Tutorials 使用窗口功能分析信息

2024年4月13日 231次阅读来源: 眼泪成诗hocc

本教程中简要介绍了分析钻1.2,即ANSI兼容sql解析和窗口函数。钻支持以下SQL窗口函数:

PATRIRION BY和OVER条款
各种聚合窗口函数,Sum，Max，Min，Count，Avg
分析函数，如First_Value Last_Value、Lead、Lag，NTile，Row_Number和Rank

窗口功能多种多样。你可以减少连接、子查询和需要编写显式游标。窗口函数解决各种用例以最少的编码工作。

本教程建立在之前的教程，分析Yelp学术数据集和分析高度动态数据集，并使用相同的Yelp数据集。

开始

要开始,请下载 Yelp (业务审查)。
安装和启动钻。
在Drill中列出可用的模式列表。

SHOW schemas;
+---------------------+
|     SCHEMA_NAME     |
+---------------------+
| INFORMATION_SCHEMA  |
| cp.default          |
| dfs.default         |
| dfs.root            |
| dfs.tmp             |
| dfs.yelp            |
| sys                 |
+---------------------+

7 rows selected (1.755 seconds)

切换到使用Yelp的工作区数据加载。
USE dfs.yelp;
+——-+—————————————+
| ok | summary |
+——-+—————————————+
| true | Default schema changed to [dfs.yelp] |
+——-+—————————————+
1 row selected (0.129 seconds)
开始探索的一个数据集在Yelp数据集——业务信息。
SELECT * FROM business.json LIMIT 1;
+————————+—————————————————–+—————————————————————————————————————————————————————————————————————————————————–+——+——————————–+———+————–+——————-+————-+——-+——-+———–+—————————————————————————————————————————————————–+———-+—————+
| business_id | full_address | hours | open | categories | city | review_count | name | longitude | state | stars | latitude | attributes | type | neighborhoods |
+————————+————–+——+——————————-+—————————————————————————————————————————————————————————————————————————————————–+——+——————————–+———+————–+——————-+————-+——-+——-+———–+—————————————————————————————————————————————————–+———-+—————+
| vcNAWiLM4dR7D2nwwJ7nCA | 4840 E Indian School Rd Ste 101 Phoenix, AZ 85018 | {“Tuesday”:{“close”:”17:00″,”open”:”08:00″},”Friday”:{“close”:”17:00″,”open”:”08:00″},”Monday”:{“close”:”17:00″,”open”:”08:00″},”Wednesday”:{“close”:”17:00″,”open”:”08:00″},”Thursday”:{“close”:”17:00″,”open”:”08:00″},”Sunday”:{},”Saturday”:{}} | true | [“Doctors”,”Health & Medical”] | Phoenix | 7 | Eric Goldberg, MD | -111.983758 | AZ | 3.5 | 33.499313 | {“By Appointment Only”:true,”Good Ambience”:{},”Parking”:{},”Music”:{},”Hair Types Specialized In”:{},”Payment Types”:{},”Dietary Restrictions”:{}} | business | [] |
+————-+————–+——-+——+————+——+————–+—————————————————————————————————————————————————————————————————————————————————–+——+——————————–+———+————–+——————-+————-+——-+——-+———–+—————————————————————————————————————————————————–+———-+—————+
1 row selected (0.514 seconds)

