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ElasticSearch聚合语法学习（bucket，metric，hitogram，date hitogram）

發布時間：2024/4/11 编程问答 27 豆豆

生活随笔收集整理的這篇文章主要介紹了 ElasticSearch聚合语法学习（bucket，metric，hitogram，date hitogram）小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

ElasticSearch聚合語法學習

PUT /tvs {"mappings": {"sales": {"properties": {"price": {"type": "long"},"color": {"type": "keyword"},"brand": {"type": "keyword"},"sold_date": {"type": "date"}}}} } POST /tvs/sales/_bulk { "index": {}} { "price" : 1000, "color" : "紅色", "brand" : "長虹", "sold_date" : "2016-10-28" } { "index": {}} { "price" : 2000, "color" : "紅色", "brand" : "長虹", "sold_date" : "2016-11-05" } { "index": {}} { "price" : 3000, "color" : "綠色", "brand" : "小米", "sold_date" : "2016-05-18" } { "index": {}} { "price" : 1500, "color" : "藍色", "brand" : "TCL", "sold_date" : "2016-07-02" } { "index": {}} { "price" : 1200, "color" : "綠色", "brand" : "TCL", "sold_date" : "2016-08-19" } { "index": {}} { "price" : 2000, "color" : "紅色", "brand" : "長虹", "sold_date" : "2016-11-05" } { "index": {}} { "price" : 8000, "color" : "紅色", "brand" : "三星", "sold_date" : "2017-01-01" } { "index": {}} { "price" : 2500, "color" : "藍色", "brand" : "小米", "sold_date" : "2017-02-12" }

3. 統計哪種顏色電視銷量最高

GET /tvs/sales/_search {"size" : 0,"aggs" : { "popular_colors" : { "terms" : { "field" : "color"}}} }

size：只獲取聚合結果，而不要執行聚合的原始數據

aggs：固定語法，要對一份數據執行分組聚合操作

popular_colors：就是對每個aggs，都要起一個名字，這個名字是隨機的，你隨便取什么都ok

terms：根據字段的值進行分組

field：根據指定的字段的值進行分組

相當于：select color，count(*) from tbl group by color;

4. 統計每種顏色電視平均價格

GET /tvs/sales/_search {"size" : 0,"aggs": {"colors": {"terms": {"field": "color"},"aggs": { "avg_price": { "avg": {"field": "price" }}}}} }

按照color去分bucket，可以拿到每個color bucket中的數量，這個僅僅只是一個bucket操作，doc_count其實只是es的bucket操作默認執行的一個內置metric

除了bucket操作，分組，還要對每個bucket執行一個metric聚合統計操作

在一個aggs執行的bucket操作（terms），平級的json結構下，再加一個aggs，這個第二個aggs內部，同樣取個名字，執行一個metric操作，avg，對之前的每個bucket中的數據的指定的field，price field，求一個平均值

"aggs": { "avg_price": { "avg": {"field": "price" }} }

metric，就是一個對一個bucket分組操作之后，對每個bucket都要執行的一個metric

第一個metric，avg，求指定字段的平均值

5. bucket嵌套實現顏色+品牌的多層下鉆

從顏色到品牌進行下鉆分析，每種顏色的平均價格，以及找到每種顏色每個品牌的平均價格

我們可以進行多層次的下鉆

比如說，現在紅色的電視有4臺，同時這4臺電視中，有3臺是屬于長虹的，1臺是屬于小米的

紅色電視中的3臺長虹的平均價格是多少？

紅色電視中的1臺小米的平均價格是多少？

下鉆的意思是，已經分了一個組了，比如說顏色的分組，然后還要繼續對這個分組內的數據，再分組，比如一個顏色內，還可以分成多個不同的品牌的組，最后對每個最小粒度的分組執行聚合分析操作，這就叫做下鉆分析

