Elasticsearch 系列（五）- 数据聚合
本章将和大家分享 Elasticsearch 中的数据聚合功能，通过聚合（aggregations）可以实现对文档数据的统计、分析、运算。一、数据聚合-聚合的分类
聚合（aggregations）可以实现对文档数据的统计、分析、运算。聚合的官方文档地址：https://www.elastic.co/guide/en/elasticsearch/reference/current/search-aggregations.html
聚合常见的有三类：
1）桶（Bucket）聚合：用来对文档做分组
[*]TermAggregation：按照文档字段值分组
[*]Date Histogram：按照日期阶梯分组，例如：一周为一组，或者一月为一组。
2）度量（Metric）聚合：用于计算一些值，比如：最大值、最小值、平均值等。
[*]Avg：求平均值
[*]Max：求最大值
[*]Min：求最小值
[*]Stats：同时求max、min、avg、sum等。
3）管道（Pipeline）聚合：以其它聚合的结果为基础做聚合。
总结：
1）什么是聚合？
[*]聚合是对文档数据的统计、分析、计算
2）聚合的常见种类有哪些？
[*]Bucket：对文档数据分组，并统计每组数量
[*]Metric：对文档数据做计算，例如：avg
[*]Pipeline：基于其它聚合结果再做聚合
3）参与聚合的字段类型不能是 text（可分词的文本）类型，可以是：keyword、数值、日期、布尔类型。
二、数据聚合-DSL实现Bucket聚合
1、DSL实现Bucket聚合
现在，我们要统计所有数据中的酒店品牌有几种，此时可以根据酒店品牌的名称做聚合。
类型为term类型，DSL示例：
# 聚合功能
GET /hotel/_search
{
"size": 0, //设置size为0，结果中不包含文档，只包含聚合结果
"aggs": { //定义聚合
"brandAgg": { //给聚合起个名字
"terms": { //聚合的类型，按照品牌值聚合，所以选择term
"field": "brand", //参与聚合的字段
"size": 10 //希望获取的聚合结果数量
}
}
}
}运行结果如下：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 201,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"brandAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 39,
"buckets" : [
{
"key" : "7天酒店",
"doc_count" : 30
},
{
"key" : "如家",
"doc_count" : 30
},
{
"key" : "皇冠假日",
"doc_count" : 17
},
{
"key" : "速8",
"doc_count" : 15
},
{
"key" : "万怡",
"doc_count" : 13
},
{
"key" : "华美达",
"doc_count" : 13
},
{
"key" : "和颐",
"doc_count" : 12
},
{
"key" : "万豪",
"doc_count" : 11
},
{
"key" : "喜来登",
"doc_count" : 11
},
{
"key" : "希尔顿",
"doc_count" : 10
}
]
}
}
}2、Bucket聚合-聚合结果排序
默认情况下，Bucket聚合会统计Bucket内的文档数量，记为_count，并且按照_count降序排序。
我们可以修改结果排序方式：
# 聚合功能，自定义排序规则
GET /hotel/_search
{
"size": 0,
"aggs": {
"brandAgg": {
"terms": {
"field": "brand",
"size": 10,
"order": {
"_count": "asc" //按照_count升序排列
}
}
}
}
}运行结果如下：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 201,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"brandAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 130,
"buckets" : [
{
"key" : "万丽",
"doc_count" : 2
},
{
"key" : "丽笙",
"doc_count" : 2
},
{
"key" : "君悦",
"doc_count" : 4
},
{
"key" : "豪生",
"doc_count" : 6
},
{
"key" : "维也纳",
"doc_count" : 7
},
{
"key" : "凯悦",
"doc_count" : 8
},
{
"key" : "希尔顿",
"doc_count" : 10
},
{
"key" : "汉庭",
"doc_count" : 10
},
{
"key" : "万豪",
"doc_count" : 11
},
{
"key" : "喜来登",
"doc_count" : 11
}
]
}
}
}3、Bucket聚合-限定聚合范围
默认情况下，Bucket聚合是对索引库的所有文档做聚合，我们可以限定要聚合的文档范围，只要添加query条件即可。
示例：
# 聚合功能，限定聚合范围
GET /hotel/_search
{
"query": {
"range": {
"price": {
"lte": 200 //只对200元以下的文档聚合
}
}
},
"size": 0,
"aggs": {
"brandAgg": {
"terms": {
"field": "brand",
"size": 10,
"order": {
"_count": "asc"
}
}
}
}
}运行结果如下：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 17,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"brandAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "7天酒店",
"doc_count" : 1
},
{
"key" : "汉庭",
"doc_count" : 1
},
{
"key" : "速8",
"doc_count" : 2
},
{
"key" : "如家",
"doc_count" : 13
}
]
}
}
}4、总结
1）aggs代表聚合，与query同级，此时query的作用是？
[*]限定聚合的的文档范围
2）聚合必须的三要素是什么？
[*]聚合名称
[*]聚合类型
[*]聚合字段
3）聚合可配置属性有哪些？
[*]size：指定聚合结果数量
[*]order：指定聚合结果排序方式
[*]field：指定聚合字段
三、数据聚合-DSL实现Metric聚合
例如：我们要求获取每个品牌的用户评分的min、max、avg等值。
我们可以利用stats聚合：
# 嵌套聚合Metric
GET /hotel/_search
{
"size": 0,
"aggs": {
"brandAgg": {
"terms": {
"field": "brand",
"size": 10,
"order": {
"scoreAgg.avg": "desc" //对桶里面的数据做排序
}
},
"aggs": { //是brandAgg聚合的子聚合，也就是分组后对每组分别计算
