.
Tables to use with the LOOKUP operation. The top level key is the table name and the next level key is the column name.
When set to true and performing a cross-cluster query, the response will include an extra _clusters
object with information about the clusters that participated in the search along with info such as shards
count.
Default value is false.
POST /_query
{
"query": """
FROM library,remote-*:library
| EVAL year = DATE_TRUNC(1 YEARS, release_date)
| STATS MAX(page_count) BY year
| SORT year
| LIMIT 5
""",
"include_ccs_metadata": true
}resp = client.esql.query(
query="\n FROM library,remote-*:library\n | EVAL year = DATE_TRUNC(1 YEARS, release_date)\n | STATS MAX(page_count) BY year\n | SORT year\n | LIMIT 5\n ",
include_ccs_metadata=True,
)const response = await client.esql.query({
query:
"\n FROM library,remote-*:library\n | EVAL year = DATE_TRUNC(1 YEARS, release_date)\n | STATS MAX(page_count) BY year\n | SORT year\n | LIMIT 5\n ",
include_ccs_metadata: true,
});response = client.esql.query(
body: {
"query": "\n FROM library,remote-*:library\n | EVAL year = DATE_TRUNC(1 YEARS, release_date)\n | STATS MAX(page_count) BY year\n | SORT year\n | LIMIT 5\n ",
"include_ccs_metadata": true
}
)$resp = $client->esql()->query([
"body" => [
"query" => "\n FROM library,remote-*:library\n | EVAL year = DATE_TRUNC(1 YEARS, release_date)\n | STATS MAX(page_count) BY year\n | SORT year\n | LIMIT 5\n ",
"include_ccs_metadata" => true,
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"query":"\n FROM library,remote-*:library\n | EVAL year = DATE_TRUNC(1 YEARS, release_date)\n | STATS MAX(page_count) BY year\n | SORT year\n | LIMIT 5\n ","include_ccs_metadata":true}' "$ELASTICSEARCH_URL/_query"client.esql().query(q -> q
.includeCcsMetadata(true)
.query(" FROM library,remote-*:library | EVAL year = DATE_TRUNC(1 YEARS, release_date) | STATS MAX(page_count) BY year | SORT year | LIMIT 5 ")
);
{
"query": """
FROM library,remote-*:library
| EVAL year = DATE_TRUNC(1 YEARS, release_date)
| STATS MAX(page_count) BY year
| SORT year
| LIMIT 5
""",
"include_ccs_metadata": true
}The graph explore API enables you to extract and summarize information about the documents and terms in an Elasticsearch data stream or index.
All methods and paths for this operation:
Extract and summarize information about the documents and terms in an Elasticsearch data stream or index.
The easiest way to understand the behavior of this API is to use the Graph UI to explore connections.
An initial request to the _explore API contains a seed query that identifies the documents of interest and specifies the fields that define the vertices and connections you want to include in the graph.
Subsequent requests enable you to spider out from one more vertices of interest.
You can exclude vertices that have already been returned.
Custom value used to route operations to a specific shard.
Specifies the period of time to wait for a response from each shard. If no response is received before the timeout expires, the request fails and returns an error. Defaults to no timeout.
Values are -1 or 0.
An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
Specifies one or more fields that contain the terms you want to include in the graph as vertices.
POST clicklogs/_graph/explore
{
"query": {
"match": {
"query.raw": "midi"
}
},
"vertices": [
{
"field": "product"
}
],
"connections": {
"vertices": [
{
"field": "query.raw"
}
]
}
}resp = client.graph.explore(
index="clicklogs",
query={
"match": {
"query.raw": "midi"
}
},
vertices=[
{
"field": "product"
}
],
connections={
"vertices": [
{
"field": "query.raw"
}
]
},
)const response = await client.graph.explore({
index: "clicklogs",
query: {
match: {
"query.raw": "midi",
},
},
vertices: [
{
field: "product",
},
],
connections: {
vertices: [
{
field: "query.raw",
},
],
},
});response = client.graph.explore(
index: "clicklogs",
body: {
"query": {
"match": {
"query.raw": "midi"
}
},
"vertices": [
{
"field": "product"
}
],
"connections": {
"vertices": [
{
"field": "query.raw"
}
]
}
}
)$resp = $client->graph()->explore([
"index" => "clicklogs",
"body" => [
"query" => [
"match" => [
"query.raw" => "midi",
],
],
"vertices" => array(
[
"field" => "product",
],
),
"connections" => [
"vertices" => array(
[
"field" => "query.raw",
],
),
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"query":{"match":{"query.raw":"midi"}},"vertices":[{"field":"product"}],"connections":{"vertices":[{"field":"query.raw"}]}}' "$ELASTICSEARCH_URL/clicklogs/_graph/explore"client.graph().explore(e -> e
.connections(c -> c
.vertices(v -> v
.field("query.raw")
)
)
.index("clicklogs")
.query(q -> q
.match(m -> m
.field("query.raw")
.query(FieldValue.of("midi"))
)
)
.vertices(v -> v
.field("product")
)
);
{
"query": {
"match": {
"query.raw": "midi"
}
},
"vertices": [
{
"field": "product"
}
],
"connections": {
"vertices": [
{
"field": "query.raw"
}
]
}
}Comma-separated list of component template names used to limit the request.
Wildcard (*) expressions are supported.
If true, returns settings in flat format.
Filter out results, for example to filter out sensitive information. Supports wildcards or full settings keys
Return all default configurations for the component template (default: false)
If true, the request retrieves information from the local node only.
If false, information is retrieved from the master node.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
GET /_component_template/template_1
resp = client.cluster.get_component_template(
name="template_1",
)const response = await client.cluster.getComponentTemplate({
name: "template_1",
});response = client.cluster.get_component_template(
name: "template_1"
)$resp = $client->cluster()->getComponentTemplate([
"name" => "template_1",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_component_template/template_1"client.cluster().getComponentTemplate(g -> g
.name("template_1")
);
All methods and paths for this operation:
Component templates are building blocks for constructing index templates that specify index mappings, settings, and aliases.
An index template can be composed of multiple component templates.
To use a component template, specify it in an index template’s composed_of list.
Component templates are only applied to new data streams and indices as part of a matching index template.
Settings and mappings specified directly in the index template or the create index request override any settings or mappings specified in a component template.
Component templates are only used during index creation. For data streams, this includes data stream creation and the creation of a stream’s backing indices. Changes to component templates do not affect existing indices, including a stream’s backing indices.
You can use C-style /* *\/ block comments in component templates.
You can include comments anywhere in the request body except before the opening curly bracket.
Applying component templates
You cannot directly apply a component template to a data stream or index.
To be applied, a component template must be included in an index template's composed_of list.
manage_index_templatesName of the component template to create.
Elasticsearch includes the following built-in component templates: logs-mappings; logs-settings; metrics-mappings; metrics-settings;synthetics-mapping; synthetics-settings.
Elastic Agent uses these templates to configure backing indices for its data streams.
If you use Elastic Agent and want to overwrite one of these templates, set the version for your replacement template higher than the current version.
If you don’t use Elastic Agent and want to disable all built-in component and index templates, set stack.templates.enabled to false using the cluster update settings API.
If true, this request cannot replace or update existing component templates.
User defined reason for create the component template.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
PUT _component_template/template_1
{
"template": null,
"settings": {
"number_of_shards": 1
},
"mappings": {
"_source": {
"enabled": false
},
"properties": {
"host_name": {
"type": "keyword"
},
"created_at": {
"type": "date",
"format": "EEE MMM dd HH:mm:ss Z yyyy"
}
}
}
}resp = client.cluster.put_component_template(
name="template_1",
template=None,
settings={
"number_of_shards": 1
},
mappings={
"_source": {
"enabled": False
},
"properties": {
"host_name": {
"type": "keyword"
},
"created_at": {
"type": "date",
"format": "EEE MMM dd HH:mm:ss Z yyyy"
}
}
},
)const response = await client.cluster.putComponentTemplate({
name: "template_1",
template: null,
settings: {
number_of_shards: 1,
},
mappings: {
_source: {
enabled: false,
},
properties: {
host_name: {
type: "keyword",
},
created_at: {
type: "date",
format: "EEE MMM dd HH:mm:ss Z yyyy",
},
},
},
});response = client.cluster.put_component_template(
name: "template_1",
body: {
"template": nil,
"settings": {
"number_of_shards": 1
},
"mappings": {
"_source": {
"enabled": false
},
"properties": {
"host_name": {
"type": "keyword"
},
"created_at": {
"type": "date",
"format": "EEE MMM dd HH:mm:ss Z yyyy"
}
}
}
}
)$resp = $client->cluster()->putComponentTemplate([
"name" => "template_1",
"body" => [
"template" => null,
"settings" => [
"number_of_shards" => 1,
],
"mappings" => [
"_source" => [
"enabled" => false,
],
"properties" => [
"host_name" => [
"type" => "keyword",
],
"created_at" => [
"type" => "date",
"format" => "EEE MMM dd HH:mm:ss Z yyyy",
],
],
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"template":null,"settings":{"number_of_shards":1},"mappings":{"_source":{"enabled":false},"properties":{"host_name":{"type":"keyword"},"created_at":{"type":"date","format":"EEE MMM dd HH:mm:ss Z yyyy"}}}}' "$ELASTICSEARCH_URL/_component_template/template_1"{
"template": null,
"settings": {
"number_of_shards": 1
},
"mappings": {
"_source": {
"enabled": false
},
"properties": {
"host_name": {
"type": "keyword"
},
"created_at": {
"type": "date",
"format": "EEE MMM dd HH:mm:ss Z yyyy"
}
}
}
}{
"template": null,
"settings": {
"number_of_shards": 1
},
"aliases": {
"alias1": {},
"alias2": {
"filter": {
"term": {
"user.id": "kimchy"
}
},
"routing": "shard-1"
},
"{index}-alias": {}
}
}Comma-separated list or wildcard expression of component template names used to limit the request.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
DELETE _component_template/template_1
resp = client.cluster.delete_component_template(
name="template_1",
)const response = await client.cluster.deleteComponentTemplate({
name: "template_1",
});response = client.cluster.delete_component_template(
name: "template_1"
)$resp = $client->cluster()->deleteComponentTemplate([
"name" => "template_1",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_component_template/template_1"client.cluster().deleteComponentTemplate(d -> d
.name("template_1")
);
Returns information about whether a particular component template exists.
Comma-separated list of component template names used to limit the request. Wildcard (*) expressions are supported.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
If true, the request retrieves information from the local node only. Defaults to false, which means information is retrieved from the master node.
curl \
--request HEAD 'http://api.example.com/_component_template/{name}' \
--header "Authorization: $API_KEY"Add an index block to an index. Index blocks limit the operations allowed on an index by blocking specific operation types.
A comma-separated list or wildcard expression of index names used to limit the request.
By default, you must explicitly name the indices you are adding blocks to.
To allow the adding of blocks to indices with _all, *, or other wildcard expressions, change the action.destructive_requires_name setting to false.
You can update this setting in the elasticsearch.yml file or by using the cluster update settings API.
The block type to add to the index.
Supported values include:
metadata: Disable metadata changes, such as closing the index.read: Disable read operations.read_only: Disable write operations and metadata changes.write: Disable write operations. However, metadata changes are still allowed.Values are metadata, read, read_only, or write.
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 targeting foo*,bar* returns an error if an index starts with foo but no index starts with bar.
The 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.
It 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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
The period to wait for the master node.
If the master node is not available before the timeout expires, the request fails and returns an error.
It can also be set to -1 to indicate that the request should never timeout.
Values are -1 or 0.
The period to wait for a response from all relevant nodes in the cluster after updating the cluster metadata.
If no response is received before the timeout expires, the cluster metadata update still applies but the response will indicate that it was not completely acknowledged.
It can also be set to -1 to indicate that the request should never timeout.
Values are -1 or 0.
PUT /my-index-000001/_block/write
resp = client.indices.add_block(
index="my-index-000001",
block="write",
)const response = await client.indices.addBlock({
index: "my-index-000001",
block: "write",
});response = client.indices.add_block(
index: "my-index-000001",
block: "write"
)$resp = $client->indices()->addBlock([
"index" => "my-index-000001",
"block" => "write",
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001/_block/write"client.indices().addBlock(a -> a
.block(IndicesBlockOptions.Write)
.index("my-index-000001")
);
{
"acknowledged" : true,
"shards_acknowledged" : true,
"indices" : [ {
"name" : "my-index-000001",
"blocked" : true
} ]
}A comma-separated list or wildcard expression of index names used to limit the request.
By default, you must explicitly name the indices you are removing blocks from.
To allow the removal of blocks from indices with _all, *, or other wildcard expressions, change the action.destructive_requires_name setting to false.
You can update this setting in the elasticsearch.yml file or by using the cluster update settings API.
The block type to remove from the index.
Supported values include:
metadata: Disable metadata changes, such as closing the index.read: Disable read operations.read_only: Disable write operations and metadata changes.write: Disable write operations. However, metadata changes are still allowed.Values are metadata, read, read_only, or write.
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 targeting foo*,bar* returns an error if an index starts with foo but no index starts with bar.
The 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.
It 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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
The period to wait for the master node.
If the master node is not available before the timeout expires, the request fails and returns an error.
It can also be set to -1 to indicate that the request should never timeout.
Values are -1 or 0.
The period to wait for a response from all relevant nodes in the cluster after updating the cluster metadata.
If no response is received before the timeout expires, the cluster metadata update still applies but the response will indicate that it was not completely acknowledged.
It can also be set to -1 to indicate that the request should never timeout.
Values are -1 or 0.
DELETE /my-index-000001/_block/write
resp = client.indices.remove_block(
index="my-index-000001",
block="write",
)const response = await client.indices.removeBlock({
index: "my-index-000001",
block: "write",
});response = client.indices.remove_block(
index: "my-index-000001",
block: "write"
)$resp = $client->indices()->removeBlock([
"index" => "my-index-000001",
"block" => "write",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001/_block/write"client.indices().removeBlock(a -> a
.block(IndicesBlockOptions.Write)
.index("my-index-000001")
);
{
"acknowledged" : true,
"indices" : [ {
"name" : "my-index-000001",
"unblocked" : true
} ]
}All methods and paths for this operation:
The analyze API performs analysis on a text string and returns the resulting tokens.
Generating excessive amount of tokens may cause a node to run out of memory.
The index.analyze.max_token_count setting enables you to limit the number of tokens that can be produced.
If more than this limit of tokens gets generated, an error occurs.
The _analyze endpoint without a specified index will always use 10000 as its limit.
indexIndex used to derive the analyzer.
If specified, the analyzer or field parameter overrides this value.
If no index is specified or the index does not have a default analyzer, the analyze API uses the standard analyzer.
Index used to derive the analyzer.
If specified, the analyzer or field parameter overrides this value.
If no index is specified or the index does not have a default analyzer, the analyze API uses the standard analyzer.
The name of the analyzer that should be applied to the provided text.
This could be a built-in analyzer, or an analyzer that’s been configured in the index.
Array of token attributes used to filter the output of the explain parameter.
Array of character filters used to preprocess characters before the tokenizer.
If true, the response includes token attributes and additional details.
Default value is false.
Path to field or array of paths. Some API's support wildcards in the path to select multiple fields.
Array of token filters used to apply after the tokenizer.
Normalizer to use to convert text into a single token.
GET /_analyze
{
"analyzer": "standard",
"text": "this is a test"
}resp = client.indices.analyze(
analyzer="standard",
text="this is a test",
)const response = await client.indices.analyze({
analyzer: "standard",
text: "this is a test",
});response = client.indices.analyze(
body: {
"analyzer": "standard",
"text": "this is a test"
}
)$resp = $client->indices()->analyze([
"body" => [
"analyzer" => "standard",
"text" => "this is a test",
],
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"analyzer":"standard","text":"this is a test"}' "$ELASTICSEARCH_URL/_analyze"client.indices().analyze(a -> a
.analyzer("standard")
.text("this is a test")
);
{
"analyzer": "standard",
"text": "this is a test"
}{
"analyzer": "standard",
"text": [
"this is a test",
"the second text"
]
}{
"tokenizer": "keyword",
"filter": [
"lowercase"
],
"char_filter": [
"html_strip"
],
"text": "this is a <b>test</b>"
}{
"tokenizer": "whitespace",
"filter": [
"lowercase",
{
"type": "stop",
"stopwords": [
"a",
"is",
"this"
]
}
],
"text": "this is a test"
}{
"field": "obj1.field1",
"text": "this is a test"
}{
"normalizer": "my_normalizer",
"text": "BaR"
}{
"tokenizer": "standard",
"filter": [
"snowball"
],
"text": "detailed output",
"explain": true,
"attributes": [
"keyword"
]
}{
"detail": {
"custom_analyzer": true,
"charfilters": [],
"tokenizer": {
"name": "standard",
"tokens": [
{
"token": "detailed",
"start_offset": 0,
"end_offset": 8,
"type": "<ALPHANUM>",
"position": 0
},
{
"token": "output",
"start_offset": 9,
"end_offset": 15,
"type": "<ALPHANUM>",
"position": 1
}
]
},
"tokenfilters": [
{
"name": "snowball",
"tokens": [
{
"token": "detail",
"start_offset": 0,
"end_offset": 8,
"type": "<ALPHANUM>",
"position": 0,
"keyword": false
},
{
"token": "output",
"start_offset": 9,
"end_offset": 15,
"type": "<ALPHANUM>",
"position": 1,
"keyword": false
}
]
}
]
}
}Comma-separated list of data streams, indices, and index aliases used to limit the request. Wildcard expressions (*) are supported.
