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