Refactor metric aggregation section (#4543)
* Refactor metric aggregation section Signed-off-by: Fanit Kolchina <kolchfa@amazon.com> * Update _query-dsl/aggregations/metric/matrix-stats.md Co-authored-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> Signed-off-by: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> * Update _query-dsl/aggregations/metric/extended-stats.md Co-authored-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> 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: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com>
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parent
3567e8b90a
commit
02f40d7efd
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@ -43,6 +43,7 @@ GET opensearch_dashboards_sample_data_ecommerce/_search
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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@ -26,6 +26,7 @@ GET opensearch_dashboards_sample_data_logs/_search
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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@ -31,6 +31,7 @@ GET opensearch_dashboards_sample_data_logs/_search
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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@ -31,6 +31,7 @@ GET opensearch_dashboards_sample_data_logs/_search
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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@ -36,6 +36,7 @@ GET opensearch_dashboards_sample_data_ecommerce/_search
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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@ -113,6 +113,7 @@ GET opensearch_dashboards_sample_data_logs/_search
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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@ -37,6 +37,7 @@ PUT national_parks
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}
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}
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```
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{% include copy-curl.html %}
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Index the following documents into the sample index:
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@ -46,19 +47,26 @@ PUT national_parks/_doc/1
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"name": "Yellowstone National Park",
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"location": "44.42, -110.59"
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}
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```
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{% include copy-curl.html %}
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```json
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PUT national_parks/_doc/2
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{
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"name": "Yosemite National Park",
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"location": "37.87, -119.53"
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}
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```
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{% include copy-curl.html %}
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```json
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PUT national_parks/_doc/3
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{
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"name": "Death Valley National Park",
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"location": "36.53, -116.93"
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}
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```
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{% include copy-curl.html %}
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You can index geopoints in several formats. For a list of all supported formats, see the [geopoint documentation]({{site.url}}{{site.baseurl}}/opensearch/supported-field-types/geo-point#formats).
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{: .note}
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}
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}
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```
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{% include copy-curl.html %}
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You can use either the `GET` or `POST` HTTP method for geohex grid aggregation queries.
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{: .note}
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}
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}
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```
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{% include copy-curl.html %}
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All three documents are bucketed separately because of higher granularity:
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}
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}
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```
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{% include copy-curl.html %}
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The response contains the two documents that are within the `geo_bounding_box` bounds:
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}
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}
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```
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{% include copy-curl.html %}
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The response contains only the two results that are within the specified bounds:
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}
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}
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```
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{% include copy-curl.html %}
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Index the following documents into the sample index:
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"name": "Yellowstone National Park",
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"location": "44.42, -110.59"
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}
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```
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{% include copy-curl.html %}
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```json
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PUT national_parks/_doc/2
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{
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"name": "Yosemite National Park",
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"location": "37.87, -119.53"
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}
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```
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{% include copy-curl.html %}
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```json
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PUT national_parks/_doc/3
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{
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"name": "Death Valley National Park",
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"location": "36.53, -116.93"
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}
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```
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{% include copy-curl.html %}
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You can index geopoints in several formats. For a list of all supported formats, see the [geopoint documentation]({{site.url}}{{site.baseurl}}/opensearch/supported-field-types/geo-point#formats).
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{: .note}
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}
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}
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```
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{% include copy-curl.html %}
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You can use either the `GET` or `POST` HTTP method for geotile grid aggregation queries.
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{: .note}
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}
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}
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```
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{% include copy-curl.html %}
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All three documents are bucketed separately because of higher granularity:
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}
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}
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```
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{% include copy-curl.html %}
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The response contains only the two results that are within the specified bounds:
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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Because the default value for the `min_doc_count` parameter is 1, the `missing` parameter doesn't return any buckets in its response. Set `min_doc_count` parameter to 0 to see the "N/A" bucket in the response:
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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]
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}
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```
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{% include copy-curl.html %}
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OpenSearch merges all sub-properties of the entity relations that looks something like this:
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}
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}
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```
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{% include copy-curl.html %}
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Nested documents allow you to index the same JSON document but will keep your pages in separate Lucene documents, making only searches like `pages=landing` and `load_time=200` return the expected result. Internally, nested objects index each object in the array as a separate hidden document, meaning that each nested object can be queried independently of the others.
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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The response includes the `from` key values and excludes the `to` key values:
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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{% include copy-curl.html %}
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#### Example response
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}
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}
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```
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#### Example response
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@ -1,638 +0,0 @@
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---
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layout: default
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title: Metric aggregations
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parent: Aggregations
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nav_order: 2
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permalink: /aggregations/metric-agg/
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redirect_from:
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- /opensearch/metric-agg/
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---
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# Metric aggregations
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Metric aggregations let you perform simple calculations such as finding the minimum, maximum, and average values of a field.
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## Types of metric aggregations
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Metric aggregations are of two types: single-value metric aggregations and multi-value metric aggregations.
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### Single-value metric aggregations
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Single-value metric aggregations return a single metric. For example, `sum`, `min`, `max`, `avg`, `cardinality`, and `value_count`.
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### Multi-value metric aggregations
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Multi-value metric aggregations return more than one metric. For example, `stats`, `extended_stats`, `matrix_stats`, `percentile`, `percentile_ranks`, `geo_bound`, `top_hits`, and `scripted_metric`.
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## sum, min, max, avg
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The `sum`, `min`, `max`, and `avg` metrics are single-value metric aggregations that return the sum, minimum, maximum, and average values of a field, respectively.
