# Sum Bucket Aggregation 原文链接 : [https://www.elastic.co/guide/en/elasticsearch/reference/5.4/search-aggregations-pipeline-sum-bucket-aggregation.html](https://www.elastic.co/guide/en/elasticsearch/reference/5.4/search-aggregations-pipeline-sum-bucket-aggregation.html) 译文链接 : [Sum Bucket Aggregation](/display/Elasticsearch/Sum+Bucket+Aggregation) 贡献者 : @苏涛，[ApacheCN](/display/~apachecn)，[Apache中文网](/display/~apachechina) 警告 此功能是实验性的，可能会在将来的版本中完全更改或删除。Elastic将采取最大的努力来解决此问题，但实验功能不受SLA官方功能的支持。 总和桶聚合用于计算一组聚合创建的所有桶中指定度量的和。指定的度量必须是数字型而且这个组聚合必须是多桶聚合。 ### 语法 sum_bucket聚合结构如下： ``` { "sum_bucket": { "buckets_path": "the_sum" } } ``` `max_bucket` 参数如下： | 参数名称 | 描述 | 是否必填 | 默认值 | | --- | --- | --- | --- | | buckets_path | 想要计算总和的桶路径，点击 [the section called “`buckets_path` Syntax](https://www.elastic.co/guide/en/elasticsearch/reference/5.4/search-aggregations-pipeline.html#buckets-path-syntax "buckets_path Syntaxedit")[edit](https://github.com/elastic/elasticsearch/edit/5.4/docs/reference/aggregations/pipeline.asciidoc "Edit this page on GitHub")”查看更多细节 | 必填 |   | | gap_policy | 当数据缺口出现时采用的策略，点击[the section called “Dealing with gaps in the data](https://www.elastic.co/guide/en/elasticsearch/reference/5.4/search-aggregations-pipeline.html#gap-policy "Dealing with gaps in the dataedit")[edit](https://github.com/elastic/elasticsearch/edit/5.4/docs/reference/aggregations/pipeline.asciidoc "Edit this page on GitHub")”查看更多细节 | 可选 | skip | | format | 用于规范聚合输出值的格式 | 可选 | null | 以下代码段计算所有月销售总额的总和： ``` POST /sales/_search { "size": 0, "aggs" : { "sales_per_month" : { "date_histogram" : { "field" : "date", "interval" : "month" }, "aggs": { "sales": { "sum": { "field": "price" } } } }, "sum_monthly_sales": { "sum_bucket": { "buckets_path": "sales_per_month>sales" #1 } } } } ``` | 1 | buckets_path指示这个sum_bucket聚合是要得到sales_per_month日期直方图中的sales聚合sum的总和 | 可能得到如下的响应： ``` { "took": 11, "timed_out": false, "_shards": ..., "hits": ..., "aggregations": { "sales_per_month": { "buckets": [ { "key_as_string": "2015/01/01 00:00:00", "key": 1420070400000, "doc_count": 3, "sales": { "value": 550.0 } }, { "key_as_string": "2015/02/01 00:00:00", "key": 1422748800000, "doc_count": 2, "sales": { "value": 60.0 } }, { "key_as_string": "2015/03/01 00:00:00", "key": 1425168000000, "doc_count": 2, "sales": { "value": 375.0 } } ] }, "sum_monthly_sales": { "value": 985.0 } } } ```