Adjacency matrix aggregationedit
A bucket aggregation returning a form of adjacency matrix.
The request provides a collection of named filter expressions, similar to the filters
aggregation
request.
Each bucket in the response represents a nonempty cell in the matrix of intersecting filters.
Given filters named A
, B
and C
the response would return buckets with the following names:
A  B  C  

A 
A 
A&B 
A&C 
B 
B 
B&C 

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:
PUT /emails/_bulk?refresh { "index" : { "_id" : 1 } } { "accounts" : ["hillary", "sidney"]} { "index" : { "_id" : 2 } } { "accounts" : ["hillary", "donald"]} { "index" : { "_id" : 3 } } { "accounts" : ["vladimir", "donald"]} GET emails/_search { "size": 0, "aggs" : { "interactions" : { "adjacency_matrix" : { "filters" : { "grpA" : { "terms" : { "accounts" : ["hillary", "sidney"] }}, "grpB" : { "terms" : { "accounts" : ["donald", "mitt"] }}, "grpC" : { "terms" : { "accounts" : ["vladimir", "nigel"] }} } } } } }
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:
{ "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 } ] } } }
Usageedit
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 dynamic network analysis
where examining interactions over time becomes important.
Limitationsedit
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
indexlevel setting
(note this setting is deprecated and will be repaced with indices.query.bool.max_clause_count
in 8.0+).