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The following example calculates the total sum of the `taxful_total_price` field:
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```json
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GET opensearch_dashboards_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"sum_taxful_total_price": {
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"sum": {
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"field": "taxful_total_price"
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}
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}
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}
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}
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```
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#### Example response
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```json
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...
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"aggregations" : {
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"sum_taxful_total_price" : {
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"value" : 350884.12890625
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}
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}
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}
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```
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In a similar fashion, you can find the minimum, maximum, and average values of a field.
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## cardinality
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The `cardinality` metric is a single-value metric aggregation that counts the number of unique or distinct values of a field.
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The following example finds the number of unique products in an eCommerce store:
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```json
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GET opensearch_dashboards_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"unique_products": {
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"cardinality": {
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"field": "products.product_id"
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}
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}
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}
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}
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```
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#### Example response
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```json
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...
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"aggregations" : {
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"unique_products" : {
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"value" : 7033
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}
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}
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}
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```
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Cardinality count is approximate.
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If you have tens of thousands of products in your hypothetical store, an accurate cardinality calculation requires loading all the values into a hash set and returning its size. This approach doesn't scale well; it requires huge amounts of memory and can cause high latencies.
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You can control the trade-off between memory and accuracy with the `precision_threshold` setting. This setting defines the threshold below which counts are expected to be close to accurate. Above this value, counts might become a bit less accurate. The default value of `precision_threshold` is 3,000. The maximum supported value is 40,000.
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```json
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GET opensearch_dashboards_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"unique_products": {
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"cardinality": {
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"field": "products.product_id",
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"precision_threshold": 10000
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}
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}
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}
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}
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```
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## value_count
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The `value_count` metric is a single-value metric aggregation that calculates the number of values that an aggregation is based on.
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For example, you can use the `value_count` metric with the `avg` metric to find how many numbers the aggregation uses to calculate an average value.
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```json
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GET opensearch_dashboards_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"number_of_values": {
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"value_count": {
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"field": "taxful_total_price"
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}
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}
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}
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}
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```
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#### Example response
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```json
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...
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"aggregations" : {
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"number_of_values" : {
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"value" : 4675
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}
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}
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}
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```
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## stats, extended_stats, matrix_stats
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The `stats` metric is a multi-value metric aggregation that returns all basic metrics such as `min`, `max`, `sum`, `avg`, and `value_count` in one aggregation query.
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The following example returns the basic stats for the `taxful_total_price` field:
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```json
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GET opensearch_dashboards_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"stats_taxful_total_price": {
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"stats": {
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"field": "taxful_total_price"
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}
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}
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}
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}
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```
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#### Example response
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```json
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...
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"aggregations" : {
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"stats_taxful_total_price" : {
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"count" : 4675,
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"min" : 6.98828125,
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"max" : 2250.0,
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"avg" : 75.05542864304813,
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"sum" : 350884.12890625
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}
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}
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}
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```
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The `extended_stats` aggregation is an extended version of the `stats` aggregation. Apart from including basic stats, `extended_stats` also returns stats such as `sum_of_squares`, `variance`, and `std_deviation`.
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```json
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GET opensearch_dashboards_sample_data_ecommerce/_search
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{
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"size": 0,
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"aggs": {
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"extended_stats_taxful_total_price": {
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"extended_stats": {
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"field": "taxful_total_price"
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}
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}
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}
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}
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```
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#### Example response
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```json
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...
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"aggregations" : {
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"extended_stats_taxful_total_price" : {
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"count" : 4675,
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"min" : 6.98828125,
|
||||
"max" : 2250.0,
|
||||
"avg" : 75.05542864304813,
|
||||
"sum" : 350884.12890625,
|
||||
"sum_of_squares" : 3.9367749294174194E7,
|
||||
"variance" : 2787.59157113862,
|
||||
"variance_population" : 2787.59157113862,
|
||||
"variance_sampling" : 2788.187974983536,
|
||||
"std_deviation" : 52.79764740155209,
|
||||
"std_deviation_population" : 52.79764740155209,
|
||||
"std_deviation_sampling" : 52.80329511482722,
|
||||
"std_deviation_bounds" : {
|
||||
"upper" : 180.6507234461523,
|
||||
"lower" : -30.53986616005605,
|
||||
"upper_population" : 180.6507234461523,
|
||||
"lower_population" : -30.53986616005605,
|
||||
"upper_sampling" : 180.66201887270256,
|
||||
"lower_sampling" : -30.551161586606312
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The `std_deviation_bounds` object provides a visual variance of the data with an interval of plus/minus two standard deviations from the mean.
|
||||
To set the standard deviation to a different value, say 3, set `sigma` to 3:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"extended_stats_taxful_total_price": {
|
||||
"extended_stats": {
|
||||
"field": "taxful_total_price",
|
||||
"sigma": 3
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The `matrix_stats` aggregation generates advanced stats for multiple fields in a matrix form.
