elasticsearc使用指南之ES管道聚合(Pipeline Aggregation)

管道聚合处理来自其他聚合而不是文档集的输出，将信息添加到输出树中。

注：关于脚本聚合目前在本文中暂时不会涉及。

主要有如下两种管道聚合方式：

• parent
• sibling

下面一一介绍ES定义的管道聚合。

1、Avg Bucket Aggregation

同级管道聚合，它计算同级聚合中指定度量的平均值。同级聚合必须是多桶聚合，针对的是度量聚合(metric Aggregation)。
示例如下：

{ "avg_bucket": { "buckets_path": "the_sum" // @1 } }

• buckets_path：指定聚合的名称，支持多级嵌套聚合。
  其他参数：
• gap_policy
  当管道聚合遇到不存在的值，有点类似于term等聚合的(missing)时所采取的策略，可选择值为：skip、insert_zeros。
• skip：此选项将丢失的数据视为bucket不存在。它将跳过桶并使用下一个可用值继续计算。
• insert_zeros：默认使用0代替。
• format
  用于格式化聚合桶的输出(key)。

示例如下：

POST /_search { "size": 0, "aggs": { "sales_per_month": { // @1 "date_histogram": { "field": "date", "interval": "month" }, "aggs": { // @2 "sales": { "sum": { "field": "price" } } } }, "avg_monthly_sales": { // @3 "avg_bucket": { "buckets_path": "sales_per_month>sales" } } } }

代码@1：首先定义第一级聚合（按月）直方图聚合。
代码@2：定义第二级聚合，在按月聚合的基础上，对每个月的文档求sum。
代码@3：对上面的聚合求平均值。

其返回结果如下：

{ ... // 省略 "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 } } ] }, "avg_monthly_sales": { // 这是对二级聚合的结果再进行一次求平均值聚合。 "value": 328.33333333333333 } } }

对应的JAVA示例如下：

public static void test_pipeline_avg_buncket_aggregation() { RestHighLevelClient client = EsClient.getClient(); try { SearchRequest searchRequest = new SearchRequest(); searchRequest.indices("aggregations_index02"); SearchSourceBuilder sourceBuilder = new SearchSourceBuilder(); AggregationBuilder aggregationBuild = AggregationBuilders.terms("seller_agg") .field("sellerId") .subAggregation(AggregationBuilders.sum("seller_num_agg") .field("num") ) ; sourceBuilder.aggregation(aggregationBuild); // 添加 avg bucket pipeline sourceBuilder.aggregation(new AvgBucketPipelineAggregationBuilder("seller_num_agg_av", "seller_agg>seller_num_agg")); sourceBuilder.size(0); searchRequest.source(sourceBuilder); SearchResponse result = client.search(searchRequest, RequestOptions.DEFAULT); System.out.println(result); } catch (Throwable e) { e.printStackTrace(); } finally { EsClient.close(client); } }

2、Percentiles Bucket Aggregation

同级管道聚合，百分位管道聚合。其JAVA示例如下：

public static void test_Percentiles_buncket_aggregation() { RestHighLevelClient client = EsClient.getClient(); try { SearchRequest searchRequest = new SearchRequest(); searchRequest.indices("aggregations_index02"); SearchSourceBuilder sourceBuilder = new SearchSourceBuilder(); AggregationBuilder aggregationBuild = AggregationBuilders.terms("seller_agg") .field("sellerId") .subAggregation(AggregationBuilders.sum("seller_num_agg") .field("num") ) ; sourceBuilder.aggregation(aggregationBuild); // 添加 avg bucket pipeline sourceBuilder.aggregation(new PercentilesBucketPipelineAggregationBuilder("seller_num_agg_av", "seller_agg>seller_num_agg")); sourceBuilder.size(0); searchRequest.source(sourceBuilder); SearchResponse result = client.search(searchRequest, RequestOptions.DEFAULT); System.out.println(result); } catch (Throwable e) { e.printStackTrace(); } finally { EsClient.close(client); } }

其返回值如下：

{ ... // 省略其他属性 "aggregations":{ "lterms#seller_agg":{ "doc_count_error_upper_bound":0, "sum_other_doc_count":12, "buckets":[ { "key":45, "doc_count":567, "sum#seller_num_agg":{ "value":911 } }, { "key":31, "doc_count":324, "sum#seller_num_agg":{ "value":353 } } // 省略其他桶的显示 ] }, "percentiles_bucket#seller_num_agg_av":{ "values":{ "1.0":5, "5.0":5, "25.0":10, "50.0":20, "75.0":290, "95.0":911, "99.0":911 } } } }

3、Cumulative Sum Aggregation

累积管道聚合，就是就是依次将每个管道的sum聚合进行累加。

其语法(restfull)如下：

{ "cumulative_sum": { "buckets_path": "the_sum" } }

支持的参数说明：

• buckets_path
  桶聚合名称，作为管道聚合的输入信息。
• format
  格式化key。

使用示例如下：

POST /sales/_search { "size": 0, "aggs" : { "sales_per_month" : { "date_histogram" : { "field" : "date", "interval" : "month" }, "aggs": { "sales": { "sum": { "field": "price" } }, "cumulative_sales": { "cumulative_sum": { "buckets_path": "sales" } } } } } }

其返回结果如下：

{ "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 }, "cumulative_sales": { "value": 550.0 } }, { "key_as_string": "2015/02/01 00:00:00", "key": 1422748800000, "doc_count": 2, "sales": { "value": 60.0 }, "cumulative_sales": { "value": 610.0 } }, { "key_as_string": "2015/03/01 00:00:00", "key": 1425168000000, "doc_count": 2, "sales": { "value": 375.0 }, "cumulative_sales": { "value": 985.0 } } ] } } }

