OpenSearch/docs/reference/aggregations/bucket/adjacency-matrix-aggregatio...

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[[search-aggregations-bucket-adjacency-matrix-aggregation]]
=== Adjacency Matrix Aggregation
A bucket aggregation returning a form of https://en.wikipedia.org/wiki/Adjacency_matrix[adjacency matrix].
The request provides a collection of named filter expressions, similar to the `filters` aggregation
request.
Each bucket in the response represents a non-empty cell in the matrix of intersecting filters.
experimental[The `adjacency_matrix` aggregation is a new feature and we may evolve its design as we get feedback on its use. As a result, the API for this feature may change in non-backwards compatible ways]
Given filters named `A`, `B` and `C` the response would return buckets with the following names:
[options="header"]
|=======================
| h|A h|B h|C
h|A |A |A&B |A&C
h|B | |B |B&C
h|C | | |C
|=======================
The intersecting buckets e.g `A&C` are labelled using a combination of the two filter names separated by
the ampersand character. Note that the response does not also include a "C&A" bucket as this would be the
same set of documents as "A&C". The matrix is said to be _symmetric_ so we only return half of it. To do this we sort
the filter name strings and always use the lowest of a pair as the value to the left of the "&" separator.
An alternative `separator` parameter can be passed in the request if clients wish to use a separator string
other than the default of the ampersand.
Example:
[source,js]
--------------------------------------------------
PUT /emails/message/_bulk?refresh
{ "index" : { "_id" : 1 } }
{ "accounts" : ["hillary", "sidney"]}
{ "index" : { "_id" : 2 } }
{ "accounts" : ["hillary", "donald"]}
{ "index" : { "_id" : 3 } }
{ "accounts" : ["vladimir", "donald"]}
GET emails/message/_search
{
"size": 0,
"aggs" : {
"interactions" : {
"adjacency_matrix" : {
"filters" : {
"grpA" : { "terms" : { "accounts" : ["hillary", "sidney"] }},
"grpB" : { "terms" : { "accounts" : ["donald", "mitt"] }},
"grpC" : { "terms" : { "accounts" : ["vladimir", "nigel"] }}
}
}
}
}
}
--------------------------------------------------
// CONSOLE
In the above example, we analyse email messages to see which groups of individuals
have exchanged messages.
We will get counts for each group individually and also a count of messages for pairs
of groups that have recorded interactions.
Response:
[source,js]
--------------------------------------------------
{
"took": 9,
"timed_out": false,
"_shards": ...,
"hits": ...,
"aggregations": {
"interactions": {
"buckets": [
{
"key":"grpA",
"doc_count": 2
},
{
"key":"grpA&grpB",
"doc_count": 1
},
{
"key":"grpB",
"doc_count": 2
},
{
"key":"grpB&grpC",
"doc_count": 1
},
{
"key":"grpC",
"doc_count": 1
}
]
}
}
}
--------------------------------------------------
// TESTRESPONSE[s/"took": 9/"took": $body.took/]
// TESTRESPONSE[s/"_shards": \.\.\./"_shards": $body._shards/]
// TESTRESPONSE[s/"hits": \.\.\./"hits": $body.hits/]
==== Usage
On its own this aggregation can provide all of the data required to create an undirected weighted graph.
However, when used with child aggregations such as a `date_histogram` the results can provide the
additional levels of data required to perform https://en.wikipedia.org/wiki/Dynamic_network_analysis[dynamic network analysis]
where examining interactions _over time_ becomes important.
==== Limitations
For N filters the matrix of buckets produced can be N²/2 and so there is a default maximum
imposed of 100 filters . This setting can be changed using the `index.max_adjacency_matrix_filters` index-level setting.