Elasticsearch 聚合检索 (分组统计)
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:
fielddata | Elasticsearch Guide [6.6] | Elastic. 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
}
},
......
}
]
}
}
]
}
}