|
||||
The following example returns advanced stats in a matrix form for the `taxful_total_price` and `products.base_price` fields:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"matrix_stats_taxful_total_price": {
|
||||
"matrix_stats": {
|
||||
"fields": ["taxful_total_price", "products.base_price"]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"matrix_stats_taxful_total_price" : {
|
||||
"doc_count" : 4675,
|
||||
"fields" : [
|
||||
{
|
||||
"name" : "products.base_price",
|
||||
"count" : 4675,
|
||||
"mean" : 34.994239430147196,
|
||||
"variance" : 360.5035285833703,
|
||||
"skewness" : 5.530161335032702,
|
||||
"kurtosis" : 131.16306324042148,
|
||||
"covariance" : {
|
||||
"products.base_price" : 360.5035285833703,
|
||||
"taxful_total_price" : 846.6489362233166
|
||||
},
|
||||
"correlation" : {
|
||||
"products.base_price" : 1.0,
|
||||
"taxful_total_price" : 0.8444765264325268
|
||||
}
|
||||
},
|
||||
{
|
||||
"name" : "taxful_total_price",
|
||||
"count" : 4675,
|
||||
"mean" : 75.05542864304839,
|
||||
"variance" : 2788.1879749835402,
|
||||
"skewness" : 15.812149139924037,
|
||||
"kurtosis" : 619.1235507385902,
|
||||
"covariance" : {
|
||||
"products.base_price" : 846.6489362233166,
|
||||
"taxful_total_price" : 2788.1879749835402
|
||||
},
|
||||
"correlation" : {
|
||||
"products.base_price" : 0.8444765264325268,
|
||||
"taxful_total_price" : 1.0
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Statistic | Description
|
||||
:--- | :---
|
||||
`count` | The number of samples measured.
|
||||
`mean` | The average value of the field measured from the sample.
|
||||
`variance` | How far the values of the field measured are spread out from its mean value. The larger the variance, the more it's spread from its mean value.
|
||||
`skewness` | An asymmetric measure of the distribution of the field's values around the mean.
|
||||
`kurtosis` | A measure of the tail heaviness of a distribution. As the tail becomes lighter, kurtosis decreases. As the tail becomes heavier, kurtosis increases. To learn about kurtosis, see [Wikipedia](https://en.wikipedia.org/wiki/Kurtosis).
|
||||
`covariance` | A measure of the joint variability between two fields. A positive value means their values move in the same direction and vice versa.
|
||||
`correlation` | A measure of the strength of the relationship between two fields. The valid values are between [-1, 1]. A value of -1 means that the value is negatively correlated and a value of 1 means that it's positively correlated. A value of 0 means that there's no identifiable relationship between them.
|
||||
|
||||
## percentile, percentile_ranks
|
||||
|
||||
Percentile is the percentage of the data that's at or below a certain threshold value.
|
||||
|
||||
The `percentile` metric is a multi-value metric aggregation that lets you find outliers in your data or figure out the distribution of your data.
|
||||
|
||||
Like the `cardinality` metric, the `percentile` metric is also approximate.
|
||||
|
||||
The following example calculates the percentile in relation to the `taxful_total_price` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"percentile_taxful_total_price": {
|
||||
"percentiles": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"percentile_taxful_total_price" : {
|
||||
"values" : {
|
||||
"1.0" : 21.984375,
|
||||
"5.0" : 27.984375,
|
||||
"25.0" : 44.96875,
|
||||
"50.0" : 64.22061688311689,
|
||||
"75.0" : 93.0,
|
||||
"95.0" : 156.0,
|
||||
"99.0" : 222.0
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Percentile rank is the percentile of values at or below a threshold grouped by a specified value. For example, if a value is greater than or equal to 80% of the values, it has a percentile rank of 80.
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"percentile_rank_taxful_total_price": {
|
||||
"percentile_ranks": {
|
||||
"field": "taxful_total_price",
|
||||
"values": [
|
||||
10,
|
||||
15
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"percentile_rank_taxful_total_price" : {
|
||||
"values" : {
|
||||
"10.0" : 0.055096056411283456,
|
||||
"15.0" : 0.0830092961834656
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## geo_bound
|
||||
|
||||
The `geo_bound` metric is a multi-value metric aggregation that calculates the bounding box in terms of latitude and longitude around a `geo_point` field.
|
||||
|
||||
The following example returns the `geo_bound` metrics for the `geoip.location` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"geo": {
|
||||
"geo_bounds": {
|
||||
"field": "geoip.location"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
"aggregations" : {
|
||||
"geo" : {
|
||||
"bounds" : {
|
||||
"top_left" : {
|
||||
"lat" : 52.49999997206032,
|
||||
"lon" : -118.20000001229346
|
||||
},
|
||||
"bottom_right" : {
|
||||
"lat" : 4.599999985657632,
|
||||
"lon" : 55.299999956041574
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## top_hits
|
||||
|
||||
The `top_hits` metric is a multi-value metric aggregation that ranks the matching documents based on a relevance score for the field that's being aggregated.
|
||||
|
||||
You can specify the following options:
|
||||
|
||||
- `from`: The starting position of the hit.
|
||||
- `size`: The maximum size of hits to return. The default value is 3.
|
||||
- `sort`: How the matching hits are sorted. By default, the hits are sorted by the relevance score of the aggregation query.
