The API accepts 3 different authentication methods:
Elasticsearch APIs support 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}"
To get API keys, use the /_security/api_key APIs.
Basic auth tokens are constructed with the Basic keyword, followed by a space, followed by a base64-encoded string of your username:password
(separated by a : colon).
Example: send a Authorization: Basic aGVsbG86aGVsbG8=
HTTP header with your requests to authenticate with the API.
Elasticsearch APIs support the use of bearer tokens in the Authorization HTTP header to authenticate with the API.
For examples, refer to Token-based authentication services
NOTE: This feature is designed for indirect use by Elasticsearch Service, Elastic Cloud Enterprise, and Elastic Cloud on Kubernetes. Direct use is not supported.
the name of the autoscaling policy
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.
curl \
--request GET 'http://api.example.com/_autoscaling/policy/{name}' \
--header "Authorization: $API_KEY"{
"roles": <roles>,
"deciders": <deciders>
}The associated data stream is also deleted.
The name of the analytics collection to be deleted
curl \
--request DELETE 'http://api.example.com/_application/analytics/{name}' \
--header "Authorization: $API_KEY"curl \
--request GET 'http://api.example.com/_application/analytics' \
--header "Authorization: $API_KEY"{
"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"
}
}
}The name of the behavioral analytics collection.
The analytics event type.
Values are page_view, search, or search_click.
Whether the response type has to include more details
curl \
--request POST 'http://api.example.com/_application/analytics/{collection_name}/event/{event_type}' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '"{\n \"session\": {\n \"id\": \"1797ca95-91c9-4e2e-b1bd-9c38e6f386a9\"\n },\n \"user\": {\n \"id\": \"5f26f01a-bbee-4202-9298-81261067abbd\"\n },\n \"search\":{\n \"query\": \"search term\",\n \"results\": {\n \"items\": [\n {\n \"document\": {\n \"id\": \"123\",\n \"index\": \"products\"\n }\n }\n ],\n \"total_results\": 10\n },\n \"sort\": {\n \"name\": \"relevance\"\n },\n \"search_application\": \"website\"\n },\n \"document\":{\n \"id\": \"123\",\n \"index\": \"products\"\n }\n}"'{
"session": {
"id": "1797ca95-91c9-4e2e-b1bd-9c38e6f386a9"
},
"user": {
"id": "5f26f01a-bbee-4202-9298-81261067abbd"
},
"search":{
"query": "search term",
"results": {
"items": [
{
"document": {
"id": "123",
"index": "products"
}
}
],
"total_results": 10
},
"sort": {
"name": "relevance"
},
"search_application": "website"
},
"document":{
"id": "123",
"index": "products"
}
}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.
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.
The 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.
curl \
--request GET 'http://api.example.com/_cat/component_templates/{name}' \
--header "Authorization: $API_KEY"[
{
"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]"
}
]Get the amount of heap memory currently used by the field data cache on every data node in the cluster.
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 nodes stats API.
The unit used to display byte values.
Values are b, kb, mb, gb, tb, or pb.
Comma-separated list of fields used to limit returned information.
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.
curl \
--request GET 'http://api.example.com/_cat/fielddata' \
--header "Authorization: $API_KEY"[
{
"id": "Nqk-6inXQq-OxUfOUI8jNQ",
"host": "127.0.0.1",
"ip": "127.0.0.1",
"node": "Nqk-6in",
"field": "body",
"size": "544b"
}
][
{
"id": "Nqk-6inXQq-OxUfOUI8jNQ",
"host": "1127.0.0.1",
"ip": "127.0.0.1",
"node": "Nqk-6in",
"field": "body",
"size": "544b"
},
{
"id": "Nqk-6inXQq-OxUfOUI8jNQ",
"host": "127.0.0.1",
"ip": "127.0.0.1",
"node": "Nqk-6in",
"field": "soul",
"size": "480b"
}
]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 cluster health API.
This API is often used to check malfunctioning clusters.
To help you track cluster health alongside log files and alerting systems, the API returns timestamps in two formats:
HH:MM:SS, which is human-readable but includes no date information;
Unix epoch time, which is machine-sortable and includes date information.
The latter format is useful for cluster recoveries that take multiple days.
You can use the cat health API to verify cluster health across multiple nodes.
You also can use the API to track the recovery of a large cluster over a longer period of time.
The unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
If true, returns HH:MM:SS and Unix epoch timestamps.