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 targeting foo*,bar* returns an error if an index starts with foo but no index starts with bar.
Type of index that wildcard expressions 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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, returns settings in flat format.
If true, return all default settings in the response.
If true, the request retrieves information from the local node only. Defaults to false, which means information is retrieved from the master node.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Return only information on specified index features
Supported values include: aliases, mappings, settings
Values are aliases, mappings, or settings.
GET /my-index-000001
resp = client.indices.get(
index="my-index-000001",
)const response = await client.indices.get({
index: "my-index-000001",
});response = client.indices.get(
index: "my-index-000001"
)$resp = $client->indices()->get([
"index" => "my-index-000001",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001"client.indices().get(g -> g
.index("my-index-000001")
);
You can use the create index API to add a new index to an Elasticsearch cluster. When creating an index, you can specify the following:
Wait for active shards
By default, index creation will only return a response to the client when the primary copies of each shard have been started, or the request times out.
The index creation response will indicate what happened.
For example, acknowledged indicates whether the index was successfully created in the cluster, while shards_acknowledged indicates whether the requisite number of shard copies were started for each shard in the index before timing out.
Note that it is still possible for either acknowledged or shards_acknowledged to be false, but for the index creation to be successful.
These values simply indicate whether the operation completed before the timeout.
If acknowledged is false, the request timed out before the cluster state was updated with the newly created index, but it probably will be created sometime soon.
If shards_acknowledged is false, then the request timed out before the requisite number of shards were started (by default just the primaries), even if the cluster state was successfully updated to reflect the newly created index (that is to say, acknowledged is true).
You can change the default of only waiting for the primary shards to start through the index setting index.write.wait_for_active_shards.
Note that changing this setting will also affect the wait_for_active_shards value on all subsequent write operations.
create_index,manageName of the index you wish to create. Index names must meet the following criteria:
\, /, *, ?, ", <, >, |, ,, or #:), but that has been deprecated and will not be supported in later versions-, _, or +. or ... are deprecated, except for hidden indices and internal indices managed by pluginsPeriod to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
The number of shard copies that must be active before proceeding with the operation.
Set to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
Values are all or index-setting.
Aliases for the index.
PUT /my-index-000001
{
"settings": {
"number_of_shards": 3,
"number_of_replicas": 2
}
}resp = client.indices.create(
index="my-index-000001",
settings={
"number_of_shards": 3,
"number_of_replicas": 2
},
)const response = await client.indices.create({
index: "my-index-000001",
settings: {
number_of_shards: 3,
number_of_replicas: 2,
},
});response = client.indices.create(
index: "my-index-000001",
body: {
"settings": {
"number_of_shards": 3,
"number_of_replicas": 2
}
}
)$resp = $client->indices()->create([
"index" => "my-index-000001",
"body" => [
"settings" => [
"number_of_shards" => 3,
"number_of_replicas" => 2,
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"settings":{"number_of_shards":3,"number_of_replicas":2}}' "$ELASTICSEARCH_URL/my-index-000001"client.indices().create(c -> c
.index("my-index-000001")
.settings(s -> s
.numberOfShards("3")
.numberOfReplicas("2")
)
);
{
"settings": {
"number_of_shards": 3,
"number_of_replicas": 2
}
}{
"settings": {
"number_of_shards": 1
},
"mappings": {
"properties": {
"field1": { "type": "text" }
}
}
}{
"aliases": {
"alias_1": {},
"alias_2": {
"filter": {
"term": {
"user.id": "kimchy"
}
},
"routing": "shard-1"
}
}
}Deleting an index deletes its documents, shards, and metadata. It does not delete related Kibana components, such as data views, visualizations, or dashboards.
You cannot delete the current write index of a data stream. To delete the index, you must roll over the data stream so a new write index is created. You can then use the delete index API to delete the previous write index.
delete_indexComma-separated list of indices to delete.
You cannot specify index aliases.
By default, this parameter does not support wildcards (*) or _all.
To use wildcards or _all, set the action.destructive_requires_name cluster setting to false.
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.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
DELETE /books
resp = client.indices.delete(
index="books",
)const response = await client.indices.delete({
index: "books",
});response = client.indices.delete(
index: "books"
)$resp = $client->indices()->delete([
"index" => "books",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/books"client.indices().delete(d -> d
.index("books")
);
Check if one or more indices, index aliases, or data streams exist.
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.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, returns settings in flat format.
If true, return all default settings in the response.
If true, the request retrieves information from the local node only.
HEAD my-data-stream
resp = client.indices.exists(
index="my-data-stream",
)const response = await client.indices.exists({
index: "my-data-stream",
});response = client.indices.exists(
index: "my-data-stream"
)$resp = $client->indices()->exists([
"index" => "my-data-stream",
]);curl --head -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-data-stream"client.indices().exists(e -> e
.index("my-data-stream")
);
All methods and paths for this operation:
Adds a data stream or index to an alias.
Comma-separated list of data streams or indices to add.
Supports wildcards (*).
Wildcard patterns that match both data streams and indices return an error.
Alias to update. If the alias doesn’t exist, the request creates it. Index alias names support date math.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
If true, sets the write index or data stream for the alias.
If an alias points to multiple indices or data streams and is_write_index isn’t set, the alias rejects write requests.
If an index alias points to one index and is_write_index isn’t set, the index automatically acts as the write index.
Data stream aliases don’t automatically set a write data stream, even if the alias points to one data stream.
POST _aliases
{
"actions": [
{
"add": {
"index": "my-data-stream",
"alias": "my-alias"
}
}
]
}resp = client.indices.update_aliases(
actions=[
{
"add": {
"index": "my-data-stream",
"alias": "my-alias"
}
}
],
)const response = await client.indices.updateAliases({
actions: [
{
add: {
index: "my-data-stream",
alias: "my-alias",
},
},
],
});response = client.indices.update_aliases(
body: {
"actions": [
{
"add": {
"index": "my-data-stream",
"alias": "my-alias"
}
}
]
}
)$resp = $client->indices()->updateAliases([
"body" => [
"actions" => array(
[
"add" => [
"index" => "my-data-stream",
"alias" => "my-alias",
],
],
),
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"actions":[{"add":{"index":"my-data-stream","alias":"my-alias"}}]}' "$ELASTICSEARCH_URL/_aliases"client.indices().updateAliases(u -> u
.actions(a -> a
.add(ad -> ad
.alias("my-alias")
.index("my-data-stream")
)
)
);
{
"actions": [
{
"add": {
"index": "my-data-stream",
"alias": "my-alias"
}
}
]
}Comma-separated list of data streams or indices used to limit the request.
Supports wildcards (*).
Comma-separated list of aliases to remove.
Supports wildcards (*). To remove all aliases, use * or _all.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
DELETE my-data-stream/_alias/my-alias
resp = client.indices.delete_alias(
index="my-data-stream",
name="my-alias",
)const response = await client.indices.deleteAlias({
index: "my-data-stream",
name: "my-alias",
});response = client.indices.delete_alias(
index: "my-data-stream",
name: "my-alias"
)$resp = $client->indices()->deleteAlias([
"index" => "my-data-stream",
"name" => "my-alias",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-data-stream/_alias/my-alias"client.indices().deleteAlias(d -> d
.index("my-data-stream")
.name("my-alias")
);
Comma-separated list of index template names used to limit the request. Wildcard (*) expressions are supported.
If true, the request retrieves information from the local node only. Defaults to false, which means information is retrieved from the master node.
If true, returns settings in flat format.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
If true, returns all relevant default configurations for the index template.
GET _index_template/*?filter_path=index_templates.name,index_templates.index_template.index_patterns,index_templates.index_template.data_stream
resp = client.indices.get_index_template(
name="*",
filter_path="index_templates.name,index_templates.index_template.index_patterns,index_templates.index_template.data_stream",
)const response = await client.indices.getIndexTemplate({
name: "*",
filter_path:
"index_templates.name,index_templates.index_template.index_patterns,index_templates.index_template.data_stream",
});response = client.indices.get_index_template(
name: "*",
filter_path: "index_templates.name,index_templates.index_template.index_patterns,index_templates.index_template.data_stream"
)$resp = $client->indices()->getIndexTemplate([
"name" => "*",
"filter_path" => "index_templates.name,index_templates.index_template.index_patterns,index_templates.index_template.data_stream",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_index_template/*?filter_path=index_templates.name,index_templates.index_template.index_patterns,index_templates.index_template.data_stream"All methods and paths for this operation:
Index templates define settings, mappings, and aliases that can be applied automatically to new indices.
Elasticsearch applies templates to new indices based on an wildcard pattern that matches the index name. Index templates are applied during data stream or index creation. For data streams, these settings and mappings are applied when the stream's backing indices are created. Settings and mappings specified in a create index API request override any settings or mappings specified in an index template. Changes to index templates do not affect existing indices, including the existing backing indices of a data stream.
You can use C-style /* *\/ block comments in index templates.
You can include comments anywhere in the request body, except before the opening curly bracket.
Multiple matching templates
If multiple index templates match the name of a new index or data stream, the template with the highest priority is used.
Multiple templates with overlapping index patterns at the same priority are not allowed and an error will be thrown when attempting to create a template matching an existing index template at identical priorities.
Composing aliases, mappings, and settings
When multiple component templates are specified in the composed_of field for an index template, they are merged in the order specified, meaning that later component templates override earlier component templates.
Any mappings, settings, or aliases from the parent index template are merged in next.
Finally, any configuration on the index request itself is merged.
Mapping definitions are merged recursively, which means that later mapping components can introduce new field mappings and update the mapping configuration.
If a field mapping is already contained in an earlier component, its definition will be completely overwritten by the later one.
This recursive merging strategy applies not only to field mappings, but also root options like dynamic_templates and meta.
If an earlier component contains a dynamic_templates block, then by default new dynamic_templates entries are appended onto the end.
If an entry already exists with the same key, then it is overwritten by the new definition.
manage_index_templatesIf true, this request cannot replace or update existing index templates.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
User defined reason for creating/updating the index template
An ordered list of component template names. Component templates are merged in the order specified, meaning that the last component template specified has the highest precedence.
Priority to determine index template precedence when a new data stream or index is created. The index template with the highest priority is chosen. If no priority is specified the template is treated as though it is of priority 0 (lowest priority). This number is not automatically generated by Elasticsearch.
This setting overrides the value of the action.auto_create_index cluster setting.
If set to true in a template, then indices can be automatically created using that template even if auto-creation of indices is disabled via actions.auto_create_index.
If set to false, then indices or data streams matching the template must always be explicitly created, and may never be automatically created.
The configuration option ignore_missing_component_templates can be used when an index template references a component template that might not exist
Marks this index template as deprecated. When creating or updating a non-deprecated index template that uses deprecated components, Elasticsearch will emit a deprecation warning.
PUT /_index_template/template_1
{
"index_patterns" : ["template*"],
"priority" : 1,
"template": {
"settings" : {
"number_of_shards" : 2
}
}
}resp = client.indices.put_index_template(
name="template_1",
index_patterns=[
"template*"
],
priority=1,
template={
"settings": {
"number_of_shards": 2
}
},
)const response = await client.indices.putIndexTemplate({
name: "template_1",
index_patterns: ["template*"],
priority: 1,
template: {
settings: {
number_of_shards: 2,
},
},
});response = client.indices.put_index_template(
name: "template_1",
body: {
"index_patterns": [
"template*"
],
"priority": 1,
"template": {
"settings": {
"number_of_shards": 2
}
}
}
)$resp = $client->indices()->putIndexTemplate([
"name" => "template_1",
"body" => [
"index_patterns" => array(
"template*",
),
"priority" => 1,
"template" => [
"settings" => [
"number_of_shards" => 2,
],
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"index_patterns":["template*"],"priority":1,"template":{"settings":{"number_of_shards":2}}}' "$ELASTICSEARCH_URL/_index_template/template_1"client.indices().putIndexTemplate(p -> p
.indexPatterns("template*")
.name("template_1")
.priority(1L)
.template(t -> t
.settings(s -> s
.numberOfShards("2")
)
)
);
{
"index_patterns" : ["template*"],
"priority" : 1,
"template": {
"settings" : {
"number_of_shards" : 2
}
}
}{
"index_patterns": [
"template*"
],
"template": {
"settings": {
"number_of_shards": 1
},
"aliases": {
"alias1": {},
"alias2": {
"filter": {
"term": {
"user.id": "kimchy"
}
},
"routing": "shard-1"
},
"{index}-alias": {}
}
}
}Comma-separated list of index template names used to limit the request. Wildcard (*) expressions are supported.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
DELETE /_index_template/my-index-template
resp = client.indices.delete_index_template(
name="my-index-template",
)const response = await client.indices.deleteIndexTemplate({
name: "my-index-template",
});response = client.indices.delete_index_template(
name: "my-index-template"
)$resp = $client->indices()->deleteIndexTemplate([
"name" => "my-index-template",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_index_template/my-index-template"client.indices().deleteIndexTemplate(d -> d
.name("my-index-template")
);
Comma-separated list of index template names used to limit the request. Wildcard (*) expressions are supported.
If true, the request retrieves information from the local node only. Defaults to false, which means information is retrieved from the master node.
If true, returns settings in flat format.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
curl \
--request HEAD 'http://api.example.com/_index_template/{name}' \
--header "Authorization: $API_KEY"Comma-separated list of data streams or indices used to limit the request.
Supports wildcards (*).
To target all data streams and indices, omit this parameter or use * or _all.
Comma-separated list of aliases to retrieve.
Supports wildcards (*).
To retrieve all aliases, omit this parameter or use * or _all.
If false, the request returns an error if any wildcard expression, index alias, or _all value targets only missing or closed indices.
This behavior applies even if the request targets other open indices.
Type of index that wildcard patterns can match.
If the request can target data streams, this argument determines whether wildcard expressions match hidden data streams.
Supports comma-separated values, such as open,hidden.
Supported values include:
all: Match any data stream or index, including hidden ones.open: Match open, non-hidden indices. Also matches any non-hidden data stream.closed: Match closed, non-hidden indices. Also matches any non-hidden data stream. Data streams cannot be closed.hidden: Match hidden data streams and hidden indices. Must be combined with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
GET _alias
resp = client.indices.get_alias()const response = await client.indices.getAlias();response = client.indices.get_alias$resp = $client->indices()->getAlias();curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_alias"client.indices().getAlias(g -> g);
All methods and paths for this operation:
Check if one or more data stream or index aliases exist.
Comma-separated list of data streams or indices used to limit the request. Supports wildcards (*).
To target all data streams and indices, omit this parameter or use * or _all.
Comma-separated list of aliases to check. Supports wildcards (*).
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.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
HEAD _alias/my-alias
resp = client.indices.exists_alias(
name="my-alias",
)const response = await client.indices.existsAlias({
name: "my-alias",
});response = client.indices.exists_alias(
name: "my-alias"
)$resp = $client->indices()->existsAlias([
"name" => "my-alias",
]);curl --head -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_alias/my-alias"client.indices().existsAlias(e -> e
.name("my-alias")
);
Comma-separated list of data streams, indices, and aliases used to limit the request.
Supports wildcards (*).
To target all data streams and indices, omit this parameter or use * or _all.
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.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, the request retrieves information from the local node only.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
GET /books/_mapping
resp = client.indices.get_mapping(
index="books",
)const response = await client.indices.getMapping({
index: "books",
});response = client.indices.get_mapping(
index: "books"
)$resp = $client->indices()->getMapping([
"index" => "books",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/books/_mapping"client.indices().getMapping(g -> g
.index("books")
);
All methods and paths for this operation:
Add new fields to an existing data stream or index. You can use the update mapping API to:
Learn how to use the update mapping API with practical examples in the Update mapping API examples guide.
manageA comma-separated list of index names the mapping should be added to (supports wildcards); use _all or omit to add the mapping on all indices.
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.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
If true, the mappings are applied only to the current write index for the target.
Controls whether dynamic date detection is enabled.
Values are strict, runtime, true, or false.
If date detection is enabled then new string fields are checked against 'dynamic_date_formats' and if the value matches then a new date field is added instead of string.
Specify dynamic templates for the mapping.
Automatically map strings into numeric data types for all fields.
Default value is false.
Mapping for a field. For new fields, this mapping can include:
PUT /my-index-000001/_mapping
{
"properties": {
"user": {
"properties": {
"name": {
"type": "keyword"
}
}
}
}
}resp = client.indices.put_mapping(
index="my-index-000001",
properties={
"user": {
"properties": {
"name": {
"type": "keyword"
}
}
}
},
)const response = await client.indices.putMapping({
index: "my-index-000001",
properties: {
user: {
properties: {
name: {
type: "keyword",
},
},
},
},
});response = client.indices.put_mapping(
index: "my-index-000001",
body: {
"properties": {
"user": {
"properties": {
"name": {
"type": "keyword"
}
}
}
}
}
)$resp = $client->indices()->putMapping([
"index" => "my-index-000001",
"body" => [
"properties" => [
"user" => [
"properties" => [
"name" => [
"type" => "keyword",
],
],
],
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"properties":{"user":{"properties":{"name":{"type":"keyword"}}}}}' "$ELASTICSEARCH_URL/my-index-000001/_mapping"client.indices().putMapping(p -> p
.index("my-index-000001")
.properties("user", pr -> pr
.object(o -> o
.properties("name", pro -> pro
.keyword(k -> k)
)
)
)
);
{
"properties": {
"user": {
"properties": {
"name": {
"type": "keyword"
}
}
}
}
}All methods and paths for this operation:
Get setting information for one or more indices. For data streams, it returns setting information for the stream's backing indices.
view_index_metadataComma-separated list of data streams, indices, and aliases used to limit
the request. Supports wildcards (*). To target all data streams and
indices, omit this parameter or use * or _all.