|
||||
|
||||
The following example returns the top 5 products in your eCommerce data:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"top_hits_products": {
|
||||
"top_hits": {
|
||||
"size": 5
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"top_hits_products" : {
|
||||
"hits" : {
|
||||
"total" : {
|
||||
"value" : 4675,
|
||||
"relation" : "eq"
|
||||
},
|
||||
"max_score" : 1.0,
|
||||
"hits" : [
|
||||
{
|
||||
"_index" : "opensearch_dashboards_sample_data_ecommerce",
|
||||
"_type" : "_doc",
|
||||
"_id" : "glMlwXcBQVLeQPrkHPtI",
|
||||
"_score" : 1.0,
|
||||
"_source" : {
|
||||
"category" : [
|
||||
"Women's Accessories",
|
||||
"Women's Clothing"
|
||||
],
|
||||
"currency" : "EUR",
|
||||
"customer_first_name" : "rania",
|
||||
"customer_full_name" : "rania Evans",
|
||||
"customer_gender" : "FEMALE",
|
||||
"customer_id" : 24,
|
||||
"customer_last_name" : "Evans",
|
||||
"customer_phone" : "",
|
||||
"day_of_week" : "Sunday",
|
||||
"day_of_week_i" : 6,
|
||||
"email" : "rania@evans-family.zzz",
|
||||
"manufacturer" : [
|
||||
"Tigress Enterprises"
|
||||
],
|
||||
"order_date" : "2021-02-28T14:16:48+00:00",
|
||||
"order_id" : 583581,
|
||||
"products" : [
|
||||
{
|
||||
"base_price" : 10.99,
|
||||
"discount_percentage" : 0,
|
||||
"quantity" : 1,
|
||||
"manufacturer" : "Tigress Enterprises",
|
||||
"tax_amount" : 0,
|
||||
"product_id" : 19024,
|
||||
"category" : "Women's Accessories",
|
||||
"sku" : "ZO0082400824",
|
||||
"taxless_price" : 10.99,
|
||||
"unit_discount_amount" : 0,
|
||||
"min_price" : 5.17,
|
||||
"_id" : "sold_product_583581_19024",
|
||||
"discount_amount" : 0,
|
||||
"created_on" : "2016-12-25T14:16:48+00:00",
|
||||
"product_name" : "Snood - white/grey/peach",
|
||||
"price" : 10.99,
|
||||
"taxful_price" : 10.99,
|
||||
"base_unit_price" : 10.99
|
||||
},
|
||||
{
|
||||
"base_price" : 32.99,
|
||||
"discount_percentage" : 0,
|
||||
"quantity" : 1,
|
||||
"manufacturer" : "Tigress Enterprises",
|
||||
"tax_amount" : 0,
|
||||
"product_id" : 19260,
|
||||
"category" : "Women's Clothing",
|
||||
"sku" : "ZO0071900719",
|
||||
"taxless_price" : 32.99,
|
||||
"unit_discount_amount" : 0,
|
||||
"min_price" : 17.15,
|
||||
"_id" : "sold_product_583581_19260",
|
||||
"discount_amount" : 0,
|
||||
"created_on" : "2016-12-25T14:16:48+00:00",
|
||||
"product_name" : "Cardigan - grey",
|
||||
"price" : 32.99,
|
||||
"taxful_price" : 32.99,
|
||||
"base_unit_price" : 32.99
|
||||
}
|
||||
],
|
||||
"sku" : [
|
||||
"ZO0082400824",
|
||||
"ZO0071900719"
|
||||
],
|
||||
"taxful_total_price" : 43.98,
|
||||
"taxless_total_price" : 43.98,
|
||||
"total_quantity" : 2,
|
||||
"total_unique_products" : 2,
|
||||
"type" : "order",
|
||||
"user" : "rani",
|
||||
"geoip" : {
|
||||
"country_iso_code" : "EG",
|
||||
"location" : {
|
||||
"lon" : 31.3,
|
||||
"lat" : 30.1
|
||||
},
|
||||
"region_name" : "Cairo Governorate",
|
||||
"continent_name" : "Africa",
|
||||
"city_name" : "Cairo"
|
||||
},
|
||||
"event" : {
|
||||
"dataset" : "sample_ecommerce"
|
||||
}
|
||||
}
|
||||
...
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
## scripted_metric
|
||||
|
||||
The `scripted_metric` metric is a multi-value metric aggregation that returns metrics calculated from a specified script.
|
||||
|
||||
A script has four stages: the initial stage, the map stage, the combine stage, and the reduce stage.
|
||||
|
||||
* `init_script`: (OPTIONAL) Sets the initial state and executes before any collection of documents.
|
||||
* `map_script`: Checks the value of the `type` field and executes the aggregation on the collected documents.
|
||||
* `combine_script`: Aggregates the state returned from every shard. The aggregated value is returned to the coordinating node.
|
||||
* `reduce_script`: Provides access to the variable states; this variable combines the results from the `combine_script` on each shard into an array.
|
||||
|
||||
The following example aggregates the different HTTP response types in web log data:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_logs/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggregations": {
|
||||
"responses.counts": {
|
||||
"scripted_metric": {
|
||||
"init_script": "state.responses = ['error':0L,'success':0L,'other':0L]",
|
||||
"map_script": """
|
||||
def code = doc['response.keyword'].value;
|
||||
if (code.startsWith('5') || code.startsWith('4')) {
|
||||
state.responses.error += 1 ;
|
||||
} else if(code.startsWith('2')) {
|
||||
state.responses.success += 1;
|
||||
} else {
|
||||
state.responses.other += 1;
|
||||
}
|
||||
""",
|
||||
"combine_script": "state.responses",
|
||||
"reduce_script": """
|
||||
def counts = ['error': 0L, 'success': 0L, 'other': 0L];
|
||||
for (responses in states) {
|
||||
counts.error += responses['error'];
|
||||
counts.success += responses['success'];
|
||||
counts.other += responses['other'];
|
||||
}
|
||||
return counts;
|
||||
"""
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"responses.counts" : {
|
||||
"value" : {
|
||||
"other" : 0,
|
||||
"success" : 12832,
|
||||
"error" : 1242
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
---
|
||||
layout: default
|
||||
title: Average
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 10
|
||||
---
|
||||
|
||||
# Average aggregations
|
||||
|
||||
The `avg` metric is a single-value metric aggregations that returns the average value of a field.