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.
curl \
--request GET 'http://api.example.com/_cat/health' \
--header "Authorization: $API_KEY"[
{
"epoch": "1475871424",
"timestamp": "16:17:04",
"cluster": "elasticsearch",
"status": "green",
"node.total": "1",
"node.data": "1",
"shards": "1",
"pri": "1",
"relo": "0",
"init": "0",
"unassign": "0",
"unassign.pri": "0",
"pending_tasks": "0",
"max_task_wait_time": "-",
"active_shards_percent": "100.0%"
}
]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.
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.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.
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.
curl \
--request GET 'http://api.example.com/_cat/indices' \
--header "Authorization: $API_KEY"[
{
"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"
}
]Get information about the master node, including the ID, bound IP address, and name.
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 nodes info API.
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.
Period to wait for a connection to the master node.
curl \
--request GET 'http://api.example.com/_cat/master' \
--header "Authorization: $API_KEY"[
{
"id": "YzWoH_2BT-6UjVGDyPdqYg",
"host": "127.0.0.1",
"ip": "127.0.0.1",
"node": "YzWoH_2"
}
]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.
Whether 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.
curl \
--request GET 'http://api.example.com/_cat/ml/data_frame/analytics' \
--header "Authorization: $API_KEY"[
{
"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"
}
]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.
Identifier for the anomaly detection job.
Specifies 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.
curl \
--request GET 'http://api.example.com/_cat/ml/anomaly_detectors/{job_id}' \
--header "Authorization: $API_KEY"[
{
"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"
}
]Get information about the nodes in a cluster. 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 nodes info API.
The unit used to display byte values.
Values are b, kb, mb, gb, tb, or pb.
If true, return the full node ID. If false, return the shortened node ID.
If true, the response includes information from segments that are not loaded into memory.
A comma-separated list of columns names to display. It supports simple wildcards.
Supported values include:
build (or b): The Elasticsearch build hash. For example: 5c03844.completion.size (or cs, completionSize): The size of completion. For example: 0b.cpu: The percentage of recent system CPU used.disk.avail (or d, disk, diskAvail): The available disk space. For example: 198.4gb.disk.total (or dt, diskTotal): The total disk space. For example: 458.3gb.disk.used (or du, diskUsed): The used disk space. For example: 259.8gb.disk.used_percent (or dup, diskUsedPercent): The percentage of disk space used.fielddata.evictions (or fe, fielddataEvictions): The number of fielddata cache evictions.fielddata.memory_size (or fm, fielddataMemory): The fielddata cache memory used. For example: 0b.file_desc.current (or fdc, fileDescriptorCurrent): The number of file descriptors used.file_desc.max (or fdm, fileDescriptorMax): The maximum number of file descriptors.file_desc.percent (or fdp, fileDescriptorPercent): The percentage of file descriptors used.flush.total (or ft, flushTotal): The number of flushes.flush.total_time (or ftt, flushTotalTime): The amount of time spent in flush.get.current (or gc, getCurrent): The number of current get operations.get.exists_time (or geti, getExistsTime): The time spent in successful get operations. For example: 14ms.get.exists_total (or geto, getExistsTotal): The number of successful get operations.get.missing_time (or gmti, getMissingTime): The time spent in failed get operations. For example: 0s.get.missing_total (or gmto, getMissingTotal): The number of failed get operations.get.time (or gti, getTime): The amount of time spent in get operations. For example: 14ms.get.total (or gto, getTotal): The number of get operations.heap.current (or hc, heapCurrent): The used heap size. For example: 311.2mb.heap.max (or hm, heapMax): The total heap size. For example: 4gb.heap.percent (or hp, heapPercent): The used percentage of total allocated Elasticsearch JVM heap.