使用窗口功能简单的查询

得到最高Yelp企业基于评论在每个城市数量和业务的行号。
SELECT name, city, review_count, row_number()
OVER (PARTITION BY city ORDER BY review_count DESC) AS rownum
FROM business.json LIMIT 15;
+—————————————-+————+—————+———+
| name | city | review_count | rownum |
+—————————————-+————+—————+———+
| Cupz N’ Crepes | Ahwatukee | 124 | 1 |
| My Wine Cellar | Ahwatukee | 98 | 2 |
| Kathy’s Alterations | Ahwatukee | 12 | 3 |
| McDonald’s | Ahwatukee | 7 | 4 |
| U-Haul | Ahwatukee | 5 | 5 |
| Hi-Health | Ahwatukee | 4 | 6 |
| Healthy and Clean Living Environments | Ahwatukee | 4 | 7 |
| Active Kids Pediatrics | Ahwatukee | 4 | 8 |
| Roberto’s Authentic Mexican Food | Anthem | 117 | 1 |
| Q to U BBQ | Anthem | 74 | 2 |
| Outlets At Anthem | Anthem | 64 | 3 |
| Dara Thai | Anthem | 56 | 4 |
| Cafe Provence | Anthem | 53 | 5 |
| Shanghai Club | Anthem | 50 | 6 |
| Two Brothers Kitchen | Anthem | 43 | 7 |
+—————————————-+————+—————+———+
15 rows selected (0.67 seconds)
检查评论数量的每个业务相比评论的平均数量在所有业务。
SELECT name, city,review_count,
Avg(review_count) OVER (PARTITION BY City) AS city_reviews_avg
FROM business.json LIMIT 15;
+—————————————-+————+—————+———————+
| name | city | review_count | city_reviews_avg |
+—————————————-+————+—————+———————+
| Hi-Health | Ahwatukee | 4 | 32.25 |
| My Wine Cellar | Ahwatukee | 98 | 32.25 |
| U-Haul | Ahwatukee | 5 | 32.25 |
| Cupz N’ Crepes | Ahwatukee | 124 | 32.25 |
| McDonald’s | Ahwatukee | 7 | 32.25 |
| Kathy’s Alterations | Ahwatukee | 12 | 32.25 |
| Healthy and Clean Living Environments | Ahwatukee | 4 | 32.25 |
| Active Kids Pediatrics | Ahwatukee | 4 | 32.25 |
| Anthem Community Center | Anthem | 4 | 14.492063492063492 |
| Scrapbooks To Remember | Anthem | 4 | 14.492063492063492 |
| Hungry Howie’s Pizza | Anthem | 7 | 14.492063492063492 |
| Pinata Nueva | Anthem | 3 | 14.492063492063492 |
| Starbucks Coffee Company | Anthem | 13 | 14.492063492063492 |
| Pizza Hut | Anthem | 6 | 14.492063492063492 |
| Rays Pizza | Anthem | 19 | 14.492063492063492 |
+—————————————-+————+—————+———————+
15 rows selected (0.395 seconds)
看看为每个业务贡献的评论数量对所有企业在城市的总数。

SELECT name, city,review_count,
Sum(review_count) OVER (PARTITION BY City) AS city_reviews_sum
FROM business.jsonlimit 15;

name	city	review_count	city_reviews_sum
+—————————————-+————+—————+——————-+
Hi-Health	Ahwatukee	4	258
My Wine Cellar	Ahwatukee	98	258
U-Haul	Ahwatukee	5	258
Cupz N’ Crepes	Ahwatukee	124	258
McDonald’s	Ahwatukee	7	258
Kathy’s Alterations	Ahwatukee	12	258
Healthy and Clean Living Environments	Ahwatukee	4	258
Active Kids Pediatrics	Ahwatukee	4	258
Anthem Community Center	Anthem	4	913
Scrapbooks To Remember	Anthem	4	913
Hungry Howie’s Pizza	Anthem	7	913
Pinata Nueva	Anthem	3	913
Starbucks Coffee Company	Anthem	13	913
Pizza Hut	Anthem	6	913
Rays Pizza	Anthem	19	913

15 rows selected (0.543 seconds)