es，下鉆分析，就要對bucket進行多層嵌套，多次分組

按照多個維度（顏色+品牌）多層下鉆分析，而且學會了每個下鉆維度（顏色，顏色+品牌），都可以對每個維度分別執行一次metric聚合操作

GET /tvs/sales/_search {"size": 0,"aggs": {"group_by_color": {"terms": {"field": "color"},"aggs": {"color_avg_price": {"avg": {"field": "price"}},"group_by_brand": {"terms": {"field": "brand"},"aggs": {"brand_avg_price": {"avg": {"field": "price"}}}}}}} } {"took": 8,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 8,"max_score": 0,"hits": []},"aggregations": {"group_by_color": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "紅色","doc_count": 4,"color_avg_price": {"value": 3250},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "長虹","doc_count": 3,"brand_avg_price": {"value": 1666.6666666666667}},{"key": "三星","doc_count": 1,"brand_avg_price": {"value": 8000}}]}},{"key": "綠色","doc_count": 2,"color_avg_price": {"value": 2100},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "TCL","doc_count": 1,"brand_avg_price": {"value": 1200}},{"key": "小米","doc_count": 1,"brand_avg_price": {"value": 3000}}]}},{"key": "藍色","doc_count": 2,"color_avg_price": {"value": 2000},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "TCL","doc_count": 1,"brand_avg_price": {"value": 1500}},{"key": "小米","doc_count": 1,"brand_avg_price": {"value": 2500}}]}}]}} }

6. 統計每種顏色電視最大最小，總和價格

count：bucket，terms，自動就會有一個doc_count，就相當于是count

avg：avg aggs，求平均值

max：求一個bucket內，指定field值最大的那個數據

min：求一個bucket內，指定field值最小的那個數據

sum：求一個bucket內，指定field值的總和

一般來說，90%的常見的數據分析的操作，metric，無非就是count，avg，max，min，sum

GET /tvs/sales/_search {"size" : 0,"aggs": {"colors": {"terms": {"field": "color"},"aggs": {"avg_price": { "avg": { "field": "price" } },"min_price" : { "min": { "field": "price"} }, "max_price" : { "max": { "field": "price"} },"sum_price" : { "sum": { "field": "price" } } }}} } {"took": 16,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 8,"max_score": 0,"hits": []},"aggregations": {"group_by_color": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "紅色","doc_count": 4,"max_price": {"value": 8000},"min_price": {"value": 1000},"avg_price": {"value": 3250},"sum_price": {"value": 13000}},{"key": "綠色","doc_count": 2,"max_price": {"value": 3000},"min_price": {"value":}, 1200"avg_price": {"value": 2100},"sum_price": {"value": 4200}},{"key": "藍色","doc_count": 2,"max_price": {"value": 2500},"min_price": {"value": 1500},"avg_price": {"value": 2000},"sum_price": {"value": 4000}}]}} }

7. hitogram按價格區間統計電視銷量和銷售額

histogram：類似于terms，也是進行bucket分組操作，接收一個field，按照這個field的值的各個范圍區間，進行bucket分組操作

"histogram":{ "field": "price","interval": 2000 }

interval：2000，劃分范圍，0-2000，2000-4000，4000-6000，6000-8000，8000-10000，buckets去根據price的值，比如2500，看落在哪個區間內，比如2000-4000，此時就會將這條數據放入2000-4000對應的那個bucket中

bucket劃分的方法，terms，將field值相同的數據劃分到一個bucket中

bucket有了之后，一樣的，去對每個bucket執行avg，count，sum，max，min，等各種metric操作，聚合分析

GET /tvs/sales/_search {"size" : 0,"aggs":{"price":{"histogram":{ "field": "price","interval": 2000},"aggs":{"revenue": {"sum": { "field" : "price"}}}}} } {"took": 13,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 8,"max_score": 0,"hits": []},"aggregations": {"group_by_price": {"buckets": [{"key": 0,"doc_count": 3,"sum_price": {"value": 3700}},{"key": 2000,"doc_count": 4,"sum_price": {"value": 9500}},{"key": 4000,"doc_count": 0,"sum_price": {"value": 0}},{"key": 6000,"doc_count: {"value":": 0,"sum_price" 0}},{"key": 8000,"doc_count": 1,"sum_price": {"value": 8000}}]}} }

