Elasticsearch 第六篇：聚合统计查询
2024-09-04 06:38:53
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前面一直没有记录 Elasticsearch 的聚合查询或者其它复杂的查询。本篇做一下笔记，为了方便测试，索引数据依然是第五篇生成的测试索引库 db_student_test ，别名是 student_test
第一部分 基本聚合
1、最大值 max、最小值 min、平均值 avg 、总和 sum
场景：查询语文、数学、英语 这三科的最大值、最小值、平均值
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"max_chinese" : { "max" : { "field" : "chinese" } },
"min_chinese" : { "min" : { "field" : "chinese" } },
"avg_chinese" : { "avg" : { "field" : "chinese" } },
"max_math": { "max" : { "field" : "math" } },
"min_math": { "min" : { "field" : "math" } },
"avg_math": { "avg" : { "field" : "math" } },
"max_english": { "max" : { "field" : "english" } },
"min_english": { "min" : { "field" : "english" } },
"avg_english": { "avg" : { "field" : "english" } }
}
}
查询结果是：
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"avg_english": {
"value": 57.78366490546798
},
"max_chinese": {
"value": 98
},
"min_chinese": {
"value": 25
},
"min_math": {
"value": 15
},
"max_english": {
"value": 98
},
"avg_chinese": {
"value": 59.353859695794505
},
"avg_math": {
"value": 56.92907568735187
},
"min_english": {
"value": 21
},
"max_math": {
"value": 99
}
}
}
也可以来查询语文科目分数总和，相当于 sql 的 sum 逻辑，虽然在这里并没有什么意义：
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"sum_chinese" : { "sum" : { "field" : "chinese" } }
}
}
2、求个数，相当于 sql 的 count 逻辑
场景：查询所有学生总数，这里随便 count 一个 字段就可以，例如数学这个字段
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"age_count": {
"value_count": {
"field": "math"
}
}
}
}
返回结果是：
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"age_count": {
"value": 50084828
}
}
}
课间总数是：50084828 跟第五篇我们生成的数据总量一致
3、distinct 聚合，相当于 sql 的 count ( distinct )
场景：统计语文成绩有多少种值
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"type_count" : {
"cardinality" : {
"field" : "chinese"
}
}
}
}
返回结果是：
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"type_count": {
"value": 74
}
}
}
从结果上看，只有74个不同的分数，与第五篇随机生成数据的规则匹配
4、统计聚合
场景：查询语文成绩 总个数、最大值、最小值、平均值、总和等
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"chinese_stats": {
"stats": {
"field": "chinese"
}
}
}
}
返回结果是：
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"chinese_stats": {
"count": 50084828,
"min": 25,
"max": 98,
"avg": 59.353859695794505,
"sum": 2972727854
}
}
}
5、加强版统计聚合，查询结果在上面的基础上，加上方差等统计学上的数据
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"chinese_stats": {
"extended_stats": {
"field": "chinese"
}
}
}
}
6、分位聚合统计
默认的分位是 1% 5% 25% 50% 75% 95% 99% 《= 的概念
分位数的概念：25% 的分位数是 54，意思是小于等于 54 的样本占据了总样本的 25% ，即是 54 这个数将最底层的1/4 的数据分割出来。
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"chinese_percents": {
"percentiles": {
"field": "chinese"
}
}
}
}
也可以自定义分位：
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"chinese_percents": {
"percentiles": {
"field": "chinese",
"percents" : [10,20,30,40,50,60,70,80,90]
}
}
}
}
7、范围聚合统计
场景：分别查询语文成绩小于40分、小于50分、小于60分的比例
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"gge_perc_rank": {
"percentile_ranks": {
"field": "chinese",
"values": [40,50,60]
}
}
}
}
以上是查询成绩小于40，小于50，小于60的占比，得到的数据是： 21.29% 36.09% 51.12% 可以看到这是一个接近等差的数列，可见测试数据的随机性还是很好的。
第二部分 其它聚合方式
1、Term 聚合
场景：想知道学生的语文成绩，在所有分数值上的个数
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"genres" : {
"terms" : {
"field" : "chinese"
}
}
}
}
这个查询会将字段Chinese进行聚合，例如87分聚合成一个组，88分聚合成一个组，等等；
但是这里默认是按组的大小排序，而且不会将所有的组都显示出来，数量太小的组可能被忽略，查询结果如下：
{
"took": 1,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"genres": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 42560269,
"buckets": [
{
"key": 61,
"doc_count": 752863
},
{
"key": 68,
"doc_count": 752835
},
{
"key": 55,
"doc_count": 752749
},
{
"key": 59,
"doc_count": 752444
},
{
"key": 76,
"doc_count": 752405
},
{
"key": 74,
"doc_count": 752309
},
{
"key": 56,
"doc_count": 752283
},
{
"key": 49,
