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Elasticsearch 第六篇：聚合統計查詢
2020-11-06 20:10:00 【itread01】
前面一直沒有記錄 Elasticsearch 的聚合查詢或者其它複雜的查詢。本篇做一下筆記，為了方便測試，索引資料依然是第五篇生成的測試索引庫 db_student_test ，別名是 student_test
第一部分 基本聚合
1、最大值 max、最小值 min、平均值 avg 、總和 sum
場景：查詢語文、數學、英語 這三科的最大值、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" } }
}
}
查詢結果是：
{
"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
}
}
}
也可以來查詢語文科目分數總和，相當於 sql 的 sum 邏輯，雖然在這裡並沒有什麼意義：
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"sum_chinese" : { "sum" : { "field" : "chinese" } }
}
}
2、求個數，相當於 sql 的 count 邏輯
場景：查詢所有學生總數，這裡隨便 count 一個 欄位就可以，例如數學這個欄位
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"age_count": {
"value_count": {
"field": "math"
}
}
}
}
返回結果是：
{
"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
}
}
}
課間總數是：50084828 跟第五篇我們生成的資料總量一致
3、distinct 聚合，相當於 sql 的 count ( distinct )
場景：統計語文成績有多少種值
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"type_count" : {
"cardinality" : {
"field" : "chinese"
}
}
}
}
返回結果是：
{
"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
}
}
}
從結果上看，只有74個不同的分數，與第五篇隨機生成資料的規則匹配
4、統計聚合
場景：查詢語文成績 總個數、最大值、最小值、平均值、總和等
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"chinese_stats": {
"stats": {
"field": "chinese"
}
}
}
}
返回結果是：
{
"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、加強版統計聚合，查詢結果在上面的基礎上，加上方差等統計學上的資料
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"chinese_stats": {
"extended_stats": {
"field": "chinese"
}
}
}
}
6、分位聚合統計
預設的分位是 1% 5% 25% 50% 75% 95% 99% 《= 的概念
分位數的概念：25% 的分位數是 54，意思是小於等於 54 的樣本佔據了總樣本的 25% ，即是 54 這個數將最底層的1/4 的資料分割出來。
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs": {
"chinese_percents": {
"percentiles": {
"field": "chinese"
}
}
}
}
也可以自定義分位：
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、範圍聚合統計
場景：分別查詢語文成績小於40分、小於50分、小於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]
}
}
}
}
以上是查詢成績小於40，小於50，小於60的佔比，得到的資料是： 21.29% 36.09% 51.12% 可以看到這是一個接近等差的數列，可見測試資料的隨機性還是很好的。
第二部分 其它聚合方式
1、Term 聚合
場景：想知道學生的語文成績，在所有分數值上的個數
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"genres" : {
"terms" : {
"field" : "chinese"
}
}
}
}
這個查詢會將欄位Chinese進行聚合，例如87分聚合成一個組，88分聚合成一個組，等等；
但是這裡預設是按組的大小排序，而且不會將所有的組都顯示出來，數量太小的組可能被忽略，查詢結果如下：
{
"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
}
]
}
}
}
如果想要自定義篩選條件，Term聚合還可以按照以下設定來查詢：
post http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"genres" : {
"terms" : {
"field" : "chinese",
"size" : 100, // 可能有100個不用的分數，我們將全部都展示出來
"order" : { "_count" : "asc" }, // 按照組數由小到大排序
"min_doc_count": 752200 //過濾條件：組數最小值是752200
}
}
}
}
查詢結果是：
{
"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 聚合會先進行條件過濾，在進行聚合
場景：查詢華南理工大學的學生的數學科目平均分（先篩選學校，再進行分數統計聚合）
{
"aggs" : {
"scut_math_avg" : {
"filter" : { "term": { "school": "華南理工大學" } },
"aggs" : {
"avg_price" : { "avg" : { "field" : "math" } }
}
}
}
}
查詢結果是：
{
"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
場景：查詢各個學校，語文、數學、英語的平均分都是多少，可以採用多重聚合，速度可能有點慢，如下
POST http://localhost:9200/student_test1/_search?size=0
{
"aggs" : {
"messages" : {
"filters" : {
"filters" : {
"school_1" : { "term" : { "school" : "華南理工大學" }},
"school_2" : { "term" : { "school" : "中山大學" }},
"school_3" : { "match" : { "school" : "暨南大學" }}
}
},
"aggs" : {
"avg_chinese" : { "avg" : { "field" : "chinese" } },
"avg_math" : { "avg" : { "field" : "math" } }
}
}
}
}
於是得到結果：
{
"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 範圍聚合
場景：想要查詢語文成績各個分數段的人數，可以這樣查詢
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 } ] } } } }
查詢結果是：
{
"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
}
]
}
}
}
這個返回結果的組名分別是 *-60.0 60.0-75.0 75.0-85.0 85.0-*
如果我們不想要這樣的組名，可以自定義組名，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" : "優秀", "from" : 85 }
]
}
}
}
}
查詢結果將會是：
{
"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
},
"優秀": {
"from": 85,
"doc_count": 6284812
}
}
}
}
}
還有其它各種各樣的、複雜的聚合查詢，都是可以網上查資料，甚至還支援推薦系統的一些計算方法，例如矩陣的概念等等。
還可以參考 https://blog.csdn.net/alex_xfboy/article/details/8610
版权声明
本文为[itread01]所创，转载请带上原文链接，感谢
https://www.itread01.com/content/1604650142.html
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