Comma-separated list or wildcard expression of settings to retrieve.
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 targeting foo*,bar* returns an error if an index
starts with foo but no index starts with bar.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, returns settings in flat format.
If true, return all default settings in the response.
If true, the request retrieves information from the local node only. If
false, information is retrieved from the master node.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
GET _all/_settings?expand_wildcards=all&filter_path=*.settings.index.*.slowlog
resp = client.indices.get_settings(
index="_all",
expand_wildcards="all",
filter_path="*.settings.index.*.slowlog",
)const response = await client.indices.getSettings({
index: "_all",
expand_wildcards: "all",
filter_path: "*.settings.index.*.slowlog",
});response = client.indices.get_settings(
index: "_all",
expand_wildcards: "all",
filter_path: "*.settings.index.*.slowlog"
)$resp = $client->indices()->getSettings([
"index" => "_all",
"expand_wildcards" => "all",
"filter_path" => "*.settings.index.*.slowlog",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_all/_settings?expand_wildcards=all&filter_path=*.settings.index.*.slowlog"All methods and paths for this operation:
Changes dynamic index settings in real time. For data streams, index setting changes are applied to all backing indices by default.
To revert a setting to the default value, use a null value.
The list of per-index settings that can be updated dynamically on live indices can be found in index settings documentation.
To preserve existing settings from being updated, set the preserve_existing parameter to true.
For performance optimization during bulk indexing, you can disable the refresh interval. Refer to disable refresh interval for an example. There are multiple valid ways to represent index settings in the request body. You can specify only the setting, for example:
{
"number_of_replicas": 1
}
Or you can use an index setting object:
{
"index": {
"number_of_replicas": 1
}
}
Or you can use dot annotation:
{
"index.number_of_replicas": 1
}
Or you can embed any of the aforementioned options in a settings object. For example:
{
"settings": {
"index": {
"number_of_replicas": 1
}
}
}
NOTE: You can only define new analyzers on closed indices. To add an analyzer, you must close the index, define the analyzer, and reopen the index. You cannot close the write index of a data stream. To update the analyzer for a data stream's write index and future backing indices, update the analyzer in the index template used by the stream. Then roll over the data stream to apply the new analyzer to the stream's write index and future backing indices. This affects searches and any new data added to the stream after the rollover. However, it does not affect the data stream's backing indices or their existing data. To change the analyzer for existing backing indices, you must create a new data stream and reindex your data into it. Refer to updating analyzers on existing indices for step-by-step examples.
manageComma-separated list of data streams, indices, and aliases used to limit
the request. Supports wildcards (*). To target all data streams and
indices, omit this parameter or use * or _all.
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 targeting foo*,bar* returns an error if an index
starts with foo but no index starts with bar.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, returns settings in flat format.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
If true, existing index settings remain unchanged.
Whether to close and reopen the index to apply non-dynamic settings.
If set to true the indices to which the settings are being applied
will be closed temporarily and then reopened in order to apply the changes.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
PUT /my-index-000001/_settings
{
"index" : {
"number_of_replicas" : 2
}
}resp = client.indices.put_settings(
index="my-index-000001",
settings={
"index": {
"number_of_replicas": 2
}
},
)const response = await client.indices.putSettings({
index: "my-index-000001",
settings: {
index: {
number_of_replicas: 2,
},
},
});response = client.indices.put_settings(
index: "my-index-000001",
body: {
"index": {
"number_of_replicas": 2
}
}
)$resp = $client->indices()->putSettings([
"index" => "my-index-000001",
"body" => [
"index" => [
"number_of_replicas" => 2,
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"index":{"number_of_replicas":2}}' "$ELASTICSEARCH_URL/my-index-000001/_settings"client.indices().putSettings(p -> p
.index("my-index-000001")
.settings(s -> s
.index(i -> i
.numberOfReplicas("2")
)
)
);
{
"index" : {
"number_of_replicas" : 2
}
}{
"index" : {
"refresh_interval" : null
}
}{
"analysis" : {
"analyzer":{
"content":{
"type":"custom",
"tokenizer":"whitespace"
}
}
}
}
POST /my-index-000001/_openAll methods and paths for this operation:
A refresh makes recent operations performed on one or more indices available for search. For data streams, the API runs the refresh operation on the stream’s backing indices.
By default, Elasticsearch periodically refreshes indices every second, but only on indices that have received one search request or more in the last 30 seconds.
You can change this default interval with the index.refresh_interval setting.
Refresh requests are synchronous and do not return a response until the refresh operation completes.
Refreshes are resource-intensive. To ensure good cluster performance, it's recommended to wait for Elasticsearch's periodic refresh rather than performing an explicit refresh when possible.
If your application workflow indexes documents and then runs a search to retrieve the indexed document, it's recommended to use the index API's refresh=wait_for query parameter option.
This option ensures the indexing operation waits for a periodic refresh before running the search.
maintenanceComma-separated list of data streams, indices, and aliases used to limit the request.
Supports wildcards (*).
To target all data streams and indices, omit this parameter or use * or _all.
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.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
GET _refresh
resp = client.indices.refresh()const response = await client.indices.refresh();response = client.indices.refresh$resp = $client->indices()->refresh();curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_refresh"client.indices().refresh(r -> r);
Comma-separated name(s) or index pattern(s) of the indices, aliases, and data streams to resolve.
Resources on remote clusters can be specified using the <cluster>:<name> syntax.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
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 targeting foo*,bar* returns an error if an index starts with foo but no index starts with bar.
GET /_resolve/index/f*,remoteCluster1:bar*?expand_wildcards=all
resp = client.indices.resolve_index(
name="f*,remoteCluster1:bar*",
expand_wildcards="all",
)const response = await client.indices.resolveIndex({
name: "f*,remoteCluster1:bar*",
expand_wildcards: "all",
});response = client.indices.resolve_index(
name: "f*,remoteCluster1:bar*",
expand_wildcards: "all"
)$resp = $client->indices()->resolveIndex([
"name" => "f*,remoteCluster1:bar*",
"expand_wildcards" => "all",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_resolve/index/f*,remoteCluster1:bar*?expand_wildcards=all"{
"indices": [
{
"name": "foo_closed",
"attributes": [
"closed"
]
},
{
"name": "freeze-index",
"aliases": [
"f-alias"
],
"attributes": [
"open"
]
},
{
"name": "remoteCluster1:bar-01",
"attributes": [
"open"
]
}
],
"aliases": [
{
"name": "f-alias",
"indices": [
"freeze-index",
"my-index-000001"
]
}
],
"data_streams": [
{
"name": "foo",
"backing_indices": [
".ds-foo-2099.03.07-000001"
],
"timestamp_field": "@timestamp"
}
]
}All methods and paths for this operation:
TIP: It is recommended to use the index lifecycle rollover action to automate rollovers.
The rollover API creates a new index for a data stream or index alias. The API behavior depends on the rollover target.
Roll over a data stream
If you roll over a data stream, the API creates a new write index for the stream. The stream's previous write index becomes a regular backing index. A rollover also increments the data stream's generation.
Roll over an index alias with a write index
TIP: Prior to Elasticsearch 7.9, you'd typically use an index alias with a write index to manage time series data. Data streams replace this functionality, require less maintenance, and automatically integrate with data tiers.
If an index alias points to multiple indices, one of the indices must be a write index.
The rollover API creates a new write index for the alias with is_write_index set to true.
The API also sets is_write_index to false for the previous write index.
Roll over an index alias with one index
If you roll over an index alias that points to only one index, the API creates a new index for the alias and removes the original index from the alias.
NOTE: A rollover creates a new index and is subject to the wait_for_active_shards setting.
Increment index names for an alias
When you roll over an index alias, you can specify a name for the new index.
If you don't specify a name and the current index ends with - and a number, such as my-index-000001 or my-index-3, the new index name increments that number.
For example, if you roll over an alias with a current index of my-index-000001, the rollover creates a new index named my-index-000002.
This number is always six characters and zero-padded, regardless of the previous index's name.
If you use an index alias for time series data, you can use date math in the index name to track the rollover date.
For example, you can create an alias that points to an index named <my-index-{now/d}-000001>.
If you create the index on May 6, 2099, the index's name is my-index-2099.05.06-000001.
If you roll over the alias on May 7, 2099, the new index's name is my-index-2099.05.07-000002.
manageName of the data stream or index alias to roll over.
Name of the index to create. Supports date math. Data streams do not support this parameter.
If true, checks whether the current index satisfies the specified conditions but does not perform a rollover.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
The number of shard copies that must be active before proceeding with the operation.
Set to all or any positive integer up to the total number of shards in the index (number_of_replicas+1).
Values are all or index-setting.
If set to true, the rollover action will only mark a data stream to signal that it needs to be rolled over at the next write. Only allowed on data streams.
POST my-data-stream/_rollover
{
"conditions": {
"max_age": "7d",
"max_docs": 1000,
"max_primary_shard_size": "50gb",
"max_primary_shard_docs": "2000"
}
}resp = client.indices.rollover(
alias="my-data-stream",
conditions={
"max_age": "7d",
"max_docs": 1000,
"max_primary_shard_size": "50gb",
"max_primary_shard_docs": "2000"
},
)const response = await client.indices.rollover({
alias: "my-data-stream",
conditions: {
max_age: "7d",
max_docs: 1000,
max_primary_shard_size: "50gb",
max_primary_shard_docs: "2000",
},
});response = client.indices.rollover(
alias: "my-data-stream",
body: {
"conditions": {
"max_age": "7d",
"max_docs": 1000,
"max_primary_shard_size": "50gb",
"max_primary_shard_docs": "2000"
}
}
)$resp = $client->indices()->rollover([
"alias" => "my-data-stream",
"body" => [
"conditions" => [
"max_age" => "7d",
"max_docs" => 1000,
"max_primary_shard_size" => "50gb",
"max_primary_shard_docs" => "2000",
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"conditions":{"max_age":"7d","max_docs":1000,"max_primary_shard_size":"50gb","max_primary_shard_docs":"2000"}}' "$ELASTICSEARCH_URL/my-data-stream/_rollover"client.indices().rollover(r -> r
.alias("my-data-stream")
.conditions(c -> c
.maxAge(m -> m
.time("7d")
)
.maxDocs(1000L)
.maxPrimaryShardSize("50gb")
.maxPrimaryShardDocs(2000L)
)
);
{
"conditions": {
"max_age": "7d",
"max_docs": 1000,
"max_primary_shard_size": "50gb",
"max_primary_shard_docs": "2000"
}
}{
"_shards": {},
"indices": {
"test": {
"shards": {
"0": [
{
"routing": {
"state": "STARTED",
"primary": true,
"node": "zDC_RorJQCao9xf9pg3Fvw"
},
"num_committed_segments": 0,
"num_search_segments": 1,
"segments": {
"_0": {
"generation": 0,
"num_docs": 1,
"deleted_docs": 0,
"size_in_bytes": 3800,
"committed": false,
"search": true,
"version": "7.0.0",
"compound": true,
"attributes": {}
}
}
}
]
}
}
}
}Whether the index template we optionally defined in the body should only be dry-run added if new or can also replace an existing one
User defined reason for dry-run creating the new template for simulation purposes
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
If true, returns all relevant default configurations for the index template.
POST /_index_template/_simulate_index/my-index-000001
resp = client.indices.simulate_index_template(
name="my-index-000001",
)const response = await client.indices.simulateIndexTemplate({
name: "my-index-000001",
});response = client.indices.simulate_index_template(
name: "my-index-000001"
)$resp = $client->indices()->simulateIndexTemplate([
"name" => "my-index-000001",
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_index_template/_simulate_index/my-index-000001"client.indices().simulateIndexTemplate(s -> s
.name("my-index-000001")
);
{
"template" : {
"settings" : {
"index" : {
"number_of_shards" : "2",
"number_of_replicas" : "0",
"routing" : {
"allocation" : {
"include" : {
"_tier_preference" : "data_content"
}
}
}
}
},
"mappings" : {
"properties" : {
"@timestamp" : {
"type" : "date"
}
}
},
"aliases" : { }
},
"overlapping" : [
{
"name" : "template_1",
"index_patterns" : [
"my-index-*"
]
}
]
}Name of the index template to simulate. To test a template configuration before you add it to the cluster, omit this parameter and specify the template configuration in the request body.
If true, the template passed in the body is only used if no existing templates match the same index patterns. If false, the simulation uses the template with the highest priority. Note that the template is not permanently added or updated in either case; it is only used for the simulation.
User defined reason for dry-run creating the new template for simulation purposes
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
If true, returns all relevant default configurations for the index template.
This setting overrides the value of the action.auto_create_index cluster setting.
If set to true in a template, then indices can be automatically created using that template even if auto-creation of indices is disabled via actions.auto_create_index.
If set to false, then indices or data streams matching the template must always be explicitly created, and may never be automatically created.
An ordered list of component template names. Component templates are merged in the order specified, meaning that the last component template specified has the highest precedence.
Priority to determine index template precedence when a new data stream or index is created. The index template with the highest priority is chosen. If no priority is specified the template is treated as though it is of priority 0 (lowest priority). This number is not automatically generated by Elasticsearch.
The configuration option ignore_missing_component_templates can be used when an index template references a component template that might not exist
Marks this index template as deprecated. When creating or updating a non-deprecated index template that uses deprecated components, Elasticsearch will emit a deprecation warning.
POST /_index_template/_simulate
{
"index_patterns": ["my-index-*"],
"composed_of": ["ct2"],
"priority": 10,
"template": {
"settings": {
"index.number_of_replicas": 1
}
}
}resp = client.indices.simulate_template(
index_patterns=[
"my-index-*"
],
composed_of=[
"ct2"
],
priority=10,
template={
"settings": {
"index.number_of_replicas": 1
}
},
)const response = await client.indices.simulateTemplate({
index_patterns: ["my-index-*"],
composed_of: ["ct2"],
priority: 10,
template: {
settings: {
"index.number_of_replicas": 1,
},
},
});response = client.indices.simulate_template(
body: {
"index_patterns": [
"my-index-*"
],
"composed_of": [
"ct2"
],
"priority": 10,
"template": {
"settings": {
"index.number_of_replicas": 1
}
}
}
)$resp = $client->indices()->simulateTemplate([
"body" => [
"index_patterns" => array(
"my-index-*",
),
"composed_of" => array(
"ct2",
),
"priority" => 10,
"template" => [
"settings" => [
"index.number_of_replicas" => 1,
],
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"index_patterns":["my-index-*"],"composed_of":["ct2"],"priority":10,"template":{"settings":{"index.number_of_replicas":1}}}' "$ELASTICSEARCH_URL/_index_template/_simulate"client.indices().simulateTemplate(s -> s
.composedOf("ct2")
.indexPatterns("my-index-*")
.priority(10L)
.template(t -> t
.settings(se -> se
.otherSettings("index.number_of_replicas", JsonData.fromJson("1"))
)
)
);
{
"index_patterns": ["my-index-*"],
"composed_of": ["ct2"],
"priority": 10,
"template": {
"settings": {
"index.number_of_replicas": 1
}
}
}{
"template" : {
"settings" : {
"index" : {
"number_of_replicas" : "1",
"routing" : {
"allocation" : {
"include" : {
"_tier_preference" : "data_content"
}
}
}
}
},
"mappings" : {
"properties" : {
"@timestamp" : {
"type" : "date"
}
}
},
"aliases" : { }
},
"overlapping" : [
{
"name" : "final-template",
"index_patterns" : [
"my-index-*"
]
}
]
}Adds a data stream or index to an alias.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
POST _aliases
{
"actions": [
{
"add": {
"index": "logs-nginx.access-prod",
"alias": "logs"
}
}
]
}resp = client.indices.update_aliases(
actions=[
{
"add": {
"index": "logs-nginx.access-prod",
"alias": "logs"
}
}
],
)const response = await client.indices.updateAliases({
actions: [
{
add: {
index: "logs-nginx.access-prod",
alias: "logs",
},
},
],
});response = client.indices.update_aliases(
body: {
"actions": [
{
"add": {
"index": "logs-nginx.access-prod",
"alias": "logs"
}
}
]
}
)$resp = $client->indices()->updateAliases([
"body" => [
"actions" => array(
[
"add" => [
"index" => "logs-nginx.access-prod",
"alias" => "logs",
],
],
),
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"actions":[{"add":{"index":"logs-nginx.access-prod","alias":"logs"}}]}' "$ELASTICSEARCH_URL/_aliases"client.indices().updateAliases(u -> u
.actions(a -> a
.add(ad -> ad
.alias("logs")
.index("logs-nginx.access-prod")
)
)
);
{
"actions": [
{
"add": {
"index": "logs-nginx.access-prod",
"alias": "logs"
}
}
]
}All methods and paths for this operation:
Validates a query without running it.
Comma-separated list of data streams, indices, and aliases to search.
Supports wildcards (*).
To search all data streams or indices, omit this parameter or use * or _all.
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.
If true, the validation is executed on all shards instead of one random shard per index.
Analyzer to use for the query string.
This parameter can only be used when the q query string parameter is specified.
If true, wildcard and prefix queries are analyzed.
The default operator for query string query: AND or OR.
Values are and, AND, or, or OR.