"doc_count": 752273
},
{
"key": 52,
"doc_count": 752201
},
{
"key": 50,
"doc_count": 752197
}
]
}
}
}
如果想要自定义筛选条件，Term聚合还可以按照以下设定来查询：
post http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"genres" : {
"terms" : {
"field" : "chinese",
"size" : 100, // 可能有100个不用的分数，我们将全部都展示出来
"order" : { "_count" : "asc" }, // 按照组数由小到大排序
"min_doc_count": 752200 //过滤条件：组数最小值是752200
}
}
}
}
查询结果是：
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"genres": {
"doc_count_error_upper_bound": 0,
"sum_other_doc_count": 0,
"buckets": [
{
"key": 52,
"doc_count": 752201
},
{
"key": 49,
"doc_count": 752273
},
{
"key": 56,
"doc_count": 752283
},
{
"key": 74,
"doc_count": 752309
},
{
"key": 76,
"doc_count": 752405
},
{
"key": 59,
"doc_count": 752444
},
{
"key": 55,
"doc_count": 752749
},
{
"key": 68,
"doc_count": 752835
},
{
"key": 61,
"doc_count": 752863
}
]
}
}
}
2、Filter 聚合
Filter 聚合会先进行条件过滤，在进行聚合
场景：查询华南理工大学的学生的数学科目平均分（先筛选学校，再进行分数统计聚合）
{
"aggs" : {
"scut_math_avg" : {
"filter" : { "term": { "school": "华南理工大学" } },
"aggs" : {
"avg_price" : { "avg" : { "field" : "math" } }
}
}
}
}
查询结果是：
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"scut_math_avg": {
"doc_count": 1854993,
"avg_price": {
"value": 56.93080027795253
}
}
}
}
3、Filters 多重聚合
场景：查询各个学校，语文、数学、英语的平均分都是多少，可以采用多重聚合，速度可能有点慢，如下
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"messages" : {
"filters" : {
"filters" : {
"school_1" : { "term" : { "school" : "华南理工大学" }},
"school_2" : { "term" : { "school" : "中山大学" }},
"school_3" : { "match" : { "school" : "暨南大学" }}
}
},
"aggs" : {
"avg_chinese" : { "avg" : { "field" : "chinese" } },
"avg_math" : { "avg" : { "field" : "math" } }
}
}
}
}
于是得到结果：
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"messages": {
"buckets": {
"school_1": {
"doc_count": 1854993,
"avg_chinese": {
"value": 59.353236912484306
},
"avg_math": {
"value": 56.93080027795253
}
},
"school_2": {
"doc_count": 1855016,
"avg_chinese": {
"value": 59.349129064115886
},
"avg_math": {
"value": 56.93540918245449
}
},
"school_3": {
"doc_count": 44519876,
"avg_chinese": {
"value": 59.35397212247402
},
"avg_math": {
"value": 56.92948502372289
}
}
}
}
}
}
4、Range 范围聚合
场景：想要查询语文成绩各个分数段的人数，可以这样查询
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"chinese_ranges" : {
"range" : {
"field" : "chinese",
"ranges" : [
{ "to" : 60 },
{ "from" : 60, "to" : 75 },
{ "from" : 75, "to" : 85 },
{ "from" : 85 }
]
}
}
}
}
查询结果是：
{
"took": 0,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"chinese_ranges": {
"buckets": [
{
"key": "*-60.0",
"to": 60,
"doc_count": 25096839
},
{
"key": "60.0-75.0",
"from": 60,
"to": 75,
"doc_count": 11278543
},
{
"key": "75.0-85.0",
"from": 75,
"to": 85,
"doc_count": 7424634
},
{
"key": "85.0-*",
"from": 85,
"doc_count": 6284812
}
]
}
}
}
这个返回结果的组名分别是 *-60.0 60.0-75.0 75.0-85.0 85.0-*
如果我们不想要这样的组名，可以自定义组名，例如：
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"chinese_ranges" : {
"range" : {
"field" : "chinese",
"keyed" : true,
"ranges" : [
{ "key" : "不及格", "to" : 60 },
{ "key" : "及格", "from" : 60, "to" : 75 },
{ "key" : "良好", "from" : 75, "to" : 85 },
{ "key" : "优秀", "from" : 85 }
]
}
}
}
}
查询结果将会是：
{
"took": 1675,
"timed_out": false,
"_shards": {
"total": 1,
"successful": 1,
"skipped": 0,
"failed": 0
},
"hits": {
"total": {
"value": 10000,
"relation": "gte"
},
"max_score": null,
"hits": []
},
"aggregations": {
"chinese_ranges": {
"buckets": {
"不及格": {
"to": 60,
"doc_count": 25096839
},
"及格": {
"from": 60,
"to": 75,
"doc_count": 11278543
},
"良好": {
"from": 75,
"to": 85,
"doc_count": 7424634
},
"优秀": {
"from": 85,
"doc_count": 6284812
}
}
}
}
}
还有其它各种各样的、复杂的聚合查询，都是可以网上查资料，甚至还支持推荐系统的一些计算方法，例如矩阵的概念等等。
还可以参考 https://blog.csdn.net/alex_xfboy/article/details/86100037
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