Elasticsearch 去重后求和

本文主要是介绍Elasticsearch 去重后求和，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

标题的要求可以用如下 SQL 表示

select sum(column2) from (select distinct(column1),column2 from table)t

要如何用 DSL 实现呢，先准备下索引和数据

PUT test_index
{"mappings": {"properties": {"column1": {"type": "keyword"},"column2": {"type": "long"}}}
}

PUT test_index/_doc/1
{"column1": "1","column2": 2
}PUT test_index/_doc/2
{"column1": "1","column2": 2
}PUT test_index/_doc/3
{"column1": "2","column2": 1
}PUT test_index/_doc/4
{"column1": "2","column2": 1
}

我首先想到的是 collapse 搭配 cardinality，再sum，那效果如何呢

GET test_index/_search
{"collapse": {"field": "column1"},"aggs": {"distinct_column": {"cardinality": {"field": "column1"}},"distinct_sum":{"sum": {"field": "column2"}},"all_sum":{"sum": {"field": "column2"}}}
}

结果，count是去重了的，但sum没有

{"took" : 5,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 4,"relation" : "eq"},"max_score" : null,"hits" : [{"_index" : "test_index","_type" : "_doc","_id" : "1","_score" : 1.0,"_source" : {"column1" : "1","column2" : 2},"fields" : {"column1" : ["1"]}},{"_index" : "test_index","_type" : "_doc","_id" : "3","_score" : 1.0,"_source" : {"column1" : "2","column2" : 1},"fields" : {"column1" : ["2"]}}]},"aggregations" : {"distinct_sum" : {"value" : 6.0},"all_sum" : {"value" : 6.0},"distinct_column" : {"value" : 2}}
}

尝试多次未果后，找到了这个

Sum aggregation on Unique Data in ElasticSearch - Stack Overflow

那试下呗

GET test_index/_search
{"size": 0, "aggs": {"column1_count": {"terms": {"field": "column1","size": 100},"aggs": {"column2_avg": {"avg": {"field": "column2"}}}},"unique_count": {"cardinality": {"field": "column1"}},"unique_sum_column2":{"sum_bucket": {"buckets_path": "column1_count>column2_avg"}},"sum_column2":{"sum": {"field": "column2"}}}
}

非常好，达到期望，这个是先求平均值，再求和，为这思路叹服

{"took" : 5,"timed_out" : false,"_shards" : {"total" : 1,"successful" : 1,"skipped" : 0,"failed" : 0},"hits" : {"total" : {"value" : 4,"relation" : "eq"},"max_score" : null,"hits" : [ ]},"aggregations" : {"sum_column2" : {"value" : 6.0},"column1_count" : {"doc_count_error_upper_bound" : 0,"sum_other_doc_count" : 0,"buckets" : [{"key" : "1","doc_count" : 2,"column2_avg" : {"value" : 2.0}},{"key" : "2","doc_count" : 2,"column2_avg" : {"value" : 1.0}}]},"unique_count" : {"value" : 2},"unique_sum_column2" : {"value" : 3.0}}
}

这篇关于Elasticsearch 去重后求和的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！

---