ES 24 - 如何通过Elasticsearch进行聚合检索 (分组统计)
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目录
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
,
......
]
]
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