"scoreAgg": { //聚合名称
"stats": { //聚合类型，这里stats可以计算min、max、avg等
"field": "score" //聚合字段，这里是score
}
}
}
}
}
}运行结果如下所示：
{
"took" : 0,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 201,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"brandAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 111,
"buckets" : [
{
"key" : "万丽",
"doc_count" : 2,
"scoreAgg" : {
"count" : 2,
"min" : 46.0,
"max" : 47.0,
"avg" : 46.5,
"sum" : 93.0
}
},
{
"key" : "凯悦",
"doc_count" : 8,
"scoreAgg" : {
"count" : 8,
"min" : 45.0,
"max" : 47.0,
"avg" : 46.25,
"sum" : 370.0
}
},
{
"key" : "和颐",
"doc_count" : 12,
"scoreAgg" : {
"count" : 12,
"min" : 44.0,
"max" : 47.0,
"avg" : 46.083333333333336,
"sum" : 553.0
}
},
{
"key" : "丽笙",
"doc_count" : 2,
"scoreAgg" : {
"count" : 2,
"min" : 46.0,
"max" : 46.0,
"avg" : 46.0,
"sum" : 92.0
}
},
{
"key" : "喜来登",
"doc_count" : 11,
"scoreAgg" : {
"count" : 11,
"min" : 44.0,
"max" : 48.0,
"avg" : 46.0,
"sum" : 506.0
}
},
{
"key" : "皇冠假日",
"doc_count" : 17,
"scoreAgg" : {
"count" : 17,
"min" : 44.0,
"max" : 48.0,
"avg" : 46.0,
"sum" : 782.0
}
},
{
"key" : "万豪",
"doc_count" : 11,
"scoreAgg" : {
"count" : 11,
"min" : 43.0,
"max" : 47.0,
"avg" : 45.81818181818182,
"sum" : 504.0
}
},
{
"key" : "万怡",
"doc_count" : 13,
"scoreAgg" : {
"count" : 13,
"min" : 44.0,
"max" : 48.0,
"avg" : 45.69230769230769,
"sum" : 594.0
}
},
{
"key" : "君悦",
"doc_count" : 4,
"scoreAgg" : {
"count" : 4,
"min" : 44.0,
"max" : 47.0,
"avg" : 45.5,
"sum" : 182.0
}
},
{
"key" : "希尔顿",
"doc_count" : 10,
"scoreAgg" : {
"count" : 10,
"min" : 37.0,
"max" : 48.0,
"avg" : 45.4,
"sum" : 454.0
}
}
]
}
}
}四、数据聚合-多条件聚合
需求：搜索页面中的城市、星级、品牌等信息不应该是在页面写死，而是通过聚合索引库中的酒店数据得来的。
示例：
# 多条件聚合
GET /hotel/_search
{
"query": {
"bool": {
"must": [
{
"match": {
"all": "酒店"
}
}
],
"should": [
{
"term": {
"brand": "皇冠假日"
}
},
{
"term": {
"brand": "华美达"
}
}
],
"must_not": [
{
"range": {
"price": {
"lte": 500
}
}
}
],
"filter": [
{
"range": {
"score": {
"gte": 45
}
}
}
],
"minimum_should_match": 1,
"boost": 1
}
},
"size": 0,
"aggs": {
"cityAgg": {
"terms": {
"field": "city",
"size": 10,
"order": {
"_count": "desc"
}
}
},
"starNameAgg": {
"terms": {
"field": "starName",
"size": 10,
"order": {
"_count": "desc"
}
}
},
"brandAgg": {
"terms": {
"field": "brand",
"size": 10,
"order": {
"scoreAgg.avg": "desc"
}
},
"aggs": {
"scoreAgg": {
"stats": {
"field": "score"
}
}
}
}
}
}运行结果如下：
{
"took" : 1,
"timed_out" : false,
"_shards" : {
"total" : 1,
"successful" : 1,
"skipped" : 0,
"failed" : 0
},
"hits" : {
"total" : {
"value" : 17,
"relation" : "eq"
},
"max_score" : null,
"hits" : [ ]
},
"aggregations" : {
"brandAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "皇冠假日",
"doc_count" : 12,
"scoreAgg" : {
"count" : 12,
"min" : 45.0,
"max" : 48.0,
"avg" : 46.416666666666664,
"sum" : 557.0
}
},
{
"key" : "华美达",
"doc_count" : 5,
"scoreAgg" : {
"count" : 5,
"min" : 45.0,
"max" : 46.0,
"avg" : 45.2,
"sum" : 226.0
}
}
]
},
"starNameAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "五钻",
"doc_count" : 7
},
{
"key" : "五星级",
"doc_count" : 5
},
{
"key" : "四星级",
"doc_count" : 3
},
{
"key" : "四钻",
"doc_count" : 2
}
]
},
"cityAgg" : {
"doc_count_error_upper_bound" : 0,
"sum_other_doc_count" : 0,
"buckets" : [
{
"key" : "上海",
"doc_count" : 10
},
{
"key" : "北京",
"doc_count" : 4
},
{
"key" : "深圳",
"doc_count" : 3
}
]
}
}
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