Because Elasticsearch runs this across a distributed index rather than a single table, it computes aggregations far faster than a traditional RDBMS running the equivalent GROUP BY logic. That speed is a big reason Elasticsearch is used for big data analytics, not only search.<\/p>\n
What is an “aggs” Query in Elasticsearch?<\/h2>\n
Every aggregation runs inside the aggs<\/b><\/strong> (short for aggregations) parameter of a search request. You give each aggregation a name, then define what it should compute, for example,\u00a0 grouping documents, calculating a metric, or both together in the same query.<\/p>\n Most real queries combine a bucket aggregation with one or more metric aggregations, which is exactly what the three examples below show.<\/p>\n The query below defines a bucket aggregation<\/b><\/strong>\u00a0named popular_cars inside the aggs component. It groups documents by the make field, so each distinct car make becomes its own bucket, with a count of how many documents fall into it.<\/p>\nTypes of Aggregations in Elasticsearch<\/h2>\n
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Example 1: Grouping Documents With a Bucket Aggregation<\/h2>\n
![\"\"](\"https:\/\/prwatech.in\/blog\/wp-content\/uploads\/2019\/11\/aggssss.png\")
<\/p>\nExample 2: Running Multiple Aggregations Together<\/h2>\n
![\"\"](\"https:\/\/prwatech.in\/blog\/wp-content\/uploads\/2019\/11\/multiple-aggs.png\")
<\/p>\n![\"\"](\"https:\/\/prwatech.in\/blog\/wp-content\/uploads\/2019\/11\/aggs_output.png\")