This value reflects only the Elasticsearch process running within the operating system and is the most direct indicator of its JVM, heap, or memory resource performance.http_address (or http): The bound HTTP address.id (or nodeId): The identifier for the node.indexing.delete_current (or idc, indexingDeleteCurrent): The number of current deletion operations.indexing.delete_time (or idti, indexingDeleteTime): The time spent in deletion operations. For example: 2ms.indexing.delete_total (or idto, indexingDeleteTotal): The number of deletion operations.indexing.index_current (or iic, indexingIndexCurrent): The number of current indexing operations.indexing.index_failed (or iif, indexingIndexFailed): The number of failed indexing operations.indexing.index_failed_due_to_version_conflict (or iifvc, indexingIndexFailedDueToVersionConflict): The number of indexing operations that failed due to version conflict.indexing.index_time (or iiti, indexingIndexTime): The time spent in indexing operations. For example: 134ms.indexing.index_total (or iito, indexingIndexTotal): The number of indexing operations.ip (or i): The IP address.jdk (or j): The Java version. For example: 1.8.0.load_1m (or l): The most recent load average. For example: 0.22.load_5m (or l): The load average for the last five minutes. For example: 0.78.load_15m (or l): The load average for the last fifteen minutes. For example: 1.24.mappings.total_count (or mtc, mappingsTotalCount): The number of mappings, including runtime and object fields.mappings.total_estimated_overhead_in_bytes (or mteo, mappingsTotalEstimatedOverheadInBytes): The estimated heap overhead, in bytes, of mappings on this node, which allows for 1KiB of heap for every mapped field.master (or m): Indicates whether the node is the elected master node.
Returned values include * (elected master) and - (not elected master).merges.current (or mc, mergesCurrent): The number of current merge operations.merges.current_docs (or mcd, mergesCurrentDocs): The number of current merging documents.merges.current_size (or mcs, mergesCurrentSize): The size of current merges. For example: 0b.merges.total (or mt, mergesTotal): The number of completed merge operations.merges.total_docs (or mtd, mergesTotalDocs): The number of merged documents.merges.total_size (or mts, mergesTotalSize): The total size of merges. For example: 0b.merges.total_time (or mtt, mergesTotalTime): The time spent merging documents. For example: 0s.name (or n): The node name.node.role (or r, role, nodeRole): The roles of the node.
Returned values include c (cold node), d (data node), f (frozen node), h (hot node), i (ingest node), l (machine learning node), m (master-eligible node), r (remote cluster client node), s (content node), t (transform node), v (voting-only node), w (warm node), and - (coordinating node only).
For example, dim indicates a master-eligible data and ingest node.pid (or p): The process identifier.port (or po): The bound transport port number.query_cache.memory_size (or qcm, queryCacheMemory): The used query cache memory. For example: 0b.query_cache.evictions (or qce, queryCacheEvictions): The number of query cache evictions.query_cache.hit_count (or qchc, queryCacheHitCount): The query cache hit count.query_cache.miss_count (or qcmc, queryCacheMissCount): The query cache miss count.ram.current (or rc, ramCurrent): The used total memory. For example: 513.4mb.ram.max (or rm, ramMax): The total memory. For example: 2.9gb.ram.percent (or rp, ramPercent): The used percentage of the total operating system memory.
This reflects all processes running on the operating system instead of only Elasticsearch and is not guaranteed to correlate to its performance.refresh.total (or rto, refreshTotal): The number of refresh operations.refresh.time (or rti, refreshTime): The time spent in refresh operations. For example: 91ms.request_cache.memory_size (or rcm, requestCacheMemory): The used request cache memory. For example: 0b.request_cache.evictions (or rce, requestCacheEvictions): The number of request cache evictions.request_cache.hit_count (or rchc, requestCacheHitCount): The request cache hit count.request_cache.miss_count (or rcmc, requestCacheMissCount): The request cache miss count.script.compilations (or scrcc, scriptCompilations): The number of total script compilations.script.cache_evictions (or scrce, scriptCacheEvictions): The number of total compiled scripts evicted from cache.search.fetch_current (or sfc, searchFetchCurrent): The number of current fetch phase operations.search.fetch_time (or sfti, searchFetchTime): The time spent in fetch phase. For example: 37ms.search.fetch_total (or sfto, searchFetchTotal): The number of fetch operations.search.open_contexts (or so, searchOpenContexts): The number of open search contexts.search.query_current (or sqc, searchQueryCurrent): The number of current query phase operations.search.query_time (or sqti, searchQueryTime): The time spent in query phase. For example: 43ms.search.query_total (or sqto, searchQueryTotal): The number of query operations.search.scroll_current (or scc, searchScrollCurrent): The number of open scroll contexts.search.scroll_time (or scti, searchScrollTime): The amount of time scroll contexts were held open. For example: 2m.search.scroll_total (or scto, searchScrollTotal): The number of completed scroll contexts.segments.count (or sc, segmentsCount): The number of segments.segments.fixed_bitset_memory (or sfbm, fixedBitsetMemory): The memory used by fixed bit sets for nested object field types and type filters for types referred in join fields.
