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* Add analyzer documentation Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Add index and search analyzer pages Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Doc review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * More doc review comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Apply suggestions from code review Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Implemented editorial comments Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Update index-analyzers.md Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> --------- Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Co-authored-by: Melissa Vagi <vagimeli@amazon.com> Co-authored-by: Nathan Bower <nbower@amazon.com>
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layout | title | parent | grand_parent | nav_order | redirect_from | |
---|---|---|---|---|---|---|
default | Adjacency matrix | Bucket aggregations | Aggregations | 10 |
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Adjacency matrix aggregations
The adjacency_matrix
aggregation lets you define filter expressions and returns a matrix of the intersecting filters where each non-empty cell in the matrix represents a bucket. You can find how many documents fall within any combination of filters.
Use the adjacency_matrix
aggregation to discover how concepts are related by visualizing the data as graphs.
For example, in the sample eCommerce dataset, to analyze how the different manufacturing companies are related:
GET opensearch_dashboards_sample_data_ecommerce/_search
{
"size": 0,
"aggs": {
"interactions": {
"adjacency_matrix": {
"filters": {
"grpA": {
"match": {
"manufacturer.keyword": "Low Tide Media"
}
},
"grpB": {
"match": {
"manufacturer.keyword": "Elitelligence"
}
},
"grpC": {
"match": {
"manufacturer.keyword": "Oceanavigations"
}
}
}
}
}
}
}
{% include copy-curl.html %}
Example response
{
...
"aggregations" : {
"interactions" : {
"buckets" : [
{
"key" : "grpA",
"doc_count" : 1553
},
{
"key" : "grpA&grpB",
"doc_count" : 590
},
{
"key" : "grpA&grpC",
"doc_count" : 329
},
{
"key" : "grpB",
"doc_count" : 1370
},
{
"key" : "grpB&grpC",
"doc_count" : 299
},
{
"key" : "grpC",
"doc_count" : 1218
}
]
}
}
}
Let’s take a closer look at the result:
{
"key" : "grpA&grpB",
"doc_count" : 590
}
grpA
: Products manufactured by Low Tide Media.grpB
: Products manufactured by Elitelligence.590
: Number of products that are manufactured by both.
You can use OpenSearch Dashboards to represent this data with a network graph.