Field to use as default where no field prefix is given in the query string.
This parameter can only be used when the q query string parameter is specified.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, the response returns detailed information if an error has occurred.
If true, format-based query failures (such as providing text to a numeric field) in the query string will be ignored.
If true, returns a more detailed explanation showing the actual Lucene query that will be executed.
Query in the Lucene query string syntax.
An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
GET my-index-000001/_validate/query?q=user.id:kimchy
resp = client.indices.validate_query(
index="my-index-000001",
q="user.id:kimchy",
)const response = await client.indices.validateQuery({
index: "my-index-000001",
q: "user.id:kimchy",
});response = client.indices.validate_query(
index: "my-index-000001",
q: "user.id:kimchy"
)$resp = $client->indices()->validateQuery([
"index" => "my-index-000001",
"q" => "user.id:kimchy",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/my-index-000001/_validate/query?q=user.id:kimchy"client.indices().validateQuery(v -> v
.index("my-index-000001")
.q("user.id:kimchy")
);
Inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
The chat completion inference API enables real-time responses for chat completion tasks by delivering answers incrementally, reducing response times during computation.
It only works with the chat_completion task type for openai and elastic inference services.
NOTE: The chat_completion task type is only available within the _stream API and only supports streaming.
The Chat completion inference API and the Stream inference API differ in their response structure and capabilities.
The Chat completion inference API provides more comprehensive customization options through more fields and function calling support.
If you use the openai, hugging_face or the elastic service, use the Chat completion inference API.
Specifies the amount of time to wait for the inference request to complete.
Values are -1 or 0.
A list of objects representing the conversation.
Requests should generally only add new messages from the user (role user).
The other message roles (assistant, system, or tool) should generally only be copied from the response to a previous completion request, such that the messages array is built up throughout a conversation.
An object representing part of the conversation.
The ID of the model to use.
The upper bound limit for the number of tokens that can be generated for a completion request.
A sequence of strings to control when the model should stop generating additional tokens.
The sampling temperature to use.
A list of tools that the model can call. Example:
{
"tools": [
{
"type": "function",
"function": {
"name": "get_price_of_item",
"description": "Get the current price of an item",
"parameters": {
"type": "object",
"properties": {
"item": {
"id": "12345"
},
"unit": {
"type": "currency"
}
}
}
}
}
]
}
A list of tools that the model can call.
Nucleus sampling, an alternative to sampling with temperature.
POST _inference/chat_completion/openai-completion/_stream
{
"model": "gpt-4o",
"messages": [
{
"role": "user",
"content": "What is Elastic?"
}
]
}resp = client.inference.chat_completion_unified(
inference_id="openai-completion",
chat_completion_request={
"model": "gpt-4o",
"messages": [
{
"role": "user",
"content": "What is Elastic?"
}
]
},
)const response = await client.inference.chatCompletionUnified({
inference_id: "openai-completion",
chat_completion_request: {
model: "gpt-4o",
messages: [
{
role: "user",
content: "What is Elastic?",
},
],
},
});response = client.inference.chat_completion_unified(
inference_id: "openai-completion",
body: {
"model": "gpt-4o",
"messages": [
{
"role": "user",
"content": "What is Elastic?"
}
]
}
)$resp = $client->inference()->chatCompletionUnified([
"inference_id" => "openai-completion",
"body" => [
"model" => "gpt-4o",
"messages" => array(
[
"role" => "user",
"content" => "What is Elastic?",
],
),
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"model":"gpt-4o","messages":[{"role":"user","content":"What is Elastic?"}]}' "$ELASTICSEARCH_URL/_inference/chat_completion/openai-completion/_stream"client.inference().chatCompletionUnified(c -> c
.inferenceId("openai-completion")
.chatCompletionRequest(ch -> ch
.messages(m -> m
.content(co -> co
.string("What is Elastic?")
)
.role("user")
)
.model("gpt-4o")
)
);
{
"model": "gpt-4o",
"messages": [
{
"role": "user",
"content": "What is Elastic?"
}
]
}{
"messages": [
{
"role": "assistant",
"content": "Let's find out what the weather is",
"tool_calls": [
{
"id": "call_KcAjWtAww20AihPHphUh46Gd",
"type": "function",
"function": {
"name": "get_current_weather",
"arguments": "{\"location\":\"Boston, MA\"}"
}
}
]
},
{
"role": "tool",
"content": "The weather is cold",
"tool_call_id": "call_KcAjWtAww20AihPHphUh46Gd"
}
]
}{
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What's the price of a scarf?"
}
]
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_current_price",
"description": "Get the current price of a item",
"parameters": {
"type": "object",
"properties": {
"item": {
"id": "123"
}
}
}
}
}
],
"tool_choice": {
"type": "function",
"function": {
"name": "get_current_price"
}
}
}event: message
data: {"chat_completion":{"id":"chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3","choices":[{"delta":{"content":"","role":"assistant"},"index":0}],"model":"gpt-4o-2024-08-06","object":"chat.completion.chunk"}}
event: message
data: {"chat_completion":{"id":"chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3","choices":[{"delta":{"content":Elastic"},"index":0}],"model":"gpt-4o-2024-08-06","object":"chat.completion.chunk"}}
event: message
data: {"chat_completion":{"id":"chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3","choices":[{"delta":{"content":" is"},"index":0}],"model":"gpt-4o-2024-08-06","object":"chat.completion.chunk"}}
(...)
event: message
data: {"chat_completion":{"id":"chatcmpl-Ae0TWsy2VPnSfBbv5UztnSdYUMFP3","choices":[],"model":"gpt-4o-2024-08-06","object":"chat.completion.chunk","usage":{"completion_tokens":28,"prompt_tokens":16,"total_tokens":44}}}
event: message
data: [DONE]Specifies the amount of time to wait for the inference request to complete.
Values are -1 or 0.
POST _inference/completion/openai_chat_completions
{
"input": "What is Elastic?"
}resp = client.inference.completion(
inference_id="openai_chat_completions",
input="What is Elastic?",
)const response = await client.inference.completion({
inference_id: "openai_chat_completions",
input: "What is Elastic?",
});response = client.inference.completion(
inference_id: "openai_chat_completions",
body: {
"input": "What is Elastic?"
}
)$resp = $client->inference()->completion([
"inference_id" => "openai_chat_completions",
"body" => [
"input" => "What is Elastic?",
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"input":"What is Elastic?"}' "$ELASTICSEARCH_URL/_inference/completion/openai_chat_completions"client.inference().completion(c -> c
.inferenceId("openai_chat_completions")
.input("What is Elastic?")
);
{
"input": "What is Elastic?"
}{
"completion": [
{
"result": "Elastic is a company that provides a range of software solutions for search, logging, security, and analytics. Their flagship product is Elasticsearch, an open-source, distributed search engine that allows users to search, analyze, and visualize large volumes of data in real-time. Elastic also offers products such as Kibana, a data visualization tool, and Logstash, a log management and pipeline tool, as well as various other tools and solutions for data analysis and management."
}
]
}All methods and paths for this operation:
GET _inference/sparse_embedding/my-elser-model
resp = client.inference.get(
task_type="sparse_embedding",
inference_id="my-elser-model",
)const response = await client.inference.get({
task_type: "sparse_embedding",
inference_id: "my-elser-model",
});response = client.inference.get(
task_type: "sparse_embedding",
inference_id: "my-elser-model"
)$resp = $client->inference()->get([
"task_type" => "sparse_embedding",
"inference_id" => "my-elser-model",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"client.inference().get(g -> g
.inferenceId("my-elser-model")
.taskType(TaskType.SparseEmbedding)
);
All methods and paths for this operation:
IMPORTANT: The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Mistral, Azure OpenAI, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
The following integrations are available through the inference API. You can find the available task types next to the integration name:
completion, rerank, sparse_embedding, text_embedding)completion, text_embedding)completion)completion, text_embedding)completion, text_embedding)completion, rerank, text_embedding)completion, chat_completion)rerank, sparse_embedding, text_embedding - this service is for built-in models and models uploaded through Eland)sparse_embedding)completion, text_embedding)rerank, text_embedding)chat_completion, completion, rerank, text_embedding)chat_completion, completion, text_embedding)chat_completion, completion, text_embedding)text_embedding, rerank)text_embedding)text_embedding, rerank)manage_inferenceThe task type. Refer to the integration list in the API description for the available task types.
Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.
The inference Id
PUT _inference/rerank/my-rerank-model
{
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
}resp = client.inference.put(
task_type="rerank",
inference_id="my-rerank-model",
inference_config={
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
},
)const response = await client.inference.put({
task_type: "rerank",
inference_id: "my-rerank-model",
inference_config: {
service: "cohere",
service_settings: {
model_id: "rerank-english-v3.0",
api_key: "{{COHERE_API_KEY}}",
},
},
});response = client.inference.put(
task_type: "rerank",
inference_id: "my-rerank-model",
body: {
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
}
)$resp = $client->inference()->put([
"task_type" => "rerank",
"inference_id" => "my-rerank-model",
"body" => [
"service" => "cohere",
"service_settings" => [
"model_id" => "rerank-english-v3.0",
"api_key" => "{{COHERE_API_KEY}}",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"cohere","service_settings":{"model_id":"rerank-english-v3.0","api_key":"{{COHERE_API_KEY}}"}}' "$ELASTICSEARCH_URL/_inference/rerank/my-rerank-model"client.inference().put(p -> p
.inferenceId("my-rerank-model")
.taskType(TaskType.Rerank)
.inferenceConfig(i -> i
.service("cohere")
.serviceSettings(JsonData.fromJson("{\"model_id\":\"rerank-english-v3.0\",\"api_key\":\"{{COHERE_API_KEY}}\"}"))
)
);
{
"service": "cohere",
"service_settings": {
"model_id": "rerank-english-v3.0",
"api_key": "{{COHERE_API_KEY}}"
}
}All methods and paths for this operation:
This API enables you to use machine learning models to perform specific tasks on data that you provide as an input. It returns a response with the results of the tasks. The inference endpoint you use can perform one specific task that has been defined when the endpoint was created with the create inference API.
For details about using this API with a service, such as Amazon Bedrock, Anthropic, or HuggingFace, refer to the service-specific documentation.
The inference APIs enable you to use certain services, such as built-in machine learning models (ELSER, E5), models uploaded through Eland, Cohere, OpenAI, Azure, Google AI Studio, Google Vertex AI, Anthropic, Watsonx.ai, or Hugging Face. For built-in models and models uploaded through Eland, the inference APIs offer an alternative way to use and manage trained models. However, if you do not plan to use the inference APIs to use these models or if you want to use non-NLP models, use the machine learning trained model APIs.
monitor_inferenceThe type of inference task that the model performs.
Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.
The unique identifier for the inference endpoint.
The amount of time to wait for the inference request to complete.
Values are -1 or 0.
The query input, which is required only for the rerank task.
It is not required for other tasks.
curl \
--request POST 'http://api.example.com/_inference/{task_type}/{inference_id}' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '{"query":"string","input":"string","task_settings":{}}'All methods and paths for this operation:
The task type
Values are sparse_embedding, text_embedding, rerank, completion, or chat_completion.
The inference identifier.
DELETE /_inference/sparse_embedding/my-elser-model
resp = client.inference.delete(
task_type="sparse_embedding",
inference_id="my-elser-model",
)const response = await client.inference.delete({
task_type: "sparse_embedding",
inference_id: "my-elser-model",
});response = client.inference.delete(
task_type: "sparse_embedding",
inference_id: "my-elser-model"
)$resp = $client->inference()->delete([
"task_type" => "sparse_embedding",
"inference_id" => "my-elser-model",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"client.inference().delete(d -> d
.inferenceId("my-elser-model")
.taskType(TaskType.SparseEmbedding)
);
The type of the inference task that the model will perform.
Values are completion, rerank, space_embedding, or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/completion/alibabacloud_ai_search_completion
{
"service": "alibabacloud-ai-search",
"service_settings": {
"host" : "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
"api_key": "AlibabaCloud-API-Key",
"service_id": "ops-qwen-turbo",
"workspace" : "default"
}
}resp = client.inference.put(
task_type="completion",
inference_id="alibabacloud_ai_search_completion",
inference_config={
"service": "alibabacloud-ai-search",
"service_settings": {
"host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
"api_key": "AlibabaCloud-API-Key",
"service_id": "ops-qwen-turbo",
"workspace": "default"
}
},
)const response = await client.inference.put({
task_type: "completion",
inference_id: "alibabacloud_ai_search_completion",
inference_config: {
service: "alibabacloud-ai-search",
service_settings: {
host: "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
api_key: "AlibabaCloud-API-Key",
service_id: "ops-qwen-turbo",
workspace: "default",
},
},
});response = client.inference.put(
task_type: "completion",
inference_id: "alibabacloud_ai_search_completion",
body: {
"service": "alibabacloud-ai-search",
"service_settings": {
"host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
"api_key": "AlibabaCloud-API-Key",
"service_id": "ops-qwen-turbo",
"workspace": "default"
}
}
)$resp = $client->inference()->put([
"task_type" => "completion",
"inference_id" => "alibabacloud_ai_search_completion",
"body" => [
"service" => "alibabacloud-ai-search",
"service_settings" => [
"host" => "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
"api_key" => "AlibabaCloud-API-Key",
"service_id" => "ops-qwen-turbo",
"workspace" => "default",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"alibabacloud-ai-search","service_settings":{"host":"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com","api_key":"AlibabaCloud-API-Key","service_id":"ops-qwen-turbo","workspace":"default"}}' "$ELASTICSEARCH_URL/_inference/completion/alibabacloud_ai_search_completion"client.inference().put(p -> p
.inferenceId("alibabacloud_ai_search_completion")
.taskType(TaskType.Completion)
.inferenceConfig(i -> i
.service("alibabacloud-ai-search")
.serviceSettings(JsonData.fromJson("{\"host\":\"default-j01.platform-cn-shanghai.opensearch.aliyuncs.com\",\"api_key\":\"AlibabaCloud-API-Key\",\"service_id\":\"ops-qwen-turbo\",\"workspace\":\"default\"}"))
)
);
{
"service": "alibabacloud-ai-search",
"service_settings": {
"host" : "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
"api_key": "AlibabaCloud-API-Key",
"service_id": "ops-qwen-turbo",
"workspace" : "default"
}
}{
"service": "alibabacloud-ai-search",
"service_settings": {
"api_key": "AlibabaCloud-API-Key",
"service_id": "ops-bge-reranker-larger",
"host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
"workspace": "default"
}
}{
"service": "alibabacloud-ai-search",
"service_settings": {
"api_key": "AlibabaCloud-API-Key",
"service_id": "ops-text-sparse-embedding-001",
"host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
"workspace": "default"
}
}{
"service": "alibabacloud-ai-search",
"service_settings": {
"api_key": "AlibabaCloud-API-Key",
"service_id": "ops-text-embedding-001",
"host": "default-j01.platform-cn-shanghai.opensearch.aliyuncs.com",
"workspace": "default"
}
}Create an inference endpoint to perform an inference task with the amazonbedrock service.
You need to provide the access and secret keys only once, during the inference model creation. The get inference API does not retrieve your access or secret keys. After creating the inference model, you cannot change the associated key pairs. If you want to use a different access and secret key pair, delete the inference model and recreate it with the same name and the updated keys.
manage_inferenceThe type of the inference task that the model will perform.
Values are completion or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/amazon_bedrock_embeddings
{
"service": "amazonbedrock",
"service_settings": {
"access_key": "AWS-access-key",
"secret_key": "AWS-secret-key",
"region": "us-east-1",
"provider": "amazontitan",
"model": "amazon.titan-embed-text-v2:0"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="amazon_bedrock_embeddings",
inference_config={
"service": "amazonbedrock",
"service_settings": {
"access_key": "AWS-access-key",
"secret_key": "AWS-secret-key",
"region": "us-east-1",
"provider": "amazontitan",
"model": "amazon.titan-embed-text-v2:0"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "amazon_bedrock_embeddings",
inference_config: {
service: "amazonbedrock",
service_settings: {
access_key: "AWS-access-key",
secret_key: "AWS-secret-key",
region: "us-east-1",
provider: "amazontitan",
model: "amazon.titan-embed-text-v2:0",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "amazon_bedrock_embeddings",
body: {
"service": "amazonbedrock",
"service_settings": {
"access_key": "AWS-access-key",
"secret_key": "AWS-secret-key",
"region": "us-east-1",
"provider": "amazontitan",
"model": "amazon.titan-embed-text-v2:0"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "amazon_bedrock_embeddings",
"body" => [
"service" => "amazonbedrock",
"service_settings" => [
"access_key" => "AWS-access-key",
"secret_key" => "AWS-secret-key",
"region" => "us-east-1",
"provider" => "amazontitan",
"model" => "amazon.titan-embed-text-v2:0",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"amazonbedrock","service_settings":{"access_key":"AWS-access-key","secret_key":"AWS-secret-key","region":"us-east-1","provider":"amazontitan","model":"amazon.titan-embed-text-v2:0"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/amazon_bedrock_embeddings"client.inference().put(p -> p
.inferenceId("amazon_bedrock_embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("amazonbedrock")
.serviceSettings(JsonData.fromJson("{\"access_key\":\"AWS-access-key\",\"secret_key\":\"AWS-secret-key\",\"region\":\"us-east-1\",\"provider\":\"amazontitan\",\"model\":\"amazon.titan-embed-text-v2:0\"}"))
)
);
{
"service": "amazonbedrock",
"service_settings": {
"access_key": "AWS-access-key",
"secret_key": "AWS-secret-key",
"region": "us-east-1",
"provider": "amazontitan",
"model": "amazon.titan-embed-text-v2:0"
}
}{
"service": "openai",
"service_settings": {
"api_key": "OpenAI-API-Key",
"model_id": "gpt-3.5-turbo"
}
}The task type.