For example: 1.0kb.segments.index_writer_memory (or siwm, segmentsIndexWriterMemory): The memory used by the index writer. For example: 18mb.segments.memory (or sm, segmentsMemory): The memory used by segments. For example: 1.4kb.segments.version_map_memory (or svmm, segmentsVersionMapMemory): The memory used by the version map. For example: 1.0kb.shard_stats.total_count (or sstc, shards, shardStatsTotalCount): The number of shards assigned.suggest.current (or suc, suggestCurrent): The number of current suggest operations.suggest.time (or suti, suggestTime): The time spent in suggest operations.suggest.total (or suto, suggestTotal): The number of suggest operations.uptime (or u): The amount of node uptime. For example: 17.3m.version (or v): The Elasticsearch version. For example: 9.0.0.A comma-separated list of column names or aliases that determines the sort order.
Sorting defaults to ascending and can be changed by setting :asc
or :desc as a suffix to the column name.
The period to wait for a connection to the master node.
The unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
curl \
--request GET 'http://api.example.com/_cat/nodes' \
--header "Authorization: $API_KEY"[
{
"ip": "127.0.0.1",
"heap.percent": "65",
"ram.percent": "99",
"cpu": "42",
"load_1m": "3.07",
"load_5m": null,
"load_15m": null,
"node.role": "cdfhilmrstw",
"master": "*",
"name": "mJw06l1"
}
][
{
"id": "veJR",
"ip": "127.0.0.1",
"port": "59938",
"v": "9.0.0",
"m": "*"
}
]Get a list of snapshot repositories for a cluster. 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 snapshot repository API.
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.
Period to wait for a connection to the master node.
curl \
--request GET 'http://api.example.com/_cat/repositories' \
--header "Authorization: $API_KEY"[
{
"id": "repo1",
"type": "fs"
},
{
"id": "repo2",
"type": "s3"
}
]Get information about the shards in a cluster. For data streams, the API returns information about the backing indices. IMPORTANT: cat APIs are only intended for human consumption using the command line or Kibana console. They are not intended for use by applications.
The unit used to display byte values.
Values are b, kb, mb, gb, tb, or pb.
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.
Period to wait for a connection to the master node.
Unit used to display time values.
Values are nanos, micros, ms, s, m, h, or d.
curl \
--request GET 'http://api.example.com/_cat/shards' \
--header "Authorization: $API_KEY"[
{
"index": "my-index-000001",
"shard": "0",
"prirep": "p",
"state": "STARTED",
"docs": "3014",
"store": "31.1mb",
"dataset": "249b",
"ip": "192.168.56.10",
"node": "H5dfFeA"
}
][
{
"index": "my-index-000001",
"shard": "0",
"prirep": "p",
"state": "STARTED",
"docs": "3014",
"store": "31.1mb",
"dataset": "249b",
"ip": "192.168.56.10",
"node": "H5dfFeA"
}
][
{
"index": "my-index-000001",
"shard": "0",
"prirep": "p",
"state": "RELOCATING",
"docs": "3014",
"store": "31.1mb",
"dataset": "249b",
"ip": "192.168.56.10",
"node": "H5dfFeA -> -> 192.168.56.30 bGG90GE"
}
][
{
"index": "my-index-000001",
"shard": "0",
"prirep": "p",
"state": "STARTED",
"docs": "3014",
"store": "31.1mb",
"dataset": "249b",
"ip": "192.168.56.10",
"node": "H5dfFeA"
},
{
"index": "my-index-000001",
"shard": "0",
"prirep": "r",
"state": "INITIALIZING",
"docs": "0",
"store": "14.3mb",
"dataset": "249b",
"ip": "192.168.56.30",
"node": "bGG90GE"
}
][
{
"index": "my-index-000001",
"shard": "0",
"prirep": "p",
"state": "STARTED",
"unassigned.reason": "3014 31.1mb 192.168.56.10 H5dfFeA"
},
{
"index": "my-index-000001",
"shard": "0",
"prirep": "r",
"state": "STARTED",
"unassigned.reason": "3014 31.1mb 192.168.56.30 bGG90GE"
},
{
"index": "my-index-000001",
"shard": "0",
"prirep": "r",
"state": "STARTED",
"unassigned.reason": "3014 31.1mb 192.168.56.20 I8hydUG"
},
{
"index": "my-index-000001",
"shard": "0",
"prirep": "r",
"state": "UNASSIGNED",
"unassigned.reason": "ALLOCATION_FAILED"
}
]Get information about the index templates in a cluster. You can use index templates to apply index settings and field mappings to new indices at creation. 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 index template API.
