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 non-empty cell in the matrix of intersecting filters.

Warning
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:

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/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"] }}
        }
      }
    }
  }
}

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 index-level setting.