The only valid task type for the model to perform is completion.
Value is completion.
The unique identifier of the inference endpoint.
PUT _inference/completion/anthropic_completion
{
"service": "anthropic",
"service_settings": {
"api_key": "Anthropic-Api-Key",
"model_id": "Model-ID"
},
"task_settings": {
"max_tokens": 1024
}
}resp = client.inference.put(
task_type="completion",
inference_id="anthropic_completion",
inference_config={
"service": "anthropic",
"service_settings": {
"api_key": "Anthropic-Api-Key",
"model_id": "Model-ID"
},
"task_settings": {
"max_tokens": 1024
}
},
)const response = await client.inference.put({
task_type: "completion",
inference_id: "anthropic_completion",
inference_config: {
service: "anthropic",
service_settings: {
api_key: "Anthropic-Api-Key",
model_id: "Model-ID",
},
task_settings: {
max_tokens: 1024,
},
},
});response = client.inference.put(
task_type: "completion",
inference_id: "anthropic_completion",
body: {
"service": "anthropic",
"service_settings": {
"api_key": "Anthropic-Api-Key",
"model_id": "Model-ID"
},
"task_settings": {
"max_tokens": 1024
}
}
)$resp = $client->inference()->put([
"task_type" => "completion",
"inference_id" => "anthropic_completion",
"body" => [
"service" => "anthropic",
"service_settings" => [
"api_key" => "Anthropic-Api-Key",
"model_id" => "Model-ID",
],
"task_settings" => [
"max_tokens" => 1024,
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"anthropic","service_settings":{"api_key":"Anthropic-Api-Key","model_id":"Model-ID"},"task_settings":{"max_tokens":1024}}' "$ELASTICSEARCH_URL/_inference/completion/anthropic_completion"client.inference().put(p -> p
.inferenceId("anthropic_completion")
.taskType(TaskType.Completion)
.inferenceConfig(i -> i
.service("anthropic")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"Anthropic-Api-Key\",\"model_id\":\"Model-ID\"}"))
.taskSettings(JsonData.fromJson("{\"max_tokens\":1024}"))
)
);
{
"service": "anthropic",
"service_settings": {
"api_key": "Anthropic-Api-Key",
"model_id": "Model-ID"
},
"task_settings": {
"max_tokens": 1024
}
}The type of the inference task that the model will perform.
Values are completion or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/azure_ai_studio_embeddings
{
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-Uri",
"provider": "openai",
"endpoint_type": "token"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="azure_ai_studio_embeddings",
inference_config={
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-Uri",
"provider": "openai",
"endpoint_type": "token"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "azure_ai_studio_embeddings",
inference_config: {
service: "azureaistudio",
service_settings: {
api_key: "Azure-AI-Studio-API-key",
target: "Target-Uri",
provider: "openai",
endpoint_type: "token",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "azure_ai_studio_embeddings",
body: {
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-Uri",
"provider": "openai",
"endpoint_type": "token"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "azure_ai_studio_embeddings",
"body" => [
"service" => "azureaistudio",
"service_settings" => [
"api_key" => "Azure-AI-Studio-API-key",
"target" => "Target-Uri",
"provider" => "openai",
"endpoint_type" => "token",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"azureaistudio","service_settings":{"api_key":"Azure-AI-Studio-API-key","target":"Target-Uri","provider":"openai","endpoint_type":"token"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/azure_ai_studio_embeddings"client.inference().put(p -> p
.inferenceId("azure_ai_studio_embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("azureaistudio")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"Azure-AI-Studio-API-key\",\"target\":\"Target-Uri\",\"provider\":\"openai\",\"endpoint_type\":\"token\"}"))
)
);
{
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-Uri",
"provider": "openai",
"endpoint_type": "token"
}
}{
"service": "azureaistudio",
"service_settings": {
"api_key": "Azure-AI-Studio-API-key",
"target": "Target-URI",
"provider": "databricks",
"endpoint_type": "realtime"
}
}Create an inference endpoint to perform an inference task with the azureopenai service.
The list of chat completion models that you can choose from in your Azure OpenAI deployment include:
The list of embeddings models that you can choose from in your deployment can be found in the Azure models documentation.
manage_inferenceThe type of the inference task that the model will perform.
NOTE: The chat_completion task type only supports streaming and only through the _stream API.
Values are completion or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/azure_openai_embeddings
{
"service": "azureopenai",
"service_settings": {
"api_key": "Api-Key",
"resource_name": "Resource-name",
"deployment_id": "Deployment-id",
"api_version": "2024-02-01"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="azure_openai_embeddings",
inference_config={
"service": "azureopenai",
"service_settings": {
"api_key": "Api-Key",
"resource_name": "Resource-name",
"deployment_id": "Deployment-id",
"api_version": "2024-02-01"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "azure_openai_embeddings",
inference_config: {
service: "azureopenai",
service_settings: {
api_key: "Api-Key",
resource_name: "Resource-name",
deployment_id: "Deployment-id",
api_version: "2024-02-01",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "azure_openai_embeddings",
body: {
"service": "azureopenai",
"service_settings": {
"api_key": "Api-Key",
"resource_name": "Resource-name",
"deployment_id": "Deployment-id",
"api_version": "2024-02-01"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "azure_openai_embeddings",
"body" => [
"service" => "azureopenai",
"service_settings" => [
"api_key" => "Api-Key",
"resource_name" => "Resource-name",
"deployment_id" => "Deployment-id",
"api_version" => "2024-02-01",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"azureopenai","service_settings":{"api_key":"Api-Key","resource_name":"Resource-name","deployment_id":"Deployment-id","api_version":"2024-02-01"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/azure_openai_embeddings"client.inference().put(p -> p
.inferenceId("azure_openai_embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("azureopenai")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"Api-Key\",\"resource_name\":\"Resource-name\",\"deployment_id\":\"Deployment-id\",\"api_version\":\"2024-02-01\"}"))
)
);
{
"service": "azureopenai",
"service_settings": {
"api_key": "Api-Key",
"resource_name": "Resource-name",
"deployment_id": "Deployment-id",
"api_version": "2024-02-01"
}
}{
"service": "azureopenai",
"service_settings": {
"api_key": "Api-Key",
"resource_name": "Resource-name",
"deployment_id": "Deployment-id",
"api_version": "2024-02-01"
}
}The type of the inference task that the model will perform.
Values are completion, rerank, or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/cohere-embeddings
{
"service": "cohere",
"service_settings": {
"api_key": "Cohere-Api-key",
"model_id": "embed-english-light-v3.0",
"embedding_type": "byte"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="cohere-embeddings",
inference_config={
"service": "cohere",
"service_settings": {
"api_key": "Cohere-Api-key",
"model_id": "embed-english-light-v3.0",
"embedding_type": "byte"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "cohere-embeddings",
inference_config: {
service: "cohere",
service_settings: {
api_key: "Cohere-Api-key",
model_id: "embed-english-light-v3.0",
embedding_type: "byte",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "cohere-embeddings",
body: {
"service": "cohere",
"service_settings": {
"api_key": "Cohere-Api-key",
"model_id": "embed-english-light-v3.0",
"embedding_type": "byte"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "cohere-embeddings",
"body" => [
"service" => "cohere",
"service_settings" => [
"api_key" => "Cohere-Api-key",
"model_id" => "embed-english-light-v3.0",
"embedding_type" => "byte",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"cohere","service_settings":{"api_key":"Cohere-Api-key","model_id":"embed-english-light-v3.0","embedding_type":"byte"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/cohere-embeddings"client.inference().put(p -> p
.inferenceId("cohere-embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("cohere")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"Cohere-Api-key\",\"model_id\":\"embed-english-light-v3.0\",\"embedding_type\":\"byte\"}"))
)
);
{
"service": "cohere",
"service_settings": {
"api_key": "Cohere-Api-key",
"model_id": "embed-english-light-v3.0",
"embedding_type": "byte"
}
}{
"service": "cohere",
"service_settings": {
"api_key": "Cohere-API-key",
"model_id": "rerank-english-v3.0"
},
"task_settings": {
"top_n": 10,
"return_documents": true
}
}The type of the inference task that the model will perform.
Values are completion or chat_completion.
The unique identifier of the inference endpoint.
curl \
--request PUT 'http://api.example.com/_inference/{task_type}/{deepseek_inference_id}' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '{"chunking_settings":{"max_chunk_size":250,"overlap":100,"sentence_overlap":1,"strategy":"sentence"},"service":"deepseek","service_settings":{"api_key":"string","model_id":"string","url":"string"}}'Create an inference endpoint to perform an inference task with the elasticsearch service.
Your Elasticsearch deployment contains preconfigured ELSER and E5 inference endpoints, you only need to create the enpoints using the API if you want to customize the settings.
If you use the ELSER or the E5 model through the elasticsearch service, the API request will automatically download and deploy the model if it isn't downloaded yet.
You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.
After creating the endpoint, wait for the model deployment to complete before using it.
To verify the deployment status, use the get trained model statistics API.
Look for "state": "fully_allocated" in the response and ensure that the "allocation_count" matches the "target_allocation_count".
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
manage_inferenceThe type of the inference task that the model will perform.
Values are rerank, sparse_embedding, or text_embedding.
The unique identifier of the inference endpoint.
The must not match the model_id.
PUT _inference/sparse_embedding/my-elser-model
{
"service": "elasticsearch",
"service_settings": {
"adaptive_allocations": {
"enabled": true,
"min_number_of_allocations": 1,
"max_number_of_allocations": 4
},
"num_threads": 1,
"model_id": ".elser_model_2"
}
}resp = client.inference.put(
task_type="sparse_embedding",
inference_id="my-elser-model",
inference_config={
"service": "elasticsearch",
"service_settings": {
"adaptive_allocations": {
"enabled": True,
"min_number_of_allocations": 1,
"max_number_of_allocations": 4
},
"num_threads": 1,
"model_id": ".elser_model_2"
}
},
)const response = await client.inference.put({
task_type: "sparse_embedding",
inference_id: "my-elser-model",
inference_config: {
service: "elasticsearch",
service_settings: {
adaptive_allocations: {
enabled: true,
min_number_of_allocations: 1,
max_number_of_allocations: 4,
},
num_threads: 1,
model_id: ".elser_model_2",
},
},
});response = client.inference.put(
task_type: "sparse_embedding",
inference_id: "my-elser-model",
body: {
"service": "elasticsearch",
"service_settings": {
"adaptive_allocations": {
"enabled": true,
"min_number_of_allocations": 1,
"max_number_of_allocations": 4
},
"num_threads": 1,
"model_id": ".elser_model_2"
}
}
)$resp = $client->inference()->put([
"task_type" => "sparse_embedding",
"inference_id" => "my-elser-model",
"body" => [
"service" => "elasticsearch",
"service_settings" => [
"adaptive_allocations" => [
"enabled" => true,
"min_number_of_allocations" => 1,
"max_number_of_allocations" => 4,
],
"num_threads" => 1,
"model_id" => ".elser_model_2",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"elasticsearch","service_settings":{"adaptive_allocations":{"enabled":true,"min_number_of_allocations":1,"max_number_of_allocations":4},"num_threads":1,"model_id":".elser_model_2"}}' "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"client.inference().put(p -> p
.inferenceId("my-elser-model")
.taskType(TaskType.SparseEmbedding)
.inferenceConfig(i -> i
.service("elasticsearch")
.serviceSettings(JsonData.fromJson("{\"adaptive_allocations\":{\"enabled\":true,\"min_number_of_allocations\":1,\"max_number_of_allocations\":4},\"num_threads\":1,\"model_id\":\".elser_model_2\"}"))
)
);
{
"service": "elasticsearch",
"service_settings": {
"adaptive_allocations": {
"enabled": true,
"min_number_of_allocations": 1,
"max_number_of_allocations": 4
},
"num_threads": 1,
"model_id": ".elser_model_2"
}
}{
"service": "elasticsearch",
"service_settings": {
"model_id": ".rerank-v1",
"num_threads": 1,
"adaptive_allocations": {
"enabled": true,
"min_number_of_allocations": 1,
"max_number_of_allocations": 4
}
}
}{
"service": "elasticsearch",
"service_settings": {
"num_allocations": 1,
"num_threads": 1,
"model_id": ".multilingual-e5-small"
}
}{
"service": "elasticsearch",
"service_settings": {
"num_allocations": 1,
"num_threads": 1,
"model_id": "msmarco-MiniLM-L12-cos-v5"
}
}{
"service": "elasticsearch",
"service_settings": {
"adaptive_allocations": {
"enabled": true,
"min_number_of_allocations": 3,
"max_number_of_allocations": 10
},
"num_threads": 1,
"model_id": ".multilingual-e5-small"
}
}{
"service": "elasticsearch",
"service_settings": {
"deployment_id": ".elser_model_2"
}
}{
"inference_id": "use_existing_deployment",
"task_type": "sparse_embedding",
"service": "elasticsearch",
"service_settings": {
"num_allocations": 2,
"num_threads": 1,
"model_id": ".elser_model_2",
"deployment_id": ".elser_model_2"
},
"chunking_settings": {
"strategy": "sentence",
"max_chunk_size": 250,
"sentence_overlap": 1
}
}Create an inference endpoint to perform an inference task with the elser service.
You can also deploy ELSER by using the Elasticsearch inference integration.
Your Elasticsearch deployment contains a preconfigured ELSER inference endpoint, you only need to create the enpoint using the API if you want to customize the settings.
The API request will automatically download and deploy the ELSER model if it isn't already downloaded.
You might see a 502 bad gateway error in the response when using the Kibana Console. This error usually just reflects a timeout, while the model downloads in the background. You can check the download progress in the Machine Learning UI. If using the Python client, you can set the timeout parameter to a higher value.
After creating the endpoint, wait for the model deployment to complete before using it.
To verify the deployment status, use the get trained model statistics API.
Look for "state": "fully_allocated" in the response and ensure that the "allocation_count" matches the "target_allocation_count".
Avoid creating multiple endpoints for the same model unless required, as each endpoint consumes significant resources.
manage_inferenceThe type of the inference task that the model will perform.
Value is sparse_embedding.
The unique identifier of the inference endpoint.
PUT _inference/sparse_embedding/my-elser-model
{
"service": "elser",
"service_settings": {
"num_allocations": 1,
"num_threads": 1
}
}resp = client.inference.put(
task_type="sparse_embedding",
inference_id="my-elser-model",
inference_config={
"service": "elser",
"service_settings": {
"num_allocations": 1,
"num_threads": 1
}
},
)const response = await client.inference.put({
task_type: "sparse_embedding",
inference_id: "my-elser-model",
inference_config: {
service: "elser",
service_settings: {
num_allocations: 1,
num_threads: 1,
},
},
});response = client.inference.put(
task_type: "sparse_embedding",
inference_id: "my-elser-model",
body: {
"service": "elser",
"service_settings": {
"num_allocations": 1,
"num_threads": 1
}
}
)$resp = $client->inference()->put([
"task_type" => "sparse_embedding",
"inference_id" => "my-elser-model",
"body" => [
"service" => "elser",
"service_settings" => [
"num_allocations" => 1,
"num_threads" => 1,
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"elser","service_settings":{"num_allocations":1,"num_threads":1}}' "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"client.inference().put(p -> p
.inferenceId("my-elser-model")
.taskType(TaskType.SparseEmbedding)
.inferenceConfig(i -> i
.service("elser")
.serviceSettings(JsonData.fromJson("{\"num_allocations\":1,\"num_threads\":1}"))
)
);
{
"service": "elser",
"service_settings": {
"num_allocations": 1,
"num_threads": 1
}
}{
"service": "elser",
"service_settings": {
"adaptive_allocations": {
"enabled": true,
"min_number_of_allocations": 3,
"max_number_of_allocations": 10
},
"num_threads": 1
}
}{
"inference_id": "my-elser-model",
"task_type": "sparse_embedding",
"service": "elser",
"service_settings": {
"num_allocations": 1,
"num_threads": 1
},
"task_settings": {}
}The type of the inference task that the model will perform.
Values are completion or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/completion/google_ai_studio_completion
{
"service": "googleaistudio",
"service_settings": {
"api_key": "api-key",
"model_id": "model-id"
}
}resp = client.inference.put(
task_type="completion",
inference_id="google_ai_studio_completion",
inference_config={
"service": "googleaistudio",
"service_settings": {
"api_key": "api-key",
"model_id": "model-id"
}
},
)const response = await client.inference.put({
task_type: "completion",
inference_id: "google_ai_studio_completion",
inference_config: {
service: "googleaistudio",
service_settings: {
api_key: "api-key",
model_id: "model-id",
},
},
});response = client.inference.put(
task_type: "completion",
inference_id: "google_ai_studio_completion",
body: {
"service": "googleaistudio",
"service_settings": {
"api_key": "api-key",
"model_id": "model-id"
}
}
)$resp = $client->inference()->put([
"task_type" => "completion",
"inference_id" => "google_ai_studio_completion",
"body" => [
"service" => "googleaistudio",
"service_settings" => [
"api_key" => "api-key",
"model_id" => "model-id",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"googleaistudio","service_settings":{"api_key":"api-key","model_id":"model-id"}}' "$ELASTICSEARCH_URL/_inference/completion/google_ai_studio_completion"client.inference().put(p -> p
.inferenceId("google_ai_studio_completion")
.taskType(TaskType.Completion)
.inferenceConfig(i -> i
.service("googleaistudio")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"api-key\",\"model_id\":\"model-id\"}"))
)
);
{
"service": "googleaistudio",
"service_settings": {
"api_key": "api-key",
"model_id": "model-id"
}
}The type of the inference task that the model will perform.
