1 普通聚合分析
1.1 直接聚合统计
(1) 计算每个tag下的文档数量, 请求语法:
GET book_shop/it_book/_search{ "size": 0, // 不显示命中(hits)的所有文档信息 "aggs": { "group_by_tags": { // 聚合结果的名称, 需要自定义(复制时请去掉此注释) "terms": { "field": "tags" } } }}
(2) 发生错误:
说明: 索引book_shop的mapping映射是ES自动创建的, 它把tag解析成了text类型, 在发起对tag的聚合请求后, 将抛出如下错误:
{ "error": { "root_cause": [ { "type": "illegal_argument_exception", "reason": "Fielddata is disabled on text fields by default. Set fielddata=true on [tags] in order to load fielddata in memory by uninverting the inverted index. Note that this can however use significant memory. Alternatively use a keyword field instead." } ], "type": "search_phase_execution_exception", "reason": "all shards failed", "phase": "query", "grouped": true, "failed_shards": [......] }, "status": 400}
(3) 错误分析:
错误信息: Set fielddata=true on [xxxx] ......
错误分析: 默认情况下, Elasticsearch 对 text 类型的字段(field)禁用了 fielddata;
text 类型的字段在创建索引时会进行分词处理, 而聚合操作必须基于字段的原始值进行分析;
所以如果要对 text 类型的字段进行聚合操作, 就需要存储其原始值 —— 创建mapping时指定fielddata=true, 以便通过反转倒排索引(即正排索引)将索引数据加载至内存中.
(4) 解决方案一: 对text类型的字段开启fielddata属性:
将要分组统计的text field(即tags)的fielddata设置为true:
PUT book_shop/_mapping/it_book{ "properties": { "tags": { "type": "text", "fielddata": true } }}
可参考官方文档进行设置:
https://www.elastic.co/guide/en/elasticsearch/reference/6.6/fielddata.html. 成功后的结果如下:
{ "acknowledged": true}
再次统计, 得到的结果如下:
{ "took": 153, "timed_out": false, "_shards": { "total": 5, "successful": 5, "skipped": 0, "failed": 0 }, "hits": { "total": 4, "max_score": 0.0, "hits": [] }, "aggregations": { "group_by_tags": { "doc_count_error_upper_bound": 0, "sum_other_doc_count": 6, "buckets": [ { "key": "java", "doc_count": 3 }, { "key": "程", "doc_count": 2 }, ...... ] } }}
(5) 解决方法二: 使用内置keyword字段:
开启fielddata将占用大量的内存.
Elasticsearch 5.x 版本开始支持通过text的内置字段keyword作精确查询、聚合分析:
GET shop/it_book/_search{ size": 0, "aggs": { "group_by_tags": { "terms": { "field": "tags.keyword" // 使用text类型的内置keyword字段 } } }}
1.2 先检索, 再聚合
(1) 统计name中含有“jvm”的图书中每个tag的文档数量, 请求语法:
GET book_shop/it_book/_search{ "query": { "match": { "name": "jvm" } }, "aggs": { "group_by_tags": { // 聚合结果的名称, 需要自定义. 下面使用内置的keyword字段: "terms": { "field": "tags.keyword" } } }}
(2) 响应结果:
{ "took" : 7, "timed_out" : false, "_shards" : { "total" : 5, "successful" : 5, "skipped" : 0, "failed" : 0 }, "hits" : { "total" : 1, "max_score" : 0.64072424, "hits" : [ { "_index" : "book_shop", "_type" : "it_book", "_id" : "2", "_score" : 0.64072424, "_source" : { "name" : "深入理解Java虚拟机：JVM高级特性与最佳实践", "author" : "周志明", "category" : "编程语言", "desc" : "Java图书领域公认的经典著作", "price" : 79.0, "date" : "2013-10-01", "publisher" : "机械工业出版社", "tags" : [ "Java", "虚拟机", "最佳实践" ] } } ] }, "aggregations" : { "group_by_tags" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "Java", "doc_count" : 1 }, { "key" : "最佳实践", "doc_count" : 1 }, { "key" : "虚拟机", "doc_count" : 1 } ] } }}
1.3 扩展: fielddata和keyword的聚合比较
为某个 text 类型的字段开启fielddata字段后, 聚合分析操作会对这个字段的所有分词分别进行聚合, 获得的结果大多数情况下并不符合我们的需求.
使用keyword内置字段, 不会对相关的分词进行聚合, 结果可能更有用.
推荐使用text类型字段的内置keyword进行聚合操作.
2 嵌套聚合
2.1 先分组, 再聚合统计
(1) 先按tags分组, 再计算每个tag下图书的平均价格, 请求语法:
GET book_shop/it_book/_search{ "size": 0, "aggs": { "group_by_tags": { "terms": { "field": "tags.keyword" }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } }}
(2) 响应结果:
"hits" : { "total" : 3, "max_score" : 0.0, "hits" : [ ] }, "aggregations" : { "group_by_tags" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "Java", "doc_count" : 3, "avg_price" : { "value" : 102.33333333333333 } }, { "key" : "编程语言", "doc_count" : 2, "avg_price" : { "value" : 114.0 } }, ...... ] } }
2.2 先分组, 再统计, 最后排序
(1) 计算每个tag下图书的平均价格, 再按平均价格降序排序, 查询语法:
GET book_shop/it_book/_search{ "size": 0, "aggs": { "all_tags": { "terms": { "field": "tags.keyword", "order": { "avg_price": "desc" } // 根据下述统计的结果排序 }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } }}
(2) 响应结果:
与#2.1节内容相似, 区别在于按照价格排序显示了.
2.3 先分组, 组内再分组, 然后统计、排序
(1) 先按价格区间分组, 组内再按tags分组, 计算每个tags组的平均价格, 查询语法:
GET book_shop/it_book/_search{ "size": 0, "aggs": { "group_by_price": { "range": { "field": "price", "ranges": [ { "from": 00, "to": 100 }, { "from": 100, "to": 150 } ] }, "aggs": { "group_by_tags": { "terms": { "field": "tags.keyword" }, "aggs": { "avg_price": { "avg": { "field": "price" } } } } } } }}
(2) 响应结果:
"hits" : { "total" : 3, "max_score" : 0.0, "hits" : [ ] }, "aggregations" : { "group_by_price" : { "buckets" : [ { "key" : "0.0-100.0", // 区间0.0-100.0 "from" : 0.0, "to" : 100.0, "doc_count" : 1, // 共查找到了3条文档 "group_by_tags" : { // 对tags分组聚合 "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "Java", "doc_count" : 1, "avg_price" : { "value" : 79.0 } }, ...... ] } }, { "key" : "100.0-150.0", "from" : 100.0, "to" : 150.0, "doc_count" : 2, "group_by_tags" : { "doc_count_error_upper_bound" : 0, "sum_other_doc_count" : 0, "buckets" : [ { "key" : "Java", "doc_count" : 2, "avg_price" : { "value" : 114.0 } }, ...... } ] } } ] } }
版权声明
作者: 马瘦风(https://healchow.com)
出处: 博客园 马瘦风的博客(https://www.cnblogs.com/shoufeng)
感谢阅读, 如果文章有帮助或启发到你, 点个[好文要顶??] 或 [推荐??] 吧??
本文版权归博主所有, 欢迎转载, 但 [必须在文章页面明显位置标明原文链接], 否则博主保留追究相关人员法律责任的权利.
经验首页