对复杂的查询使用窗口函数

排名前十的城市名单和他们的排名最高的企业数量的评论。使用钻窗口等功能等级,dense_rank这些查询。
WITH X
AS
(SELECT name, city, review_count,
RANK()
OVER (PARTITION BY city
ORDER BY review_count DESC) AS review_rank
FROM business.json)
SELECT X.name, X.city, X.review_count
FROM X
WHERE X.review_rank =1 ORDER BY review_count DESC LIMIT 10;
+——————————————-+————-+—————+
| name | city | review_count |
+——————————————-+————-+—————+
| Mon Ami Gabi | Las Vegas | 4084 |
| Studio B | Henderson | 1336 |
| Phoenix Sky Harbor International Airport | Phoenix | 1325 |
| Four Peaks Brewing Co | Tempe | 1110 |
| The Mission | Scottsdale | 783 |
| Joe’s Farm Grill | Gilbert | 770 |
| The Old Fashioned | Madison | 619 |
| Cornish Pasty Company | Mesa | 578 |
| SanTan Brewing Company | Chandler | 469 |
| Yard House | Glendale | 321 |
+——————————————-+————-+—————+
10 rows selected (0.49 seconds)
比较评论的数量为每个业务在城市顶部和底部评论数。
SELECT name, city, review_count,
FIRST_VALUE(review_count)
OVER(PARTITION BY city ORDER BY review_count DESC) AS top_review_count,
LAST_VALUE(review_count)
OVER(PARTITION BY city ORDER BY review_count DESC) AS bottom_review_count
FROM business.json limit 15;
+—————————————-+————+—————+——————-+———————-+
| name | city | review_count | top_review_count | bottom_review_count |
+—————————————-+————+—————+——————-+———————-+
| My Wine Cellar | Ahwatukee | 98 | 124 | 12 |
| McDonald’s | Ahwatukee | 7 | 124 | 12 |
| U-Haul | Ahwatukee | 5 | 124 | 12 |
| Hi-Health | Ahwatukee | 4 | 124 | 12 |
| Healthy and Clean Living Environments | Ahwatukee | 4 | 124 | 12 |
| Active Kids Pediatrics | Ahwatukee | 4 | 124 | 12 |
| Cupz N’ Crepes | Ahwatukee | 124 | 124 | 12 |
| Kathy’s Alterations | Ahwatukee | 12 | 124 | 12 |
| Q to U BBQ | Anthem | 74 | 117 | 117 |
| Dara Thai | Anthem | 56 | 117 | 117 |
| Cafe Provence | Anthem | 53 | 117 | 117 |
| Shanghai Club | Anthem | 50 | 117 | 117 |
| Two Brothers Kitchen | Anthem | 43 | 117 | 117 |
| The Tennessee Grill | Anthem | 32 | 117 | 117 |
| Dollyrockers Boutique and Salon | Anthem | 30 | 117 | 117 |
+—————————————-+————+—————+——————-+———————-+
15 rows selected (0.516 seconds)
比较的评论评论的数量之前和之后的业务。
SELECT city, review_count, name,
LAG(review_count, 1) OVER(PARTITION BY city ORDER BY review_count DESC)
AS preceding_count,
LEAD(review_count, 1) OVER(PARTITION BY city ORDER BY review_count DESC)
AS following_count
FROM business.json limit 15;
+————+—————+—————————————-+——————+——————+
| city | review_count | name | preceding_count | following_count |
+————+—————+—————————————-+——————+——————+
| Ahwatukee | 124 | Cupz N’ Crepes | null | 98 |
| Ahwatukee | 98 | My Wine Cellar | 124 | 12 |
| Ahwatukee | 12 | Kathy’s Alterations | 98 | 7 |
| Ahwatukee | 7 | McDonald’s | 12 | 5 |
| Ahwatukee | 5 | U-Haul | 7 | 4 |
| Ahwatukee | 4 | Hi-Health | 5 | 4 |
| Ahwatukee | 4 | Healthy and Clean Living Environments | 4 | 4 |
| Ahwatukee | 4 | Active Kids Pediatrics | 4 | null |
| Anthem | 117 | Roberto’s Authentic Mexican Food | null | 74 |
| Anthem | 74 | Q to U BBQ | 117 | 64 |
| Anthem | 64 | Outlets At Anthem | 74 | 56 |
| Anthem | 56 | Dara Thai | 64 | 53 |
| Anthem | 53 | Cafe Provence | 56 | 50 |
| Anthem | 50 | Shanghai Club | 53 | 43 |
| Anthem | 43 | Two Brothers Kitchen | 50 | 32 |
+————+—————+—————————————-+——————+——————+
15 rows selected (0.518 seconds)

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    原文作者：眼泪成诗hocc
    原文地址: https://segmentfault.com/a/1190000004322295
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