OpenSearch/docs/reference/aggregations/bucket/children-aggregation.asciidoc

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[[search-aggregations-bucket-children-aggregation]]
=== Children Aggregation
A special single bucket aggregation that selects child documents that have the specified type, as defined in a <<parent-join,`join` field>>.
This aggregation has a single option:
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* `type` - The child type that should be selected.
For example, let's say we have an index of questions and answers. The answer type has the following `join` field in the mapping:
[source,js]
--------------------------------------------------
PUT child_example
{
"mappings": {
"doc": {
"properties": {
"join": {
"type": "join",
"relations": {
"question": "answer"
}
}
}
}
}
}
--------------------------------------------------
// CONSOLE
The `question` document contain a tag field and the `answer` documents contain an owner field. With the `children`
aggregation the tag buckets can be mapped to the owner buckets in a single request even though the two fields exist in
two different kinds of documents.
An example of a question document:
[source,js]
--------------------------------------------------
PUT child_example/doc/1
{
"join": {
"name": "question"
},
"body": "<p>I have Windows 2003 server and i bought a new Windows 2008 server...",
"title": "Whats the best way to file transfer my site from server to a newer one?",
"tags": [
"windows-server-2003",
"windows-server-2008",
"file-transfer"
]
}
--------------------------------------------------
// CONSOLE
// TEST[continued]
Examples of `answer` documents:
[source,js]
--------------------------------------------------
PUT child_example/doc/2?routing=1
{
"join": {
"name": "answer",
"parent": "1"
},
"owner": {
"location": "Norfolk, United Kingdom",
"display_name": "Sam",
"id": 48
},
"body": "<p>Unfortunately you're pretty much limited to FTP...",
"creation_date": "2009-05-04T13:45:37.030"
}
PUT child_example/doc/3?routing=1&refresh
{
"join": {
"name": "answer",
"parent": "1"
},
"owner": {
"location": "Norfolk, United Kingdom",
"display_name": "Troll",
"id": 49
},
"body": "<p>Use Linux...",
"creation_date": "2009-05-05T13:45:37.030"
}
--------------------------------------------------
// CONSOLE
// TEST[continued]
The following request can be built that connects the two together:
[source,js]
--------------------------------------------------
POST child_example/_search?size=0
{
"aggs": {
"top-tags": {
"terms": {
"field": "tags.keyword",
"size": 10
},
"aggs": {
"to-answers": {
"children": {
"type" : "answer" <1>
},
"aggs": {
"top-names": {
"terms": {
"field": "owner.display_name.keyword",
"size": 10
}
}
}
}
}
}
}
}
--------------------------------------------------
// CONSOLE
// TEST[continued]
<1> The `type` points to type / mapping with the name `answer`.
The above example returns the top question tags and per tag the top answer owners.
Possible response:
[source,js]
--------------------------------------------------
{
"took": 25,
"timed_out": false,
"_shards": {
"total": 5,
"successful": 5,
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
},
"hits": {
"total": 3,
"max_score": 0.0,
"hits": []
},
"aggregations": {
"top-tags": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "file-transfer",
"doc_count": 1, <1>
"to-answers": {
"doc_count": 2, <2>
"top-names": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Sam",
"doc_count": 1
},
{
"key": "Troll",
"doc_count": 1
}
]
}
}
},
{
"key": "windows-server-2003",
"doc_count": 1, <1>
"to-answers": {
"doc_count": 2, <2>
"top-names": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Sam",
"doc_count": 1
},
{
"key": "Troll",
"doc_count": 1
}
]
}
}
},
{
"key": "windows-server-2008",
"doc_count": 1, <1>
"to-answers": {
"doc_count": 2, <2>
"top-names": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": "Sam",
"doc_count": 1
},
{
"key": "Troll",
"doc_count": 1
}
]
}
}
}
]
}
}
}
--------------------------------------------------
// TESTRESPONSE[s/"took": 25/"took": $body.took/]
<1> The number of question documents with the tag `file-transfer`, `windows-server-2003`, etc.
<2> The number of answer documents that are related to question documents with the tag `file-transfer`, `windows-server-2003`, etc.