The name of the template to return. Accepts wildcard expressions. If omitted, all 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.
Period to wait for a connection to the master node.
curl \
--request GET 'http://api.example.com/_cat/templates/{name}' \
--header "Authorization: $API_KEY"[
{
"name": "my-template-0",
"index_patterns": "[te*]",
"order": "500",
"version": null,
"composed_of": "[]"
},
{
"name": "my-template-1",
"index_patterns": "[tea*]",
"order": "501",
"version": null,
"composed_of": "[]"
},
{
"name": "my-template-2",
"index_patterns": "[teak*]",
"order": "502",
"version": "7",
"composed_of": "[]"
}
]Remove master-eligible nodes from the voting configuration exclusion list.
Period to wait for a connection to the master node.
Specifies whether to wait for all excluded nodes to be removed from the cluster before clearing the voting configuration exclusions list. Defaults to true, meaning that all excluded nodes must be removed from the cluster before this API takes any action. If set to false then the voting configuration exclusions list is cleared even if some excluded nodes are still in the cluster.
curl \
--request DELETE 'http://api.example.com/_cluster/voting_config_exclusions' \
--header "Authorization: $API_KEY"You can also use the API to get the health status of only specified data streams and indices. For data streams, the API retrieves the health status of the stream’s backing indices.
The cluster health status is: green, yellow or red. On the shard level, a red status indicates that the specific shard is not allocated in the cluster. Yellow means that the primary shard is allocated but replicas are not. Green means that all shards are allocated. The index level status is controlled by the worst shard status.
One of the main benefits of the API is the ability to wait until the cluster reaches a certain high watermark health level. The cluster status is controlled by the worst index status.
Whether to expand wildcard expression to concrete indices that are open, closed or both.
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.Can be one of cluster, indices or shards. Controls the details level of the health information returned.
Values are cluster, indices, or shards.
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.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
A number controlling to how many active shards to wait for, all to wait for all shards in the cluster to be active, or 0 to not wait.
Can be one of immediate, urgent, high, normal, low, languid. Wait until all currently queued events with the given priority are processed.
Values are immediate, urgent, high, normal, low, or languid.
The request waits until the specified number N of nodes is available. It also accepts >=N, <=N, >N and <N. Alternatively, it is possible to use ge(N), le(N), gt(N) and lt(N) notation.
A boolean value which controls whether to wait (until the timeout provided) for the cluster to have no shard initializations. Defaults to false, which means it will not wait for initializing shards.
A boolean value which controls whether to wait (until the timeout provided) for the cluster to have no shard relocations. Defaults to false, which means it will not wait for relocating shards.
One of green, yellow or red. Will wait (until the timeout provided) until the status of the cluster changes to the one provided or better, i.e. green > yellow > red. By default, will not wait for any 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.
curl \
--request GET 'http://api.example.com/_cluster/health' \
--header "Authorization: $API_KEY"{
"cluster_name" : "testcluster",
"status" : "yellow",
"timed_out" : false,
"number_of_nodes" : 1,
"number_of_data_nodes" : 1,
"active_primary_shards" : 1,
"active_shards" : 1,
"relocating_shards" : 0,
"initializing_shards" : 0,
"unassigned_shards" : 1,
"delayed_unassigned_shards": 0,
"number_of_pending_tasks" : 0,
"number_of_in_flight_fetch": 0,
"task_max_waiting_in_queue_millis": 0,
"active_shards_percent_as_number": 50.0
}Get information about cluster-level changes (such as create index, update mapping, allocate or fail shard) that have not yet taken effect.
NOTE: This API returns a list of any pending updates to the cluster state. These are distinct from the tasks reported by the task management API which include periodic tasks and tasks initiated by the user, such as node stats, search queries, or create index requests. However, if a user-initiated task such as a create index command causes a cluster state update, the activity of this task might be reported by both task api and pending cluster tasks API.
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.
curl \
--request GET 'http://api.example.com/_cluster/pending_tasks' \
--header "Authorization: $API_KEY"Get repositories metering information for a cluster. This API exposes monotonically non-decreasing counters and it is expected that clients would durably store the information needed to compute aggregations over a period of time. Additionally, the information exposed by this API is volatile, meaning that it will not be present after node restarts.
