OpenSearch/docs/reference/search/count.asciidoc

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[[search-count]]
=== Count API
Gets the number of matches for a search query.
[source,console]
--------------------------------------------------
GET /twitter/_count?q=user:kimchy
--------------------------------------------------
// TEST[setup:twitter]
NOTE: The query being sent in the body must be nested in a `query` key, same as
the <<search-search,search api>> works.
[[search-count-api-request]]
==== {api-request-title}
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`GET /<index>/_count`
[[search-count-api-desc]]
==== {api-description-title}
The count API allows you to execute a query and get the number of matches for
that query. It can be executed across one or more indices. The query can either
be provided using a simple query string as a parameter, or using the
<<query-dsl,Query DSL>> defined within the request body.
The count API can be applied to <<search-multi-index,multiple indices>>.
The operation is broadcast across all shards. For each shard id group, a replica
is chosen and executed against it. This means that replicas increase the
scalability of count.
[[search-count-api-path-params]]
==== {api-path-parms-title}
include::{docdir}/rest-api/common-parms.asciidoc[tag=index]
[[search-count-api-query-params]]
==== {api-query-parms-title}
include::{docdir}/rest-api/common-parms.asciidoc[tag=allow-no-indices]
+
Defaults to `true`.
include::{docdir}/rest-api/common-parms.asciidoc[tag=analyzer]
include::{docdir}/rest-api/common-parms.asciidoc[tag=analyze_wildcard]
include::{docdir}/rest-api/common-parms.asciidoc[tag=default_operator]
include::{docdir}/rest-api/common-parms.asciidoc[tag=df]
include::{docdir}/rest-api/common-parms.asciidoc[tag=expand-wildcards]
+
Defaults to `open`.
include::{docdir}/rest-api/common-parms.asciidoc[tag=ignore_throttled]
include::{docdir}/rest-api/common-parms.asciidoc[tag=index-ignore-unavailable]
include::{docdir}/rest-api/common-parms.asciidoc[tag=lenient]
`min_score`::
(Optional, float)
Sets the minimum `_score` value that documents must have to be included in the
result.
include::{docdir}/rest-api/common-parms.asciidoc[tag=preference]
include::{docdir}/rest-api/common-parms.asciidoc[tag=search-q]
include::{docdir}/rest-api/common-parms.asciidoc[tag=routing]
include::{docdir}/rest-api/common-parms.asciidoc[tag=terminate_after]
[[search-count-request-body]]
==== {api-request-body-title}
include::{docdir}/rest-api/common-parms.asciidoc[tag=query]
[[search-count-api-example]]
==== {api-examples-title}
[source,console]
--------------------------------------------------
PUT /twitter/_doc/1?refresh
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{
"user": "kimchy"
}
GET /twitter/_count?q=user:kimchy
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GET /twitter/_count
{
"query" : {
"term" : { "user" : "kimchy" }
}
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}
--------------------------------------------------
Both examples above do the same: count the number of tweets from the `twitter`
index for a certain user. The API returns the following response:
[source,console-result]
--------------------------------------------------
{
"count" : 1,
"_shards" : {
"total" : 1,
"successful" : 1,
Add a shard filter search phase to pre-filter shards based on query rewriting (#25658) Today if we search across a large amount of shards we hit every shard. Yet, it's quite common to search across an index pattern for time based indices but filtering will exclude all results outside a certain time range ie. `now-3d`. While the search can potentially hit hundreds of shards the majority of the shards might yield 0 results since there is not document that is within this date range. Kibana for instance does this regularly but used `_field_stats` to optimize the indexes they need to query. Now with the deprecation of `_field_stats` and it's upcoming removal a single dashboard in kibana can potentially turn into searches hitting hundreds or thousands of shards and that can easily cause search rejections even though the most of the requests are very likely super cheap and only need a query rewriting to early terminate with 0 results. This change adds a pre-filter phase for searches that can, if the number of shards are higher than a the `pre_filter_shard_size` threshold (defaults to 128 shards), fan out to the shards and check if the query can potentially match any documents at all. While false positives are possible, a negative response means that no matches are possible. These requests are not subject to rejection and can greatly reduce the number of shards a request needs to hit. The approach here is preferable to the kibana approach with field stats since it correctly handles aliases and uses the correct threadpools to execute these requests. Further it's completely transparent to the user and improves scalability of elasticsearch in general on large clusters.
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"skipped" : 0,
"failed" : 0
}
}
--------------------------------------------------
The query is optional, and when not provided, it will use `match_all` to
count all the docs.