Values are rerank, text_embedding, completion, or chat_completion.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/google_vertex_ai_embeddingss
{
"service": "googlevertexai",
"service_settings": {
"service_account_json": "service-account-json",
"model_id": "model-id",
"location": "location",
"project_id": "project-id"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="google_vertex_ai_embeddingss",
inference_config={
"service": "googlevertexai",
"service_settings": {
"service_account_json": "service-account-json",
"model_id": "model-id",
"location": "location",
"project_id": "project-id"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "google_vertex_ai_embeddingss",
inference_config: {
service: "googlevertexai",
service_settings: {
service_account_json: "service-account-json",
model_id: "model-id",
location: "location",
project_id: "project-id",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "google_vertex_ai_embeddingss",
body: {
"service": "googlevertexai",
"service_settings": {
"service_account_json": "service-account-json",
"model_id": "model-id",
"location": "location",
"project_id": "project-id"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "google_vertex_ai_embeddingss",
"body" => [
"service" => "googlevertexai",
"service_settings" => [
"service_account_json" => "service-account-json",
"model_id" => "model-id",
"location" => "location",
"project_id" => "project-id",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"googlevertexai","service_settings":{"service_account_json":"service-account-json","model_id":"model-id","location":"location","project_id":"project-id"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/google_vertex_ai_embeddingss"client.inference().put(p -> p
.inferenceId("google_vertex_ai_embeddingss")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("googlevertexai")
.serviceSettings(JsonData.fromJson("{\"service_account_json\":\"service-account-json\",\"model_id\":\"model-id\",\"location\":\"location\",\"project_id\":\"project-id\"}"))
)
);
{
"service": "googlevertexai",
"service_settings": {
"service_account_json": "service-account-json",
"model_id": "model-id",
"location": "location",
"project_id": "project-id"
}
}{
"service": "googlevertexai",
"service_settings": {
"service_account_json": "service-account-json",
"project_id": "project-id"
}
}Create an inference endpoint to perform an inference task with the hugging_face service.
Supported tasks include: text_embedding, completion, and chat_completion.
To configure the endpoint, first visit the Hugging Face Inference Endpoints page and create a new endpoint. Select a model that supports the task you intend to use.
For Elastic's text_embedding task:
The selected model must support the Sentence Embeddings task. On the new endpoint creation page, select the Sentence Embeddings task under the Advanced Configuration section.
After the endpoint has initialized, copy the generated endpoint URL.
Recommended models for text_embedding task:
all-MiniLM-L6-v2all-MiniLM-L12-v2all-mpnet-base-v2e5-base-v2e5-small-v2multilingual-e5-basemultilingual-e5-smallFor Elastic's chat_completion and completion tasks:
The selected model must support the Text Generation task and expose OpenAI API. HuggingFace supports both serverless and dedicated endpoints for Text Generation. When creating dedicated endpoint select the Text Generation task.
After the endpoint is initialized (for dedicated) or ready (for serverless), ensure it supports the OpenAI API and includes /v1/chat/completions part in URL. Then, copy the full endpoint URL for use.
Recommended models for chat_completion and completion tasks:
Mistral-7B-Instruct-v0.2QwQ-32BPhi-3-mini-128k-instructFor Elastic's rerank task:
The selected model must support the sentence-ranking task and expose OpenAI API.
HuggingFace supports only dedicated (not serverless) endpoints for Rerank so far.
After the endpoint is initialized, copy the full endpoint URL for use.
Tested models for rerank task:
bge-reranker-basejina-reranker-v1-turbo-en-GGUFmanage_inferenceThe type of the inference task that the model will perform.
Values are chat_completion, completion, rerank, or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/hugging-face-embeddings
{
"service": "hugging_face",
"service_settings": {
"api_key": "hugging-face-access-token",
"url": "url-endpoint"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="hugging-face-embeddings",
inference_config={
"service": "hugging_face",
"service_settings": {
"api_key": "hugging-face-access-token",
"url": "url-endpoint"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "hugging-face-embeddings",
inference_config: {
service: "hugging_face",
service_settings: {
api_key: "hugging-face-access-token",
url: "url-endpoint",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "hugging-face-embeddings",
body: {
"service": "hugging_face",
"service_settings": {
"api_key": "hugging-face-access-token",
"url": "url-endpoint"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "hugging-face-embeddings",
"body" => [
"service" => "hugging_face",
"service_settings" => [
"api_key" => "hugging-face-access-token",
"url" => "url-endpoint",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"hugging_face","service_settings":{"api_key":"hugging-face-access-token","url":"url-endpoint"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/hugging-face-embeddings"client.inference().put(p -> p
.inferenceId("hugging-face-embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("hugging_face")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"hugging-face-access-token\",\"url\":\"url-endpoint\"}"))
)
);
{
"service": "hugging_face",
"service_settings": {
"api_key": "hugging-face-access-token",
"url": "url-endpoint"
}
}{
"service": "hugging_face",
"service_settings": {
"api_key": "hugging-face-access-token",
"url": "url-endpoint"
},
"task_settings": {
"return_documents": true,
"top_n": 3
}
}Create an inference endpoint to perform an inference task with the jinaai service.
To review the available rerank models, refer to https://jina.ai/reranker.
To review the available text_embedding models, refer to the https://jina.ai/embeddings/.
manage_inferenceThe type of the inference task that the model will perform.
Values are rerank or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/jinaai-embeddings
{
"service": "jinaai",
"service_settings": {
"model_id": "jina-embeddings-v3",
"api_key": "JinaAi-Api-key"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="jinaai-embeddings",
inference_config={
"service": "jinaai",
"service_settings": {
"model_id": "jina-embeddings-v3",
"api_key": "JinaAi-Api-key"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "jinaai-embeddings",
inference_config: {
service: "jinaai",
service_settings: {
model_id: "jina-embeddings-v3",
api_key: "JinaAi-Api-key",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "jinaai-embeddings",
body: {
"service": "jinaai",
"service_settings": {
"model_id": "jina-embeddings-v3",
"api_key": "JinaAi-Api-key"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "jinaai-embeddings",
"body" => [
"service" => "jinaai",
"service_settings" => [
"model_id" => "jina-embeddings-v3",
"api_key" => "JinaAi-Api-key",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"jinaai","service_settings":{"model_id":"jina-embeddings-v3","api_key":"JinaAi-Api-key"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/jinaai-embeddings"client.inference().put(p -> p
.inferenceId("jinaai-embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("jinaai")
.serviceSettings(JsonData.fromJson("{\"model_id\":\"jina-embeddings-v3\",\"api_key\":\"JinaAi-Api-key\"}"))
)
);
{
"service": "jinaai",
"service_settings": {
"model_id": "jina-embeddings-v3",
"api_key": "JinaAi-Api-key"
}
}{
"service": "jinaai",
"service_settings": {
"api_key": "JinaAI-Api-key",
"model_id": "jina-reranker-v2-base-multilingual"
},
"task_settings": {
"top_n": 10,
"return_documents": true
}
}The type of the inference task that the model will perform.
Values are text_embedding, completion, or chat_completion.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/mistral-embeddings-test
{
"service": "mistral",
"service_settings": {
"api_key": "Mistral-API-Key",
"model": "mistral-embed"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="mistral-embeddings-test",
inference_config={
"service": "mistral",
"service_settings": {
"api_key": "Mistral-API-Key",
"model": "mistral-embed"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "mistral-embeddings-test",
inference_config: {
service: "mistral",
service_settings: {
api_key: "Mistral-API-Key",
model: "mistral-embed",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "mistral-embeddings-test",
body: {
"service": "mistral",
"service_settings": {
"api_key": "Mistral-API-Key",
"model": "mistral-embed"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "mistral-embeddings-test",
"body" => [
"service" => "mistral",
"service_settings" => [
"api_key" => "Mistral-API-Key",
"model" => "mistral-embed",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"mistral","service_settings":{"api_key":"Mistral-API-Key","model":"mistral-embed"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/mistral-embeddings-test"client.inference().put(p -> p
.inferenceId("mistral-embeddings-test")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("mistral")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"Mistral-API-Key\",\"model\":\"mistral-embed\"}"))
)
);
{
"service": "mistral",
"service_settings": {
"api_key": "Mistral-API-Key",
"model": "mistral-embed"
}
}The type of the inference task that the model will perform.
NOTE: The chat_completion task type only supports streaming and only through the _stream API.
Values are chat_completion, completion, or text_embedding.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/openai-embeddings
{
"service": "openai",
"service_settings": {
"api_key": "OpenAI-API-Key",
"model_id": "text-embedding-3-small",
"dimensions": 128
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="openai-embeddings",
inference_config={
"service": "openai",
"service_settings": {
"api_key": "OpenAI-API-Key",
"model_id": "text-embedding-3-small",
"dimensions": 128
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "openai-embeddings",
inference_config: {
service: "openai",
service_settings: {
api_key: "OpenAI-API-Key",
model_id: "text-embedding-3-small",
dimensions: 128,
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "openai-embeddings",
body: {
"service": "openai",
"service_settings": {
"api_key": "OpenAI-API-Key",
"model_id": "text-embedding-3-small",
"dimensions": 128
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "openai-embeddings",
"body" => [
"service" => "openai",
"service_settings" => [
"api_key" => "OpenAI-API-Key",
"model_id" => "text-embedding-3-small",
"dimensions" => 128,
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"openai","service_settings":{"api_key":"OpenAI-API-Key","model_id":"text-embedding-3-small","dimensions":128}}' "$ELASTICSEARCH_URL/_inference/text_embedding/openai-embeddings"client.inference().put(p -> p
.inferenceId("openai-embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("openai")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"OpenAI-API-Key\",\"model_id\":\"text-embedding-3-small\",\"dimensions\":128}"))
)
);
{
"service": "openai",
"service_settings": {
"api_key": "OpenAI-API-Key",
"model_id": "text-embedding-3-small",
"dimensions": 128
}
}{
"service": "amazonbedrock",
"service_settings": {
"access_key": "AWS-access-key",
"secret_key": "AWS-secret-key",
"region": "us-east-1",
"provider": "amazontitan",
"model": "amazon.titan-text-premier-v1:0"
}
}The type of the inference task that the model will perform.
Values are text_embedding or rerank.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/openai-embeddings
{
"service": "voyageai",
"service_settings": {
"model_id": "voyage-3-large",
"dimensions": 512
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="openai-embeddings",
inference_config={
"service": "voyageai",
"service_settings": {
"model_id": "voyage-3-large",
"dimensions": 512
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "openai-embeddings",
inference_config: {
service: "voyageai",
service_settings: {
model_id: "voyage-3-large",
dimensions: 512,
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "openai-embeddings",
body: {
"service": "voyageai",
"service_settings": {
"model_id": "voyage-3-large",
"dimensions": 512
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "openai-embeddings",
"body" => [
"service" => "voyageai",
"service_settings" => [
"model_id" => "voyage-3-large",
"dimensions" => 512,
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"voyageai","service_settings":{"model_id":"voyage-3-large","dimensions":512}}' "$ELASTICSEARCH_URL/_inference/text_embedding/openai-embeddings"client.inference().put(p -> p
.inferenceId("openai-embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("voyageai")
.serviceSettings(JsonData.fromJson("{\"model_id\":\"voyage-3-large\",\"dimensions\":512}"))
)
);
{
"service": "voyageai",
"service_settings": {
"model_id": "voyage-3-large",
"dimensions": 512
}
}{
"service": "voyageai",
"service_settings": {
"model_id": "rerank-2"
}
}Create an inference endpoint to perform an inference task with the watsonxai service.
You need an IBM Cloud Databases for Elasticsearch deployment to use the watsonxai inference service.
You can provision one through the IBM catalog, the Cloud Databases CLI plug-in, the Cloud Databases API, or Terraform.
manage_inferenceThe type of the inference task that the model will perform.
Values are text_embedding, chat_completion, or completion.
The unique identifier of the inference endpoint.
PUT _inference/text_embedding/watsonx-embeddings
{
"service": "watsonxai",
"service_settings": {
"api_key": "Watsonx-API-Key",
"url": "Wastonx-URL",
"model_id": "ibm/slate-30m-english-rtrvr",
"project_id": "IBM-Cloud-ID",
"api_version": "2024-03-14"
}
}resp = client.inference.put(
task_type="text_embedding",
inference_id="watsonx-embeddings",
inference_config={
"service": "watsonxai",
"service_settings": {
"api_key": "Watsonx-API-Key",
"url": "Wastonx-URL",
"model_id": "ibm/slate-30m-english-rtrvr",
"project_id": "IBM-Cloud-ID",
"api_version": "2024-03-14"
}
},
)const response = await client.inference.put({
task_type: "text_embedding",
inference_id: "watsonx-embeddings",
inference_config: {
service: "watsonxai",
service_settings: {
api_key: "Watsonx-API-Key",
url: "Wastonx-URL",
model_id: "ibm/slate-30m-english-rtrvr",
project_id: "IBM-Cloud-ID",
api_version: "2024-03-14",
},
},
});response = client.inference.put(
task_type: "text_embedding",
inference_id: "watsonx-embeddings",
body: {
"service": "watsonxai",
"service_settings": {
"api_key": "Watsonx-API-Key",
"url": "Wastonx-URL",
"model_id": "ibm/slate-30m-english-rtrvr",
"project_id": "IBM-Cloud-ID",
"api_version": "2024-03-14"
}
}
)$resp = $client->inference()->put([
"task_type" => "text_embedding",
"inference_id" => "watsonx-embeddings",
"body" => [
"service" => "watsonxai",
"service_settings" => [
"api_key" => "Watsonx-API-Key",
"url" => "Wastonx-URL",
"model_id" => "ibm/slate-30m-english-rtrvr",
"project_id" => "IBM-Cloud-ID",
"api_version" => "2024-03-14",
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"service":"watsonxai","service_settings":{"api_key":"Watsonx-API-Key","url":"Wastonx-URL","model_id":"ibm/slate-30m-english-rtrvr","project_id":"IBM-Cloud-ID","api_version":"2024-03-14"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/watsonx-embeddings"client.inference().put(p -> p
.inferenceId("watsonx-embeddings")
.taskType(TaskType.TextEmbedding)
.inferenceConfig(i -> i
.service("watsonxai")
.serviceSettings(JsonData.fromJson("{\"api_key\":\"Watsonx-API-Key\",\"url\":\"Wastonx-URL\",\"model_id\":\"ibm/slate-30m-english-rtrvr\",\"project_id\":\"IBM-Cloud-ID\",\"api_version\":\"2024-03-14\"}"))
)
);
{
"service": "watsonxai",
"service_settings": {
"api_key": "Watsonx-API-Key",
"url": "Wastonx-URL",
"model_id": "ibm/slate-30m-english-rtrvr",
"project_id": "IBM-Cloud-ID",
"api_version": "2024-03-14"
}
}The amount of time to wait for the inference request to complete.
Values are -1 or 0.
POST _inference/rerank/cohere_rerank
{
"input": ["luke", "like", "leia", "chewy","r2d2", "star", "wars"],
"query": "star wars main character"
}resp = client.inference.rerank(
inference_id="cohere_rerank",
input=[
"luke",
"like",
"leia",
"chewy",
"r2d2",
"star",
"wars"
],
query="star wars main character",
)const response = await client.inference.rerank({
inference_id: "cohere_rerank",
input: ["luke", "like", "leia", "chewy", "r2d2", "star", "wars"],
query: "star wars main character",
});response = client.inference.rerank(
inference_id: "cohere_rerank",
body: {
"input": [
"luke",
"like",
"leia",
"chewy",
"r2d2",
"star",
"wars"
],
"query": "star wars main character"
}
)$resp = $client->inference()->rerank([
"inference_id" => "cohere_rerank",
"body" => [
"input" => array(
"luke",
"like",
"leia",
"chewy",
"r2d2",
"star",
"wars",
),
"query" => "star wars main character",
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"input":["luke","like","leia","chewy","r2d2","star","wars"],"query":"star wars main character"}' "$ELASTICSEARCH_URL/_inference/rerank/cohere_rerank"client.inference().rerank(r -> r
.inferenceId("cohere_rerank")
.input(List.of("luke","like","leia","chewy","r2d2","star","wars"))
.query("star wars main character")
);
{
"input": ["luke", "like", "leia", "chewy","r2d2", "star", "wars"],
"query": "star wars main character"
}{
"input": ["luke", "like", "leia", "chewy","r2d2", "star", "wars"],
"query": "star wars main character",
"return_documents": false,
"top_n": 2
}{
"input": ["luke", "like", "leia", "chewy","r2d2", "star", "wars"],
"query": "star wars main character",
"return_documents": true,
"top_n": 3
}{
"rerank": [
{
"index": "2",
"relevance_score": "0.011597361",
"text": "leia"
},
{
"index": "0",
"relevance_score": "0.006338922",
"text": "luke"
},
{
"index": "5",
"relevance_score": "0.0016166499",
"text": "star"
},
{
"index": "4",
"relevance_score": "0.0011695103",
"text": "r2d2"
},
{
"index": "1",
"relevance_score": "5.614787E-4",
"text": "like"
},
{
"index": "6",
"relevance_score": "3.7850367E-4",
"text": "wars"
},
{
"index": "3",
"relevance_score": "1.2508839E-5",
"text": "chewy"
}
]
}{
"rerank": [
{
"index": 6,
"relevance_score": 0.50955844
},
{
"index": 5,
"relevance_score": 0.084341794
}
]
}{
"rerank": [
{
"index": 6,
"relevance_score": 0.50955844,
"text": "wars"
},
{
"index": 5,
"relevance_score": 0.084341794,
"text": "star"
},
{
"index": 3,
"relevance_score": 0.004520818,
"text": "chewy"
}
]
}Specifies the amount of time to wait for the inference request to complete.