|
||||
|
||||
The following example calculates the average of the `taxful_total_price` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"avg_taxful_total_price": {
|
||||
"avg": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
{
|
||||
"took": 85,
|
||||
"timed_out": false,
|
||||
"_shards": {
|
||||
"total": 1,
|
||||
"successful": 1,
|
||||
"skipped": 0,
|
||||
"failed": 0
|
||||
},
|
||||
"hits": {
|
||||
"total": {
|
||||
"value": 4675,
|
||||
"relation": "eq"
|
||||
},
|
||||
"max_score": null,
|
||||
"hits": []
|
||||
},
|
||||
"aggregations": {
|
||||
"sum_taxful_total_price": {
|
||||
"value": 75.05542864304813
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
---
|
||||
layout: default
|
||||
title: Cardinality
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 20
|
||||
---
|
||||
|
||||
# Cardinality aggregations
|
||||
|
||||
The `cardinality` metric is a single-value metric aggregation that counts the number of unique or distinct values of a field.
|
||||
|
||||
The following example finds the number of unique products in an eCommerce store:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"unique_products": {
|
||||
"cardinality": {
|
||||
"field": "products.product_id"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"unique_products" : {
|
||||
"value" : 7033
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
Cardinality count is approximate.
|
||||
If you have tens of thousands of products in your hypothetical store, an accurate cardinality calculation requires loading all the values into a hash set and returning its size. This approach doesn't scale well; it requires huge amounts of memory and can cause high latencies.
|
||||
|
||||
You can control the trade-off between memory and accuracy with the `precision_threshold` setting. This setting defines the threshold below which counts are expected to be close to accurate. Above this value, counts might become a bit less accurate. The default value of `precision_threshold` is 3,000. The maximum supported value is 40,000.
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"unique_products": {
|
||||
"cardinality": {
|
||||
"field": "products.product_id",
|
||||
"precision_threshold": 10000
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
---
|
||||
layout: default
|
||||
title: Extended stats
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 30
|
||||
---
|
||||
|
||||
# Extended stats aggregations
|
||||
|
||||
The `extended_stats` aggregation is an extended version of the [`stats`]({{site.url}}{{site.baseurl}}/query-dsl/aggregations/metric/stats/) aggregation. Apart from including basic stats, `extended_stats` also returns stats such as `sum_of_squares`, `variance`, and `std_deviation`.
|
||||
The following example returns extended stats for `taxful_total_price`:
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"extended_stats_taxful_total_price": {
|
||||
"extended_stats": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"extended_stats_taxful_total_price" : {
|
||||
"count" : 4675,
|
||||
"min" : 6.98828125,
|
||||
"max" : 2250.0,
|
||||
"avg" : 75.05542864304813,
|
||||
"sum" : 350884.12890625,
|
||||
"sum_of_squares" : 3.9367749294174194E7,
|
||||
"variance" : 2787.59157113862,
|
||||
"variance_population" : 2787.59157113862,
|
||||
"variance_sampling" : 2788.187974983536,
|
||||
"std_deviation" : 52.79764740155209,
|
||||
"std_deviation_population" : 52.79764740155209,
|
||||
"std_deviation_sampling" : 52.80329511482722,
|
||||
"std_deviation_bounds" : {
|
||||
"upper" : 180.6507234461523,
|
||||
"lower" : -30.53986616005605,
|
||||
"upper_population" : 180.6507234461523,
|
||||
"lower_population" : -30.53986616005605,
|
||||
"upper_sampling" : 180.66201887270256,
|
||||
"lower_sampling" : -30.551161586606312
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The `std_deviation_bounds` object provides a visual variance of the data with an interval of plus/minus two standard deviations from the mean.
|
||||
To set the standard deviation to a different value, say 3, set `sigma` to 3:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"extended_stats_taxful_total_price": {
|
||||
"extended_stats": {
|
||||
"field": "taxful_total_price",
|
||||
"sigma": 3
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,48 @@
|
|||
---
|
||||
layout: default
|
||||
title: Geobounds
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 40
|
||||
---
|
||||
|
||||
## Geobounds aggregations
|
||||
|
||||
The `geo_bounds` metric is a multi-value metric aggregation that calculates the bounding box in terms of latitude and longitude around a `geo_point` field.
|
||||
|
||||
The following example returns the `geo_bounds` metrics for the `geoip.location` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"geo": {
|
||||
"geo_bounds": {
|
||||
"field": "geoip.location"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
"aggregations" : {
|
||||
"geo" : {
|
||||
"bounds" : {
|
||||
"top_left" : {
|
||||
"lat" : 52.49999997206032,
|
||||
"lon" : -118.20000001229346
|
||||
},
|
||||
"bottom_right" : {
|
||||
"lat" : 4.599999985657632,
|
||||
"lon" : 55.299999956041574
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
---
|
||||
layout: default
|
||||
title: Metric aggregations
|
||||
parent: Aggregations
|
||||
has_children: true
|
||||
has_toc: true
|
||||
nav_order: 2
|
||||
redirect_from:
|
||||
- /opensearch/metric-agg/
|
||||
- /query-dsl/aggregations/metric-agg/
|
||||
- /aggregations/metric-agg/
|
||||
---
|
||||
|
||||
# Metric aggregations
|
||||
|
||||
Metric aggregations let you perform simple calculations such as finding the minimum, maximum, and average values of a field.
|
||||
|
||||
## Types of metric aggregations
|
||||
|
||||
Metric aggregations are of two types: single-value metric aggregations and multi-value metric aggregations.
|
||||
|
||||
### Single-value metric aggregations
|
||||
|
||||
Single-value metric aggregations return a single metric. For example, `sum`, `min`, `max`, `avg`, `cardinality`, and `value_count`.
