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/86100037