从结果可知，cumulative_sales的值等于上一个cumulative_sales + 当前桶的sum聚合。

对应的JAVA示例如下：

{ "aggregations":{ "date_histogram#createTime_histogram":{ "buckets":{ "2015-12-01 00:00:00":{ "key_as_string":"2015-12-01 00:00:00", "key":1448928000000, "doc_count":6, "sum#seller_num_agg":{ "value":16 }, "simple_value#Cumulative_Seller_num_agg":{ "value":16 } }, "2016-01-01 00:00:00":{ "key_as_string":"2016-03-01 00:00:00", "key":1456790400000, "doc_count":10, "sum#seller_num_agg":{ "value":11 }, "simple_value#Cumulative_Seller_num_agg":{ "value":31 } } // ... 忽略 } } } }

4、Bucket Sort Aggregation

一种父管道聚合，它对其父多桶聚合的桶进行排序。并可以指定多个排序字段。每个bucket可以根据它的_key、_count或子聚合进行排序。此外，可以设置from和size的参数，以便截断结果桶。

使用语法如下：

{ "bucket_sort": { "sort": [ {"sort_field_1": {"order": "asc"}}, {"sort_field_2": {"order": "desc"}}, "sort_field_3" ], "from": 1, "size": 3 } }

支持的参数说明如下：

• sort
  定义排序结构。
• from
  用与对父聚合的桶进行截取，该值之前的所有桶将忽略，也就是不参与排序，默认为0。
• size
  返回的桶数。默认为父聚合的所有桶。
• gap_policy
  当管道聚合遇到不存在的值，有点类似于term等聚合的(missing)时所采取的策略，可选择值为：skip、insert_zeros。
• skip：此选项将丢失的数据视为bucket不存在。它将跳过桶并使用下一个可用值继续计算。
• insert_zeros：默认使用0代替。

官方示例如下：

POST /sales/_search { "size": 0, "aggs" : { "sales_per_month" : { "date_histogram" : { "field" : "date", "interval" : "month" }, "aggs": { "total_sales": { "sum": { "field": "price" } }, "sales_bucket_sort": { "bucket_sort": { "sort": [ {"total_sales": {"order": "desc"}} ], "size": 3 } } } } } }

对应的JAVA示例如下：

public static void test_bucket_sort_Aggregation() { RestHighLevelClient client = EsClient.getClient(); try { //构建日期直方图聚合 时间间隔，示例中按月统计 DateHistogramInterval interval = new DateHistogramInterval("1M"); SearchRequest searchRequest = new SearchRequest(); searchRequest.indices("aggregations_index02"); SearchSourceBuilder sourceBuilder = new SearchSourceBuilder(); AggregationBuilder aggregationBuild = AggregationBuilders.dateHistogram("createTime_histogram") .field("createTime") .dateHistogramInterval(interval) .keyed(true) .subAggregation(AggregationBuilders.sum("seller_num_agg") .field("num") ) .subAggregation(new BucketSortPipelineAggregationBuilder("seller_num_agg_sort", Arrays.asList( new FieldSortBuilder("seller_num_agg").order(SortOrder.ASC))) .from(0) .size(3)) // BucketSortPipelineAggregationBuilder(String name, List<FieldSortBuilder> sorts) .subAggregation(new CumulativeSumPipelineAggregationBuilder("Cumulative_Seller_num_agg", "seller_num_agg")) // .format("yyyy-MM-dd") // 对key的格式化 ; sourceBuilder.aggregation(aggregationBuild); sourceBuilder.size(0); sourceBuilder.query( QueryBuilders.termQuery("sellerId", 24) ); searchRequest.source(sourceBuilder); SearchResponse result = client.search(searchRequest, RequestOptions.DEFAULT); System.out.println(result); } catch (Throwable e) { e.printStackTrace(); } finally { EsClient.close(client); } }

返回值：

{ "aggregations":{ "date_histogram#createTime_histogram":{ "buckets":{ "2016-04-01 00:00:00":{ "key_as_string":"2016-04-01 00:00:00", "key":1459468800000, "doc_count":2, "sum#seller_num_agg":{ "value":2 }, "simple_value#Cumulative_Seller_num_agg":{ "value":2 } }, "2017-05-01 00:00:00":{ "key_as_string":"2017-05-01 00:00:00", "key":1493596800000, "doc_count":3, "sum#seller_num_agg":{ "value":3 }, "simple_value#Cumulative_Seller_num_agg":{ "value":5 } }, "2017-02-01 00:00:00":{ "key_as_string":"2017-02-01 00:00:00", "key":1485907200000, "doc_count":4, "sum#seller_num_agg":{ "value":4 }, "simple_value#Cumulative_Seller_num_agg":{ "value":9 } } } } }

5、Max Bucket Aggregation

与 avg类似。

6、Min Bucket Aggregation

与 avg类似。

7、Sum Bucket Aggregation

与 avg类似。

8、Stats Bucket Aggregation

与 avg类似。

本节详细介绍了ES Pipeline Aggregation 管道聚合的使用方法，重点介绍了Avg Bucket Aggregation、Percentiles Bucket Aggregation、Cumulative Sum Aggregation、Bucket Sort Aggregation、Max Bucket Aggregation、Min Bucket Aggregation、Sum Bucket Aggregation、Stats Bucket Aggregation。

---

原文发布时间为：2019-03-18
本文作者：丁威，《RocketMQ技术内幕》作者。
本文来自中间件兴趣圈，了解相关信息可以关注中间件兴趣圈。