Comma-separated list of node IDs or names used to limit returned information.
curl \
--request GET 'http://api.example.com/_nodes/{node_id}/_repositories_metering' \
--header "Authorization: $API_KEY"By default, the API returns all attributes and core settings for cluster nodes.
Comma-separated list of node IDs or names used to limit returned information.
If true, returns settings in flat format.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
curl \
--request GET 'http://api.example.com/_nodes/{node_id}' \
--header "Authorization: $API_KEY"{
"_nodes": {},
"cluster_name": "elasticsearch",
"nodes": {
"USpTGYaBSIKbgSUJR2Z9lg": {
"name": "node-0",
"transport_address": "192.168.17:9300",
"host": "node-0.elastic.co",
"ip": "192.168.17",
"version": "{version}",
"transport_version": 100000298,
"index_version": 100000074,
"component_versions": {
"ml_config_version": 100000162,
"transform_config_version": 100000096
},
"build_flavor": "default",
"build_type": "{build_type}",
"build_hash": "587409e",
"roles": [
"master",
"data",
"ingest"
],
"attributes": {},
"plugins": [
{
"name": "analysis-icu",
"version": "{version}",
"description": "The ICU Analysis plugin integrates Lucene ICU
module into elasticsearch, adding ICU relates analysis components.",
"classname":
"org.elasticsearch.plugin.analysis.icu.AnalysisICUPlugin",
"has_native_controller": false
}
],
"modules": [
{
"name": "lang-painless",
"version": "{version}",
"description": "An easy, safe and fast scripting language for
Elasticsearch",
"classname": "org.elasticsearch.painless.PainlessPlugin",
"has_native_controller": false
}
]
}
}
}Get statistics for nodes in a cluster. By default, all stats are returned. You can limit the returned information by using metrics.
Comma-separated list or wildcard expressions of fields to include in fielddata and suggest statistics.
Comma-separated list or wildcard expressions of fields to include in fielddata statistics.
Comma-separated list or wildcard expressions of fields to include in the statistics.
Comma-separated list of search groups to include in the search statistics.
If true, the call reports the aggregated disk usage of each one of the Lucene index files (only applies if segment stats are requested).
Indicates whether statistics are aggregated at the cluster, index, or shard level.
Values are cluster, indices, or shards.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
A comma-separated list of document types for the indexing index metric.
If true, the response includes information from segments that are not loaded into memory.
curl \
--request GET 'http://api.example.com/_nodes/stats' \
--header "Authorization: $API_KEY"Get statistics for nodes in a cluster. By default, all stats are returned. You can limit the returned information by using metrics.
Limit the information returned to the specified metrics
Comma-separated list or wildcard expressions of fields to include in fielddata and suggest statistics.
Comma-separated list or wildcard expressions of fields to include in fielddata statistics.
Comma-separated list or wildcard expressions of fields to include in the statistics.
Comma-separated list of search groups to include in the search statistics.
If true, the call reports the aggregated disk usage of each one of the Lucene index files (only applies if segment stats are requested).
Indicates whether statistics are aggregated at the cluster, index, or shard level.
Values are cluster, indices, or shards.
Period to wait for a response. If no response is received before the timeout expires, the request fails and returns an error.
A comma-separated list of document types for the indexing index metric.
If true, the response includes information from segments that are not loaded into memory.
curl \
--request GET 'http://api.example.com/_nodes/stats/{metric}' \
--header "Authorization: $API_KEY"Get a report with the health status of an Elasticsearch cluster. The report contains a list of indicators that compose Elasticsearch functionality.
Each indicator has a health status of: green, unknown, yellow or red. The indicator will provide an explanation and metadata describing the reason for its current health status.
The cluster’s status is controlled by the worst indicator status.
In the event that an indicator’s status is non-green, a list of impacts may be present in the indicator result which detail the functionalities that are negatively affected by the health issue. Each impact carries with it a severity level, an area of the system that is affected, and a simple description of the impact on the system.
Some health indicators can determine the root cause of a health problem and prescribe a set of steps that can be performed in order to improve the health of the system. The root cause and remediation steps are encapsulated in a diagnosis. A diagnosis contains a cause detailing a root cause analysis, an action containing a brief description of the steps to take to fix the problem, the list of affected resources (if applicable), and a detailed step-by-step troubleshooting guide to fix the diagnosed problem.