|
||||
|
||||
### Multi-value metric aggregations
|
||||
|
||||
Multi-value metric aggregations return more than one metric. For example, `stats`, `extended_stats`, `matrix_stats`, `percentile`, `percentile_ranks`, `geo_bound`, `top_hits`, and `scripted_metric`.
|
||||
|
|
@ -0,0 +1,85 @@
|
|||
---
|
||||
layout: default
|
||||
title: Matrix stats
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 50
|
||||
---
|
||||
|
||||
# Matrix stats aggregations
|
||||
|
||||
The `matrix_stats` aggregation generates advanced stats for multiple fields in a matrix form.
|
||||
The following example returns advanced stats in a matrix form for the `taxful_total_price` and `products.base_price` fields:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"matrix_stats_taxful_total_price": {
|
||||
"matrix_stats": {
|
||||
"fields": ["taxful_total_price", "products.base_price"]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"matrix_stats_taxful_total_price" : {
|
||||
"doc_count" : 4675,
|
||||
"fields" : [
|
||||
{
|
||||
"name" : "products.base_price",
|
||||
"count" : 4675,
|
||||
"mean" : 34.994239430147196,
|
||||
"variance" : 360.5035285833703,
|
||||
"skewness" : 5.530161335032702,
|
||||
"kurtosis" : 131.16306324042148,
|
||||
"covariance" : {
|
||||
"products.base_price" : 360.5035285833703,
|
||||
"taxful_total_price" : 846.6489362233166
|
||||
},
|
||||
"correlation" : {
|
||||
"products.base_price" : 1.0,
|
||||
"taxful_total_price" : 0.8444765264325268
|
||||
}
|
||||
},
|
||||
{
|
||||
"name" : "taxful_total_price",
|
||||
"count" : 4675,
|
||||
"mean" : 75.05542864304839,
|
||||
"variance" : 2788.1879749835402,
|
||||
"skewness" : 15.812149139924037,
|
||||
"kurtosis" : 619.1235507385902,
|
||||
"covariance" : {
|
||||
"products.base_price" : 846.6489362233166,
|
||||
"taxful_total_price" : 2788.1879749835402
|
||||
},
|
||||
"correlation" : {
|
||||
"products.base_price" : 0.8444765264325268,
|
||||
"taxful_total_price" : 1.0
|
||||
}
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
The following table lists all response fields.
|
||||
|
||||
Statistic | Description
|
||||
:--- | :---
|
||||
`count` | The number of samples measured.
|
||||
`mean` | The average value of the field measured from the sample.
|
||||
`variance` | How far the values of the field measured are spread out from its mean value. The larger the variance, the more it's spread from its mean value.
|
||||
`skewness` | An asymmetric measure of the distribution of the field's values around the mean.
|
||||
`kurtosis` | A measure of the tail heaviness of a distribution. As the tail becomes lighter, kurtosis decreases. As the tail becomes heavier, kurtosis increases. To learn about kurtosis, see [Wikipedia](https://en.wikipedia.org/wiki/Kurtosis).
|
||||
`covariance` | A measure of the joint variability between two fields. A positive value means their values move in the same direction and the other way around.
|
||||
`correlation` | A measure of the strength of the relationship between two fields. The valid values are between [-1, 1]. A value of -1 means that the value is negatively correlated and a value of 1 means that it's positively correlated. A value of 0 means that there's no identifiable relationship between them.
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
---
|
||||
layout: default
|
||||
title: Maximum
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 60
|
||||
---
|
||||
|
||||
# Maximum aggregations
|
||||
|
||||
The `max` metric is a single-value metric aggregations that returns the maximum value of a field.
|
||||
|
||||
The following example calculates the maximum of the `taxful_total_price` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"max_taxful_total_price": {
|
||||
"max": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
{
|
||||
"took": 17,
|
||||
"timed_out": false,
|
||||
"_shards": {
|
||||
"total": 1,
|
||||
"successful": 1,
|
||||
"skipped": 0,
|
||||
"failed": 0
|
||||
},
|
||||
"hits": {
|
||||
"total": {
|
||||
"value": 4675,
|
||||
"relation": "eq"
|
||||
},
|
||||
"max_score": null,
|
||||
"hits": []
|
||||
},
|
||||
"aggregations": {
|
||||
"max_taxful_total_price": {
|
||||
"value": 2250
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
---
|
||||
layout: default
|
||||
title: Minimum
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 70
|
||||
---
|
||||
|
||||
# Minimum aggregations
|
||||
|
||||
The `min` metric is a single-value metric aggregations that returns the minimum value of a field.
|
||||
|
||||
The following example calculates the minimum of the `taxful_total_price` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"min_taxful_total_price": {
|
||||
"min": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
{
|
||||
"took": 13,
|
||||
"timed_out": false,
|
||||
"_shards": {
|
||||
"total": 1,
|
||||
"successful": 1,
|
||||
"skipped": 0,
|
||||
"failed": 0
|
||||
},
|
||||
"hits": {
|
||||
"total": {
|
||||
"value": 4675,
|
||||
"relation": "eq"
|
||||
},
|
||||
"max_score": null,
|
||||
"hits": []
|
||||
},
|
||||
"aggregations": {
|
||||
"min_taxful_total_price": {
|
||||
"value": 6.98828125
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,45 @@
|
|||
---
|
||||
layout: default
|
||||
title: Percentile ranks
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 80
|
||||
---
|
||||
|
||||
# Percentile rank aggregations
|
||||
|
||||
Percentile rank is the percentile of values at or below a threshold grouped by a specified value. For example, if a value is greater than or equal to 80% of the values, it has a percentile rank of 80.