8. date hitogram之統計每月電視銷量

date histogram，按照我們指定的某個date類型的日期field，以及日期interval，按照一定的日期間隔，去劃分bucket

date interval = 1m，

2017-01-01~2017-01-31，就是一個bucket

2017-02-01~2017-02-28，就是一個bucket

然后會去掃描每個數據的date field，判斷date落在哪個bucket中，就將其放入那個bucket

2017-01-05，就將其放入2017-01-01~2017-01-31，就是一個bucket

min_doc_count：即使某個日期interval，2017-01-01~2017-01-31中，一條數據都沒有，那么這個區間也是要返回的，不然默認是會過濾掉這個區間的

extended_bounds，min，max：劃分bucket的時候，會限定在這個起始日期，和截止日期內

GET /tvs/sales/_search {"size" : 0,"aggs": {"sales": {"date_histogram": {"field": "sold_date","interval": "month", "format": "yyyy-MM-dd","min_doc_count" : 0, "extended_bounds" : { "min" : "2016-01-01","max" : "2017-12-31"}}}} } {"took": 16,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 8,"max_score": 0,"hits": []},"aggregations": {"group_by_sold_date": {"buckets": [{"key_as_string": "2016-01-01","key": 1451606400000,"doc_count": 0},{"key_as_string": "2016-02-01","key": 1454284800000,"doc_count": 0},{"key_as_string": "2016-03-01","key": 1456790400000,"doc_count": 0},{"key_as_string": "2016-04-01","key": 1459468800000,"doc_count": 0},{"key_as_string": "2016-05-01","key": 1462060800000,"doc_count": 1},{"key_as_string": "2016-06-01","key": 1464739200000,"doc_count": 0},{"key_as_string": "2016-07-01","key": 1467331200000,"doc_count": 1},{"key_as_strin"key_as_string": "2016-09-01","key": 1472688000000,"doc_count": 0},g": "2016-08-01","key": 1470009600000,"doc_count": 1},{{"key_as_string": "2016-10-01","key": 1475280000000,"doc_count": 1},{"key_as_string": "2016-11-01","key": 1477958400000,"doc_count": 2},{"key_as_string": "2016-12-01","key": 1480550400000,"doc_count": 0},{"key_as_string": "2017-01-01","key": 1483228800000,"doc_count": 1},{"key_as_string": "2017-02-01","key": 1485907200000,"doc_count": 1}]}} }

9. 下鉆分析之統計每季度每個品牌的銷售額

GET /tvs/sales/_search {"size": 0,"aggs": {"group_by_sold_date": {"date_histogram": {"field": "sold_date","interval": "quarter","format": "yyyy-MM-dd","min_doc_count": 0,"extended_bounds": {"min": "2016-01-01","max": "2017-12-31"}},"aggs": {"group_by_brand": {"terms": {"field": "brand"},"aggs": {"sum_price": {"sum": {"field": "price"}}}},"total_sum_price": {"sum": {"field": "price"}}}}} } {"took": 10,"timed_out": false,"_shards": {"total": 5,"successful": 5,"failed": 0},"hits": {"total": 8,"max_score": 0,"hits": []},"aggregations": {"group_by_sold_date": {"buckets": [{"key_as_string": "2016-01-01","key": 1451606400000,"doc_count": 0,"total_sum_price": {"value": 0},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": []}},{"key_as_string": "2016-04-01","key": 1459468800000,"doc_count": 1,"total_sum_price": {"value": 3000},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "小米","doc_count": 1,"sum_price": {"value": 3000}}]}},{"key_as_string": "2016-07-01","key": 1467331200000,"doc_count": 2,"total_sum_price": {"value": 2700},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "TCL","doc_count": 2,"sum_price": {"value": 2700}}]}},{"key_as_string": "2016-10-01","key": 1475280000000,"doc_count": 3,"total_sum_price": {"value": 5000},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "長虹","doc_count": 3,"sum_price": {"value": 5000}}]}},{"key_as_string": "2017-01-01","key": 1483228800000,"doc_count": 2,"total_sum_price": {"value": 10500},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": [{"key": "三星","doc_count": 1,"sum_price": {"value": 8000}},{"key": "小米","doc_count": 1,"sum_price": {"value": 2500}}]}},{"key_as_string": "2017-04-01","key": 1491004800000,"doc_count": 0,"total_sum_price": {"value": 0},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": []}},{"key_as_string": "2017-07-01","key": 1498867200000,"doc_count": 0,"total_sum_price": {"value": 0},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": []}},{"key_as_string": "2017-10-01","key": 1506816000000,"doc_count": 0,"total_sum_price": {"value": 0},"group_by_brand": {"doc_count_error_upper_bound": 0,"sum_other_doc_count": 0,"buckets": []}}]}} }

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