Values are -1 or 0.
POST _inference/sparse_embedding/my-elser-model
{
"input": "The sky above the port was the color of television tuned to a dead channel."
}resp = client.inference.sparse_embedding(
inference_id="my-elser-model",
input="The sky above the port was the color of television tuned to a dead channel.",
)const response = await client.inference.sparseEmbedding({
inference_id: "my-elser-model",
input:
"The sky above the port was the color of television tuned to a dead channel.",
});response = client.inference.sparse_embedding(
inference_id: "my-elser-model",
body: {
"input": "The sky above the port was the color of television tuned to a dead channel."
}
)$resp = $client->inference()->sparseEmbedding([
"inference_id" => "my-elser-model",
"body" => [
"input" => "The sky above the port was the color of television tuned to a dead channel.",
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"input":"The sky above the port was the color of television tuned to a dead channel."}' "$ELASTICSEARCH_URL/_inference/sparse_embedding/my-elser-model"client.inference().sparseEmbedding(s -> s
.inferenceId("my-elser-model")
.input("The sky above the port was the color of television tuned to a dead channel.")
);
{
"input": "The sky above the port was the color of television tuned to a dead channel."
}{
"sparse_embedding": [
{
"port": 2.1259406,
"sky": 1.7073475,
"color": 1.6922266,
"dead": 1.6247464,
"television": 1.3525393,
"above": 1.2425821,
"tuned": 1.1440028,
"colors": 1.1218185,
"tv": 1.0111054,
"ports": 1.0067928,
"poem": 1.0042328,
"channel": 0.99471164,
"tune": 0.96235967,
"scene": 0.9020516
}
]
}Specifies the amount of time to wait for the inference request to complete.
Values are -1 or 0.
POST _inference/text_embedding/my-cohere-endpoint
{
"input": "The sky above the port was the color of television tuned to a dead channel.",
"task_settings": {
"input_type": "ingest"
}
}resp = client.inference.text_embedding(
inference_id="my-cohere-endpoint",
input="The sky above the port was the color of television tuned to a dead channel.",
task_settings={
"input_type": "ingest"
},
)const response = await client.inference.textEmbedding({
inference_id: "my-cohere-endpoint",
input:
"The sky above the port was the color of television tuned to a dead channel.",
task_settings: {
input_type: "ingest",
},
});response = client.inference.text_embedding(
inference_id: "my-cohere-endpoint",
body: {
"input": "The sky above the port was the color of television tuned to a dead channel.",
"task_settings": {
"input_type": "ingest"
}
}
)$resp = $client->inference()->textEmbedding([
"inference_id" => "my-cohere-endpoint",
"body" => [
"input" => "The sky above the port was the color of television tuned to a dead channel.",
"task_settings" => [
"input_type" => "ingest",
],
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"input":"The sky above the port was the color of television tuned to a dead channel.","task_settings":{"input_type":"ingest"}}' "$ELASTICSEARCH_URL/_inference/text_embedding/my-cohere-endpoint"client.inference().textEmbedding(t -> t
.inferenceId("my-cohere-endpoint")
.input("The sky above the port was the color of television tuned to a dead channel.")
.taskSettings(JsonData.fromJson("{\"input_type\":\"ingest\"}"))
);
{
"input": "The sky above the port was the color of television tuned to a dead channel.",
"task_settings": {
"input_type": "ingest"
}
}{
"text_embedding": [
{
"embedding": [
{
0.018569946,
-0.036895752,
0.01486969,
-0.0045204163,
-0.04385376,
0.0075950623,
0.04260254,
-0.004005432,
0.007865906,
0.030792236,
-0.050476074,
0.011795044,
-0.011642456,
-0.010070801
}
]
}
]
}GET /
resp = client.info()const response = await client.info();response = client.info$resp = $client->info();curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/"client.info();
{
"name": "instance-0000000000",
"cluster_name": "my_test_cluster",
"cluster_uuid": "5QaxoN0pRZuOmWSxstBBwQ",
"version": {
"build_date": "2024-02-01T13:07:13.727175297Z",
"minimum_wire_compatibility_version": "7.17.0",
"build_hash": "6185ba65d27469afabc9bc951cded6c17c21e3f3",
"number": "8.12.1",
"lucene_version": "9.9.2",
"minimum_index_compatibility_version": "7.0.0",
"build_flavor": "default",
"build_snapshot": false,
"build_type": "docker"
},
"tagline": "You Know, for Search"
}All methods and paths for this operation:
Get information about one or more ingest pipelines. This API returns a local reference of the pipeline.
Comma-separated list of pipeline IDs to retrieve.
Wildcard (*) expressions are supported.
To get all ingest pipelines, omit this parameter or use *.
GET /_ingest/pipeline/my-pipeline-id
resp = client.ingest.get_pipeline(
id="my-pipeline-id",
)const response = await client.ingest.getPipeline({
id: "my-pipeline-id",
});response = client.ingest.get_pipeline(
id: "my-pipeline-id"
)$resp = $client->ingest()->getPipeline([
"id" => "my-pipeline-id",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ingest/pipeline/my-pipeline-id"client.ingest().getPipeline(g -> g
.id("my-pipeline-id")
);
{
"my-pipeline-id" : {
"description" : "describe pipeline",
"version" : 123,
"processors" : [
{
"set" : {
"field" : "foo",
"value" : "bar"
}
}
]
}
}Changes made using this API take effect immediately.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Required version for optimistic concurrency control for pipeline updates
Description of the ingest pipeline.
Processors to run immediately after a processor failure. Each processor supports a processor-level on_failure value. If a processor without an on_failure value fails, Elasticsearch uses this pipeline-level parameter as a fallback. The processors in this parameter run sequentially in the order specified. Elasticsearch will not attempt to run the pipeline's remaining processors.
Processors used to perform transformations on documents before indexing. Processors run sequentially in the order specified.
Marks this ingest pipeline as deprecated. When a deprecated ingest pipeline is referenced as the default or final pipeline when creating or updating a non-deprecated index template, Elasticsearch will emit a deprecation warning.
Default value is false.
PUT _ingest/pipeline/my-pipeline-id
{
"description" : "My optional pipeline description",
"processors" : [
{
"set" : {
"description" : "My optional processor description",
"field": "my-keyword-field",
"value": "foo"
}
}
]
}resp = client.ingest.put_pipeline(
id="my-pipeline-id",
description="My optional pipeline description",
processors=[
{
"set": {
"description": "My optional processor description",
"field": "my-keyword-field",
"value": "foo"
}
}
],
)const response = await client.ingest.putPipeline({
id: "my-pipeline-id",
description: "My optional pipeline description",
processors: [
{
set: {
description: "My optional processor description",
field: "my-keyword-field",
value: "foo",
},
},
],
});response = client.ingest.put_pipeline(
id: "my-pipeline-id",
body: {
"description": "My optional pipeline description",
"processors": [
{
"set": {
"description": "My optional processor description",
"field": "my-keyword-field",
"value": "foo"
}
}
]
}
)$resp = $client->ingest()->putPipeline([
"id" => "my-pipeline-id",
"body" => [
"description" => "My optional pipeline description",
"processors" => array(
[
"set" => [
"description" => "My optional processor description",
"field" => "my-keyword-field",
"value" => "foo",
],
],
),
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"description":"My optional pipeline description","processors":[{"set":{"description":"My optional processor description","field":"my-keyword-field","value":"foo"}}]}' "$ELASTICSEARCH_URL/_ingest/pipeline/my-pipeline-id"client.ingest().putPipeline(p -> p
.description("My optional pipeline description")
.id("my-pipeline-id")
.processors(pr -> pr
.set(s -> s
.field("my-keyword-field")
.value(JsonData.fromJson("\"foo\""))
.description("My optional processor description")
)
)
);
{
"description" : "My optional pipeline description",
"processors" : [
{
"set" : {
"description" : "My optional processor description",
"field": "my-keyword-field",
"value": "foo"
}
}
]
}{
"description" : "My optional pipeline description",
"processors" : [
{
"set" : {
"description" : "My optional processor description",
"field": "my-keyword-field",
"value": "foo"
}
}
],
"_meta": {
"reason": "set my-keyword-field to foo",
"serialization": {
"class": "MyPipeline",
"id": 10
}
}
}Delete one or more ingest pipelines.
Pipeline ID or wildcard expression of pipeline IDs used to limit the request.
To delete all ingest pipelines in a cluster, use a value of *.
Period to wait for a connection to the master node. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
Values are -1 or 0.
DELETE /_ingest/pipeline/my-pipeline-id
resp = client.ingest.delete_pipeline(
id="my-pipeline-id",
)const response = await client.ingest.deletePipeline({
id: "my-pipeline-id",
});response = client.ingest.delete_pipeline(
id: "my-pipeline-id"
)$resp = $client->ingest()->deletePipeline([
"id" => "my-pipeline-id",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ingest/pipeline/my-pipeline-id"client.ingest().deletePipeline(d -> d
.id("my-pipeline-id")
);
Extract structured fields out of a single text field within a document. You must choose which field to extract matched fields from, as well as the grok pattern you expect will match. A grok pattern is like a regular expression that supports aliased expressions that can be reused.
GET _ingest/processor/grok
resp = client.ingest.processor_grok()const response = await client.ingest.processorGrok();response = client.ingest.processor_grok$resp = $client->ingest()->processorGrok();curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ingest/processor/grok"client.ingest().processorGrok();
All methods and paths for this operation:
Run an ingest pipeline against a set of provided documents. You can either specify an existing pipeline to use with the provided documents or supply a pipeline definition in the body of the request.
read_pipelineThe pipeline to test.
If you don't specify a pipeline in the request body, this parameter is required.
If true, the response includes output data for each processor in the executed pipeline.
POST /_ingest/pipeline/_simulate
{
"pipeline" :
{
"description": "_description",
"processors": [
{
"set" : {
"field" : "field2",
"value" : "_value"
}
}
]
},
"docs": [
{
"_index": "index",
"_id": "id",
"_source": {
"foo": "bar"
}
},
{
"_index": "index",
"_id": "id",
"_source": {
"foo": "rab"
}
}
]
}resp = client.ingest.simulate(
pipeline={
"description": "_description",
"processors": [
{
"set": {
"field": "field2",
"value": "_value"
}
}
]
},
docs=[
{
"_index": "index",
"_id": "id",
"_source": {
"foo": "bar"
}
},
{
"_index": "index",
"_id": "id",
"_source": {
"foo": "rab"
}
}
],
)const response = await client.ingest.simulate({
pipeline: {
description: "_description",
processors: [
{
set: {
field: "field2",
value: "_value",
},
},
],
},
docs: [
{
_index: "index",
_id: "id",
_source: {
foo: "bar",
},
},
{
_index: "index",
_id: "id",
_source: {
foo: "rab",
},
},
],
});response = client.ingest.simulate(
body: {
"pipeline": {
"description": "_description",
"processors": [
{
"set": {
"field": "field2",
"value": "_value"
}
}
]
},
"docs": [
{
"_index": "index",
"_id": "id",
"_source": {
"foo": "bar"
}
},
{
"_index": "index",
"_id": "id",
"_source": {
"foo": "rab"
}
}
]
}
)$resp = $client->ingest()->simulate([
"body" => [
"pipeline" => [
"description" => "_description",
"processors" => array(
[
"set" => [
"field" => "field2",
"value" => "_value",
],
],
),
],
"docs" => array(
[
"_index" => "index",
"_id" => "id",
"_source" => [
"foo" => "bar",
],
],
[
"_index" => "index",
"_id" => "id",
"_source" => [
"foo" => "rab",
],
],
),
],
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"pipeline":{"description":"_description","processors":[{"set":{"field":"field2","value":"_value"}}]},"docs":[{"_index":"index","_id":"id","_source":{"foo":"bar"}},{"_index":"index","_id":"id","_source":{"foo":"rab"}}]}' "$ELASTICSEARCH_URL/_ingest/pipeline/_simulate"client.ingest().simulate(s -> s
.docs(List.of(Document.of(d -> d
.id("id")
.index("index")
.source(JsonData.fromJson("{\"foo\":\"bar\"}"))),Document.of(d -> d
.id("id")
.index("index")
.source(JsonData.fromJson("{\"foo\":\"rab\"}")))))
.pipeline(p -> p
.description("_description")
.processors(pr -> pr
.set(se -> se
.field("field2")
.value(JsonData.fromJson("\"_value\""))
)
)
)
);
{
"pipeline" :
{
"description": "_description",
"processors": [
{
"set" : {
"field" : "field2",
"value" : "_value"
}
}
]
},
"docs": [
{
"_index": "index",
"_id": "id",
"_source": {
"foo": "bar"
}
},
{
"_index": "index",
"_id": "id",
"_source": {
"foo": "rab"
}
}
]
}{
"docs": [
{
"doc": {
"_id": "id",
"_index": "index",
"_version": "-3",
"_source": {
"field2": "_value",
"foo": "bar"
},
"_ingest": {
"timestamp": "2017-05-04T22:30:03.187Z"
}
}
},
{
"doc": {
"_id": "id",
"_index": "index",
"_version": "-3",
"_source": {
"field2": "_value",
"foo": "rab"
},
"_ingest": {
"timestamp": "2017-05-04T22:30:03.188Z"
}
}
}
]
}Licensing APIs enable you to manage your licenses.
Get information about your Elastic license including its type, its status, when it was issued, and when it expires.
If the master node is generating a new cluster state, the get license API may return a 404 Not Found response.
If you receive an unexpected 404 response after cluster startup, wait a short period and retry the request.
If true, this parameter returns enterprise for Enterprise license types. If false, this parameter returns platinum for both platinum and enterprise license types. This behavior is maintained for backwards compatibility.
This parameter is deprecated and will always be set to true in 8.x.
Specifies whether to retrieve local information. The default value is false, which means the information is retrieved from the master node.
GET /_license
resp = client.license.get()const response = await client.license.get();response = client.license.get$resp = $client->license()->get();curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_license"client.license().get(g -> g);
{
"license" : {
"status" : "active",
"uid" : "cbff45e7-c553-41f7-ae4f-9205eabd80xx",
"type" : "trial",
"issue_date" : "2018-10-20T22:05:12.332Z",
"issue_date_in_millis" : 1540073112332,
"expiry_date" : "2018-11-19T22:05:12.332Z",
"expiry_date_in_millis" : 1542665112332,
"max_nodes" : 1000,
"max_resource_units" : null,
"issued_to" : "test",
"issuer" : "elasticsearch",
"start_date_in_millis" : -1
}
}Logstash APIs enable you to manage pipelines that are used by Logstash Central Management.
All methods and paths for this operation:
Get pipelines that are used for Logstash Central Management.
manage_logstash_pipelinesGET _logstash/pipeline/my_pipeline
resp = client.logstash.get_pipeline(
id="my_pipeline",
)const response = await client.logstash.getPipeline({
id: "my_pipeline",
});response = client.logstash.get_pipeline(
id: "my_pipeline"
)$resp = $client->logstash()->getPipeline([
"id" => "my_pipeline",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_logstash/pipeline/my_pipeline"client.logstash().getPipeline(g -> g
.id("my_pipeline")
);
{
"my_pipeline": {
"description": "Sample pipeline for illustration purposes",
"last_modified": "2021-01-02T02:50:51.250Z",
"pipeline_metadata": {
"type": "logstash_pipeline",
"version": "1"
},
"username": "elastic",
"pipeline": "input {}\\n filter { grok {} }\\n output {}",
"pipeline_settings": {
"pipeline.workers": 1,
"pipeline.batch.size": 125,
"pipeline.batch.delay": 50,
"queue.type": "memory",
"queue.max_bytes": "1gb",
"queue.checkpoint.writes": 1024
}
}
}Create a pipeline that is used for Logstash Central Management. If the specified pipeline exists, it is replaced.
manage_logstash_pipelinesA description of the pipeline. This description is not used by Elasticsearch or Logstash.
The configuration for the pipeline.
The user who last updated the pipeline.