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"percentile_rank_taxful_total_price": {
|
||||
"percentile_ranks": {
|
||||
"field": "taxful_total_price",
|
||||
"values": [
|
||||
10,
|
||||
15
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"percentile_rank_taxful_total_price" : {
|
||||
"values" : {
|
||||
"10.0" : 0.055096056411283456,
|
||||
"15.0" : 0.0830092961834656
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,52 @@
|
|||
---
|
||||
layout: default
|
||||
title: Percentile
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 90
|
||||
---
|
||||
|
||||
# Percentile aggregations
|
||||
|
||||
Percentile is the percentage of the data that's at or below a certain threshold value.
|
||||
|
||||
The `percentile` metric is a multi-value metric aggregation that lets you find outliers in your data or figure out the distribution of your data.
|
||||
|
||||
Like the `cardinality` metric, the `percentile` metric is also approximate.
|
||||
|
||||
The following example calculates the percentile in relation to the `taxful_total_price` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"percentile_taxful_total_price": {
|
||||
"percentiles": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"percentile_taxful_total_price" : {
|
||||
"values" : {
|
||||
"1.0" : 21.984375,
|
||||
"5.0" : 27.984375,
|
||||
"25.0" : 44.96875,
|
||||
"50.0" : 64.22061688311689,
|
||||
"75.0" : 93.0,
|
||||
"95.0" : 156.0,
|
||||
"99.0" : 222.0
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,71 @@
|
|||
---
|
||||
layout: default
|
||||
title: Scripted metric
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 100
|
||||
---
|
||||
|
||||
# Scripted metric aggregations
|
||||
|
||||
The `scripted_metric` metric is a multi-value metric aggregation that returns metrics calculated from a specified script.
|
||||
|
||||
A script has four stages: the initial stage, the map stage, the combine stage, and the reduce stage.
|
||||
|
||||
* `init_script`: (OPTIONAL) Sets the initial state and executes before any collection of documents.
|
||||
* `map_script`: Checks the value of the `type` field and executes the aggregation on the collected documents.
|
||||
* `combine_script`: Aggregates the state returned from every shard. The aggregated value is returned to the coordinating node.
|
||||
* `reduce_script`: Provides access to the variable states; this variable combines the results from the `combine_script` on each shard into an array.
|
||||
|
||||
The following example aggregates the different HTTP response types in web log data:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_logs/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggregations": {
|
||||
"responses.counts": {
|
||||
"scripted_metric": {
|
||||
"init_script": "state.responses = ['error':0L,'success':0L,'other':0L]",
|
||||
"map_script": """
|
||||
def code = doc['response.keyword'].value;
|
||||
if (code.startsWith('5') || code.startsWith('4')) {
|
||||
state.responses.error += 1 ;
|
||||
} else if(code.startsWith('2')) {
|
||||
state.responses.success += 1;
|
||||
} else {
|
||||
state.responses.other += 1;
|
||||
}
|
||||
""",
|
||||
"combine_script": "state.responses",
|
||||
"reduce_script": """
|
||||
def counts = ['error': 0L, 'success': 0L, 'other': 0L];
|
||||
for (responses in states) {
|
||||
counts.error += responses['error'];
|
||||
counts.success += responses['success'];
|
||||
counts.other += responses['other'];
|
||||
}
|
||||
return counts;
|
||||
"""
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"responses.counts" : {
|
||||
"value" : {
|
||||
"other" : 0,
|
||||
"success" : 12832,
|
||||
"error" : 1242
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
---
|
||||
layout: default
|
||||
title: Stats aggregations
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 110
|
||||
---
|
||||
|
||||
# Stats aggregations
|
||||
|
||||
The `stats` metric is a multi-value metric aggregation that returns all basic metrics such as `min`, `max`, `sum`, `avg`, and `value_count` in one aggregation query.
|
||||
|
||||
The following example returns the basic stats for the `taxful_total_price` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"stats_taxful_total_price": {
|
||||
"stats": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"stats_taxful_total_price" : {
|
||||
"count" : 4675,
|
||||
"min" : 6.98828125,
|
||||
"max" : 2250.0,
|
||||
"avg" : 75.05542864304813,
|
||||
"sum" : 350884.12890625
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,56 @@
|
|||
---
|
||||
layout: default
|
||||
title: Sum
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 120
|
||||
---
|
||||
|
||||
# Sum aggregations
|
||||
|
||||
The `sum` metric is a single-value metric aggregations that returns the sum of the values of a field.
|
||||
|
||||
The following example calculates the total sum of the `taxful_total_price` field:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"sum_taxful_total_price": {
|
||||
"sum": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
{
|
||||
"took": 16,
|
||||
"timed_out": false,
|
||||
"_shards": {
|
||||
"total": 1,
|
||||
"successful": 1,
|
||||
"skipped": 0,
|
||||
"failed": 0
|
||||
},
|
||||
"hits": {
|
||||
"total": {
|
||||
"value": 4675,
|
||||
"relation": "eq"
|
||||
},
|
||||
"max_score": null,
|
||||
"hits": []
|
||||
},
|
||||
"aggregations": {
|
||||
"sum_taxful_total_price": {
|
||||
"value": 350884.12890625
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,147 @@
|
|||
---
|
||||
layout: default
|
||||
title: Top hits
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 130
|
||||
---
|
||||
|
||||
# Top hits aggregations
|
||||
|
||||
The `top_hits` metric is a multi-value metric aggregation that ranks the matching documents based on a relevance score for the field that's being aggregated.
|
||||
|
||||
You can specify the following options:
|
||||
|
||||
- `from`: The starting position of the hit.
|
||||
- `size`: The maximum size of hits to return. The default value is 3.
