GET /bank/_search { "query": { "bool": { "must": { "match_all": {} }, "filter": { "range": { "balance": { "gte": 20000, "lte": 30000 } } } } } }
GET /bank/_search { "size": 0, "aggs": { "group_by_state": { "terms": { "field": "state.keyword" } } } }
In SQL, the above aggregation is similar in concept to:
SELECT state, COUNT(*) FROM bank GROUP BY state ORDER BY COUNT(*) DESC LIMIT 10;
Building on the previous aggregation, let’s now sort on the average balance in descending order: GET /bank/_search { "size": 0, "aggs": { "group_by_state": { "terms": { "field": "state.keyword" }, "aggs": { "average_balance": { "avg": { "field": "balance" } } } } } }
This example demonstrates how we can group by age brackets (ages 20-29, 30-39, and 40-49), then by gender, and then finally get the average account balance, per age bracket, per gender:
GET /bank/_search { "size": 0, "aggs": { "group_by_age": { "range": { "field": "age", "ranges": [ { "from": 20, "to": 30 }, { "from": 30, "to": 40 }, { "from": 40, "to": 50 } ] }, "aggs": { "group_by_gender": { "terms": { "field": "gender.keyword" }, "aggs": { "average_balance": { "avg": { "field": "balance" } } } } } } } }