NOTE: The health indicators perform root cause analysis of non-green health statuses. This can be computationally expensive when called frequently. When setting up automated polling of the API for health status, set verbose to false to disable the more expensive analysis logic.
curl \
--request GET 'http://api.example.com/_health_report' \
--header "Authorization: $API_KEY"Get a report with the health status of an Elasticsearch cluster. The report contains a list of indicators that compose Elasticsearch functionality.
Each indicator has a health status of: green, unknown, yellow or red. The indicator will provide an explanation and metadata describing the reason for its current health status.
The cluster’s status is controlled by the worst indicator status.
In the event that an indicator’s status is non-green, a list of impacts may be present in the indicator result which detail the functionalities that are negatively affected by the health issue. Each impact carries with it a severity level, an area of the system that is affected, and a simple description of the impact on the system.
Some health indicators can determine the root cause of a health problem and prescribe a set of steps that can be performed in order to improve the health of the system. The root cause and remediation steps are encapsulated in a diagnosis. A diagnosis contains a cause detailing a root cause analysis, an action containing a brief description of the steps to take to fix the problem, the list of affected resources (if applicable), and a detailed step-by-step troubleshooting guide to fix the diagnosed problem.
NOTE: The health indicators perform root cause analysis of non-green health statuses. This can be computationally expensive when called frequently. When setting up automated polling of the API for health status, set verbose to false to disable the more expensive analysis logic.
A feature of the cluster, as returned by the top-level health report API.
curl \
--request GET 'http://api.example.com/_health_report/{feature}' \
--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.
The unique identifier of the connector to be created or updated. ID is auto-generated if not provided.
curl \
--request PUT 'http://api.example.com/_connector/{connector_id}' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '"{\n \"index_name\": \"search-google-drive\",\n \"name\": \"My Connector\",\n \"service_type\": \"google_drive\"\n}"'{
"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"
}Check in a connector sync job and set the last_seen field to the current time before updating it in the internal index.
To sync data using self-managed connectors, you need to deploy the Elastic connector service on your own infrastructure. This service runs automatically on Elastic Cloud for Elastic managed connectors.
The unique identifier of the connector sync job to be checked in.
curl \
--request PUT 'http://api.example.com/_connector/_sync_job/{connector_sync_job_id}/_check_in' \
--header "Authorization: $API_KEY"This action updates the job status to in_progress and sets the last_seen and started_at timestamps to the current time.
Additionally, it can set the sync_cursor property for the sync job.
This API is not intended for direct connector management by users. It supports the implementation of services that utilize the connector protocol to communicate with Elasticsearch.
To sync data using self-managed connectors, you need to deploy the Elastic connector service on your own infrastructure. This service runs automatically on Elastic Cloud for Elastic managed connectors.
The unique identifier of the connector sync job.
The cursor object from the last incremental sync job.
This should reference the sync_cursor field in the connector state for which the job runs.
The host name of the current system that will run the job.
curl \
--request PUT 'http://api.example.com/_connector/_sync_job/{connector_sync_job_id}/_claim' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '{"sync_cursor":{},"worker_hostname":"string"}'The unique identifier of the connector sync job
curl \
--request GET 'http://api.example.com/_connector/_sync_job/{connector_sync_job_id}' \
--header "Authorization: $API_KEY"Create a connector sync job document in the internal index and initialize its counters and timestamps with default values.
Values are full, incremental, or access_control.
Values are on_demand or scheduled.
curl \
--request POST 'http://api.example.com/_connector/_sync_job' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '"{\n \"id\": \"connector-id\",\n \"job_type\": \"full\",\n \"trigger_method\": \"on_demand\"\n}"'{
"id": "connector-id",
"job_type": "full",
"trigger_method": "on_demand"
}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.
The unique identifier of the connector to be updated
curl \
--request PUT 'http://api.example.com/_connector/{connector_id}/_api_key_id' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '"{\n \"api_key_id\": \"my-api-key-id\",\n \"api_key_secret_id\": \"my-connector-secret-id\"\n}"'{
"api_key_id": "my-api-key-id",
"api_key_secret_id": "my-connector-secret-id"
}{
"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.
The unique identifier of the connector to be updated
curl \
--request PUT 'http://api.example.com/_connector/{connector_id}/_error' \
--header "Authorization: $API_KEY" \
--header "Content-Type: application/json" \
--data '"{\n \"error\": \"Houston, we have a problem!\"\n}"'{
"error": "Houston, we have a problem!"
}{
"result": "updated"
}