|
||||
- `sort`: How the matching hits are sorted. By default, the hits are sorted by the relevance score of the aggregation query.
|
||||
|
||||
The following example returns the top 5 products in your eCommerce data:
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"top_hits_products": {
|
||||
"top_hits": {
|
||||
"size": 5
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"top_hits_products" : {
|
||||
"hits" : {
|
||||
"total" : {
|
||||
"value" : 4675,
|
||||
"relation" : "eq"
|
||||
},
|
||||
"max_score" : 1.0,
|
||||
"hits" : [
|
||||
{
|
||||
"_index" : "opensearch_dashboards_sample_data_ecommerce",
|
||||
"_type" : "_doc",
|
||||
"_id" : "glMlwXcBQVLeQPrkHPtI",
|
||||
"_score" : 1.0,
|
||||
"_source" : {
|
||||
"category" : [
|
||||
"Women's Accessories",
|
||||
"Women's Clothing"
|
||||
],
|
||||
"currency" : "EUR",
|
||||
"customer_first_name" : "rania",
|
||||
"customer_full_name" : "rania Evans",
|
||||
"customer_gender" : "FEMALE",
|
||||
"customer_id" : 24,
|
||||
"customer_last_name" : "Evans",
|
||||
"customer_phone" : "",
|
||||
"day_of_week" : "Sunday",
|
||||
"day_of_week_i" : 6,
|
||||
"email" : "rania@evans-family.zzz",
|
||||
"manufacturer" : [
|
||||
"Tigress Enterprises"
|
||||
],
|
||||
"order_date" : "2021-02-28T14:16:48+00:00",
|
||||
"order_id" : 583581,
|
||||
"products" : [
|
||||
{
|
||||
"base_price" : 10.99,
|
||||
"discount_percentage" : 0,
|
||||
"quantity" : 1,
|
||||
"manufacturer" : "Tigress Enterprises",
|
||||
"tax_amount" : 0,
|
||||
"product_id" : 19024,
|
||||
"category" : "Women's Accessories",
|
||||
"sku" : "ZO0082400824",
|
||||
"taxless_price" : 10.99,
|
||||
"unit_discount_amount" : 0,
|
||||
"min_price" : 5.17,
|
||||
"_id" : "sold_product_583581_19024",
|
||||
"discount_amount" : 0,
|
||||
"created_on" : "2016-12-25T14:16:48+00:00",
|
||||
"product_name" : "Snood - white/grey/peach",
|
||||
"price" : 10.99,
|
||||
"taxful_price" : 10.99,
|
||||
"base_unit_price" : 10.99
|
||||
},
|
||||
{
|
||||
"base_price" : 32.99,
|
||||
"discount_percentage" : 0,
|
||||
"quantity" : 1,
|
||||
"manufacturer" : "Tigress Enterprises",
|
||||
"tax_amount" : 0,
|
||||
"product_id" : 19260,
|
||||
"category" : "Women's Clothing",
|
||||
"sku" : "ZO0071900719",
|
||||
"taxless_price" : 32.99,
|
||||
"unit_discount_amount" : 0,
|
||||
"min_price" : 17.15,
|
||||
"_id" : "sold_product_583581_19260",
|
||||
"discount_amount" : 0,
|
||||
"created_on" : "2016-12-25T14:16:48+00:00",
|
||||
"product_name" : "Cardigan - grey",
|
||||
"price" : 32.99,
|
||||
"taxful_price" : 32.99,
|
||||
"base_unit_price" : 32.99
|
||||
}
|
||||
],
|
||||
"sku" : [
|
||||
"ZO0082400824",
|
||||
"ZO0071900719"
|
||||
],
|
||||
"taxful_total_price" : 43.98,
|
||||
"taxless_total_price" : 43.98,
|
||||
"total_quantity" : 2,
|
||||
"total_unique_products" : 2,
|
||||
"type" : "order",
|
||||
"user" : "rani",
|
||||
"geoip" : {
|
||||
"country_iso_code" : "EG",
|
||||
"location" : {
|
||||
"lon" : 31.3,
|
||||
"lat" : 30.1
|
||||
},
|
||||
"region_name" : "Cairo Governorate",
|
||||
"continent_name" : "Africa",
|
||||
"city_name" : "Cairo"
|
||||
},
|
||||
"event" : {
|
||||
"dataset" : "sample_ecommerce"
|
||||
}
|
||||
}
|
||||
...
|
||||
}
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
---
|
||||
layout: default
|
||||
title: Value count
|
||||
parent: Metric aggregations
|
||||
grand_parent: Aggregations
|
||||
nav_order: 140
|
||||
---
|
||||
|
||||
# Value count aggregations
|
||||
|
||||
The `value_count` metric is a single-value metric aggregation that calculates the number of values that an aggregation is based on.
|
||||
|
||||
For example, you can use the `value_count` metric with the `avg` metric to find how many numbers the aggregation uses to calculate an average value.
|
||||
|
||||
```json
|
||||
GET opensearch_dashboards_sample_data_ecommerce/_search
|
||||
{
|
||||
"size": 0,
|
||||
"aggs": {
|
||||
"number_of_values": {
|
||||
"value_count": {
|
||||
"field": "taxful_total_price"
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
{% include copy-curl.html %}
|
||||
|
||||
#### Example response
|
||||
|
||||
```json
|
||||
...
|
||||
"aggregations" : {
|
||||
"number_of_values" : {
|
||||
"value" : 4675
|
||||
}
|
||||
}
|
||||
}
|
||||
```
|
||||
Loading…
Reference in New Issue