PUT _logstash/pipeline/my_pipeline
{
"description": "Sample pipeline for illustration purposes",
"last_modified": "2021-01-02T02:50:51.250Z",
"pipeline_metadata": {
"type": "logstash_pipeline",
"version": 1
},
"username": "elastic",
"pipeline": "input {}\\n filter { grok {} }\\n output {}",
"pipeline_settings": {
"pipeline.workers": 1,
"pipeline.batch.size": 125,
"pipeline.batch.delay": 50,
"queue.type": "memory",
"queue.max_bytes": "1gb",
"queue.checkpoint.writes": 1024
}
}resp = client.logstash.put_pipeline(
id="my_pipeline",
pipeline={
"description": "Sample pipeline for illustration purposes",
"last_modified": "2021-01-02T02:50:51.250Z",
"pipeline_metadata": {
"type": "logstash_pipeline",
"version": 1
},
"username": "elastic",
"pipeline": "input {}\\n filter { grok {} }\\n output {}",
"pipeline_settings": {
"pipeline.workers": 1,
"pipeline.batch.size": 125,
"pipeline.batch.delay": 50,
"queue.type": "memory",
"queue.max_bytes": "1gb",
"queue.checkpoint.writes": 1024
}
},
)const response = await client.logstash.putPipeline({
id: "my_pipeline",
pipeline: {
description: "Sample pipeline for illustration purposes",
last_modified: "2021-01-02T02:50:51.250Z",
pipeline_metadata: {
type: "logstash_pipeline",
version: 1,
},
username: "elastic",
pipeline: "input {}\\n filter { grok {} }\\n output {}",
pipeline_settings: {
"pipeline.workers": 1,
"pipeline.batch.size": 125,
"pipeline.batch.delay": 50,
"queue.type": "memory",
"queue.max_bytes": "1gb",
"queue.checkpoint.writes": 1024,
},
},
});response = client.logstash.put_pipeline(
id: "my_pipeline",
body: {
"description": "Sample pipeline for illustration purposes",
"last_modified": "2021-01-02T02:50:51.250Z",
"pipeline_metadata": {
"type": "logstash_pipeline",
"version": 1
},
"username": "elastic",
"pipeline": "input {}\\n filter { grok {} }\\n output {}",
"pipeline_settings": {
"pipeline.workers": 1,
"pipeline.batch.size": 125,
"pipeline.batch.delay": 50,
"queue.type": "memory",
"queue.max_bytes": "1gb",
"queue.checkpoint.writes": 1024
}
}
)$resp = $client->logstash()->putPipeline([
"id" => "my_pipeline",
"body" => [
"description" => "Sample pipeline for illustration purposes",
"last_modified" => "2021-01-02T02:50:51.250Z",
"pipeline_metadata" => [
"type" => "logstash_pipeline",
"version" => 1,
],
"username" => "elastic",
"pipeline" => "input {}\\n filter { grok {} }\\n output {}",
"pipeline_settings" => [
"pipeline.workers" => 1,
"pipeline.batch.size" => 125,
"pipeline.batch.delay" => 50,
"queue.type" => "memory",
"queue.max_bytes" => "1gb",
"queue.checkpoint.writes" => 1024,
],
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"description":"Sample pipeline for illustration purposes","last_modified":"2021-01-02T02:50:51.250Z","pipeline_metadata":{"type":"logstash_pipeline","version":1},"username":"elastic","pipeline":"input {}\\n filter { grok {} }\\n output {}","pipeline_settings":{"pipeline.workers":1,"pipeline.batch.size":125,"pipeline.batch.delay":50,"queue.type":"memory","queue.max_bytes":"1gb","queue.checkpoint.writes":1024}}' "$ELASTICSEARCH_URL/_logstash/pipeline/my_pipeline"client.logstash().putPipeline(p -> p
.id("my_pipeline")
.pipeline(pi -> pi
.description("Sample pipeline for illustration purposes")
.lastModified(DateTime.of("2021-01-02T02:50:51.250Z"))
.pipeline("input {}\n filter { grok {} }\n output {}")
.pipelineMetadata(pip -> pip
.type("logstash_pipeline")
.version("1")
)
.pipelineSettings(pip -> pip
.pipelineWorkers(1)
.pipelineBatchSize(125)
.pipelineBatchDelay(50)
.queueType("memory")
.queueMaxBytes("1gb")
.queueCheckpointWrites(1024)
)
.username("elastic")
)
);
{
"description": "Sample pipeline for illustration purposes",
"last_modified": "2021-01-02T02:50:51.250Z",
"pipeline_metadata": {
"type": "logstash_pipeline",
"version": 1
},
"username": "elastic",
"pipeline": "input {}\\n filter { grok {} }\\n output {}",
"pipeline_settings": {
"pipeline.workers": 1,
"pipeline.batch.size": 125,
"pipeline.batch.delay": 50,
"queue.type": "memory",
"queue.max_bytes": "1gb",
"queue.checkpoint.writes": 1024
}
}Delete a pipeline that is used for Logstash Central Management. If the request succeeds, you receive an empty response with an appropriate status code.
manage_logstash_pipelinesDELETE _logstash/pipeline/my_pipeline
resp = client.logstash.delete_pipeline(
id="my_pipeline",
)const response = await client.logstash.deletePipeline({
id: "my_pipeline",
});response = client.logstash.delete_pipeline(
id: "my_pipeline"
)$resp = $client->logstash()->deletePipeline([
"id" => "my_pipeline",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_logstash/pipeline/my_pipeline"client.logstash().deletePipeline(d -> d
.id("my_pipeline")
);
A job can be opened and closed multiple times throughout its lifecycle. A closed job cannot receive data or perform analysis operations, but you can still explore and navigate results. When you close a job, it runs housekeeping tasks such as pruning the model history, 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. After it is closed, the job has a minimal overhead on the cluster except for maintaining its meta data. Therefore it is a best practice to close jobs that are no longer required to process data. If you close an anomaly detection job whose datafeed is running, the request first tries to stop the datafeed. This behavior is equivalent to calling stop datafeed API with the same timeout and force parameters as the close job request. When a datafeed that has a specified end date stops, it automatically closes its associated job.
manage_mlIdentifier for the anomaly detection job. It can be a job identifier, a group name, or a wildcard expression. You can close multiple anomaly detection jobs in a single API request by using a group name, a comma-separated list of jobs, or a wildcard expression. You can close all jobs by using _all or by specifying * as the job identifier.
Specifies what to do when the request: contains wildcard expressions and there are no jobs that match; contains the _all string or no identifiers and there are no matches; or contains wildcard expressions and there are only partial matches. By default, it returns an empty jobs array when there are no matches and the subset of results when there are partial matches.
If false, the request returns a 404 status code when there are no matches or only partial matches.
Use to close a failed job, or to forcefully close a job which has not responded to its initial close request; the request returns without performing the associated actions such as flushing buffers and persisting the model snapshots.
If you want the job to be in a consistent state after the close job API returns, do not set to true. This parameter should be used only in situations where the job has already failed or where you are not interested in results the job might have recently produced or might produce in the future.
Controls the time to wait until a job has closed.
Values are -1 or 0.
Refer to the description for the allow_no_match query parameter.
Default value is true.
Refer to the descriptiion for the force query parameter.
Default value is false.
A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and
d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
POST _ml/anomaly_detectors/low_request_rate/_close
resp = client.ml.close_job(
job_id="low_request_rate",
)const response = await client.ml.closeJob({
job_id: "low_request_rate",
});response = client.ml.close_job(
job_id: "low_request_rate"
)$resp = $client->ml()->closeJob([
"job_id" => "low_request_rate",
]);curl -X POST -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/anomaly_detectors/low_request_rate/_close"client.ml().closeJob(c -> c
.jobId("low_request_rate")
);
{
"closed": true
}PUT _ml/calendars/planned-outages
resp = client.ml.put_calendar(
calendar_id="planned-outages",
)const response = await client.ml.putCalendar({
calendar_id: "planned-outages",
});response = client.ml.put_calendar(
calendar_id: "planned-outages"
)$resp = $client->ml()->putCalendar([
"calendar_id" => "planned-outages",
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/calendars/planned-outages"client.ml().putCalendar(p -> p
.calendarId("planned-outages")
);
A string that uniquely identifies a calendar. You can get information for multiple calendars by using a comma-separated list of ids or a wildcard expression. You can get information for all calendars by using _all or * or by omitting the calendar identifier.
Skips the specified number of calendars. This parameter is supported only when you omit the calendar identifier.
Specifies the maximum number of calendars to obtain. This parameter is supported only when you omit the calendar identifier.
GET _ml/calendars/planned-outages
resp = client.ml.get_calendars(
calendar_id="planned-outages",
)const response = await client.ml.getCalendars({
calendar_id: "planned-outages",
});response = client.ml.get_calendars(
calendar_id: "planned-outages"
)$resp = $client->ml()->getCalendars([
"calendar_id" => "planned-outages",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/calendars/planned-outages"client.ml().getCalendars(g -> g
.calendarId("planned-outages")
);
DELETE _ml/calendars/planned-outages
resp = client.ml.delete_calendar(
calendar_id="planned-outages",
)const response = await client.ml.deleteCalendar({
calendar_id: "planned-outages",
});response = client.ml.delete_calendar(
calendar_id: "planned-outages"
)$resp = $client->ml()->deleteCalendar([
"calendar_id" => "planned-outages",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/calendars/planned-outages"client.ml().deleteCalendar(d -> d
.calendarId("planned-outages")
);
{
"acknowledged": true
}DELETE _ml/calendars/planned-outages/events/LS8LJGEBMTCMA-qz49st
resp = client.ml.delete_calendar_event(
calendar_id="planned-outages",
event_id="LS8LJGEBMTCMA-qz49st",
)const response = await client.ml.deleteCalendarEvent({
calendar_id: "planned-outages",
event_id: "LS8LJGEBMTCMA-qz49st",
});response = client.ml.delete_calendar_event(
calendar_id: "planned-outages",
event_id: "LS8LJGEBMTCMA-qz49st"
)$resp = $client->ml()->deleteCalendarEvent([
"calendar_id" => "planned-outages",
"event_id" => "LS8LJGEBMTCMA-qz49st",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/calendars/planned-outages/events/LS8LJGEBMTCMA-qz49st"client.ml().deleteCalendarEvent(d -> d
.calendarId("planned-outages")
.eventId("LS8LJGEBMTCMA-qz49st")
);
{
"acknowledged": true
}PUT _ml/calendars/planned-outages/jobs/total-requests
resp = client.ml.put_calendar_job(
calendar_id="planned-outages",
job_id="total-requests",
)const response = await client.ml.putCalendarJob({
calendar_id: "planned-outages",
job_id: "total-requests",
});response = client.ml.put_calendar_job(
calendar_id: "planned-outages",
job_id: "total-requests"
)$resp = $client->ml()->putCalendarJob([
"calendar_id" => "planned-outages",
"job_id" => "total-requests",
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/calendars/planned-outages/jobs/total-requests"client.ml().putCalendarJob(p -> p
.calendarId("planned-outages")
.jobId("total-requests")
);
DELETE _ml/calendars/planned-outages/jobs/total-requests
resp = client.ml.delete_calendar_job(
calendar_id="planned-outages",
job_id="total-requests",
)const response = await client.ml.deleteCalendarJob({
calendar_id: "planned-outages",
job_id: "total-requests",
});response = client.ml.delete_calendar_job(
calendar_id: "planned-outages",
job_id: "total-requests"
)$resp = $client->ml()->deleteCalendarJob([
"calendar_id" => "planned-outages",
"job_id" => "total-requests",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/calendars/planned-outages/jobs/total-requests"client.ml().deleteCalendarJob(d -> d
.calendarId("planned-outages")
.jobId("total-requests")
);
{
"calendar_id": "planned-outages",
"job_ids": []
}All methods and paths for this operation:
You can get information for multiple datafeeds in a single API request by
using a comma-separated list of datafeeds or a wildcard expression. You can
get information for all datafeeds by using _all, by specifying * as the
<feed_id>, or by omitting the <feed_id>.
This API returns a maximum of 10,000 datafeeds.
monitor_mlIdentifier for the datafeed. It can be a datafeed identifier or a wildcard expression. If you do not specify one of these options, the API returns information about all datafeeds.
Specifies what to do when the request:
_all string or no identifiers and there are no matches.The default value is true, which returns an empty datafeeds array
when there are no matches and the subset of results when there are
partial matches. If this parameter is false, the request returns a
404 status code when there are no matches or only partial matches.
Indicates if certain fields should be removed from the configuration on retrieval. This allows the configuration to be in an acceptable format to be retrieved and then added to another cluster.
GET _ml/datafeeds/datafeed-high_sum_total_sales
resp = client.ml.get_datafeeds(
datafeed_id="datafeed-high_sum_total_sales",
)const response = await client.ml.getDatafeeds({
datafeed_id: "datafeed-high_sum_total_sales",
});response = client.ml.get_datafeeds(
datafeed_id: "datafeed-high_sum_total_sales"
)$resp = $client->ml()->getDatafeeds([
"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"client.ml().getDatafeeds(g -> g
.datafeedId("datafeed-high_sum_total_sales")
);
Datafeeds retrieve data from Elasticsearch for analysis by an anomaly detection job.
You can associate only one datafeed with 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.
By default, the datafeed uses the following query:{"match_all": {"boost": 1}}`.
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.
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. Do not give users write privileges on the .ml-config index.
readmanage_mlA 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.
If true, wildcard indices expressions that resolve into no concrete indices are ignored. This includes the _all
string or when no indices are specified.
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.
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 with open, closed, or both.none: Wildcard expressions are not accepted.Values are all, open, closed, hidden, or none.
If true, concrete, expanded, or aliased indices are ignored when frozen.
If set, the datafeed performs aggregation searches. Support for aggregations is limited and should be used only with low cardinality data.
A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and
d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
Controls how to deal with unavailable concrete indices (closed or missing), how wildcard expressions are expanded to actual indices (all, closed or open indices) and how to deal with wildcard expressions that resolve to no indices.
If a real-time datafeed has never seen any data (including during any initial training period), it automatically
stops and closes the associated job after this many real-time searches return no documents. In other words,
it stops after frequency times max_empty_searches of real-time operation. If not set, a datafeed with no
end time that sees no data remains started until it is explicitly stopped. By default, it is not set.
An Elasticsearch Query DSL (Domain Specific Language) object that defines a query.
A duration. Units can be nanos, micros, ms (milliseconds), s (seconds), m (minutes), h (hours) and
d (days). Also accepts "0" without a unit and "-1" to indicate an unspecified value.
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.
PUT _ml/datafeeds/datafeed-test-job?pretty
{
"indices": [
"kibana_sample_data_logs"
],
"query": {
"bool": {
"must": [
{
"match_all": {}
}
]
}
},
"job_id": "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",
)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",
});response = client.ml.put_datafeed(
datafeed_id: "datafeed-test-job",
pretty: "true",
body: {
"indices": [
"kibana_sample_data_logs"
],
"query": {
"bool": {
"must": [
{
"match_all": {}
}
]
}
},
"job_id": "test-job"
}
)$resp = $client->ml()->putDatafeed([
"datafeed_id" => "datafeed-test-job",
"pretty" => "true",
"body" => [
"indices" => array(
"kibana_sample_data_logs",
),
"query" => [
"bool" => [
"must" => array(
[
"match_all" => new ArrayObject([]),
],
),
],
],
"job_id" => "test-job",
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"indices":["kibana_sample_data_logs"],"query":{"bool":{"must":[{"match_all":{}}]}},"job_id":"test-job"}' "$ELASTICSEARCH_URL/_ml/datafeeds/datafeed-test-job?pretty"{
"indices": [
"kibana_sample_data_logs"
],
"query": {
"bool": {
"must": [
{
"match_all": {}
}
]
}
},
"job_id": "test-job"
}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.
DELETE _ml/datafeeds/datafeed-total-requests
resp = client.ml.delete_datafeed(
datafeed_id="datafeed-total-requests",
)const response = await client.ml.deleteDatafeed({
datafeed_id: "datafeed-total-requests",
});response = client.ml.delete_datafeed(
datafeed_id: "datafeed-total-requests"
)$resp = $client->ml()->deleteDatafeed([
"datafeed_id" => "datafeed-total-requests",
]);curl -X DELETE -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/datafeeds/datafeed-total-requests"client.ml().deleteDatafeed(d -> d
.datafeedId("datafeed-total-requests")
);
{
"acknowledged": true
}GET _ml/filters/safe_domains
resp = client.ml.get_filters(
filter_id="safe_domains",
)const response = await client.ml.getFilters({
filter_id: "safe_domains",
});response = client.ml.get_filters(
filter_id: "safe_domains"
)$resp = $client->ml()->getFilters([
"filter_id" => "safe_domains",
]);curl -X GET -H "Authorization: ApiKey $ELASTIC_API_KEY" "$ELASTICSEARCH_URL/_ml/filters/safe_domains"client.ml().getFilters(g -> g
.filterId("safe_domains")
);
PUT _ml/filters/safe_domains
{
"description": "A list of safe domains",
"items": ["*.google.com", "wikipedia.org"]
}resp = client.ml.put_filter(
filter_id="safe_domains",
description="A list of safe domains",
items=[
"*.google.com",
"wikipedia.org"
],
)const response = await client.ml.putFilter({
filter_id: "safe_domains",
description: "A list of safe domains",
items: ["*.google.com", "wikipedia.org"],
});response = client.ml.put_filter(
filter_id: "safe_domains",
body: {
"description": "A list of safe domains",
"items": [
"*.google.com",
"wikipedia.org"
]
}
)$resp = $client->ml()->putFilter([
"filter_id" => "safe_domains",
"body" => [
"description" => "A list of safe domains",
"items" => array(
"*.google.com",
"wikipedia.org",
),
],
]);curl -X PUT -H "Authorization: ApiKey $ELASTIC_API_KEY" -H "Content-Type: application/json" -d '{"description":"A list of safe domains","items":["*.google.com","wikipedia.org"]}' "$ELASTICSEARCH_URL/_ml/filters/safe_domains"client.ml().putFilter(p -> p
.description("A list of safe domains")
.filterId("safe_domains")
.items(List.of("*.google.com","wikipedia.org"))
);
{
"description": "A list of safe domains",
"items": ["*.google.com", "wikipedia.org"]
}