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

1. 要开始,请下载 Yelp (业务审查)。

2. 安装和启动钻。

3. 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)

1. 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)

2. 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

1. 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)

2. 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)

3. 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;

15 rows selected (0.543 seconds)

对复杂的查询使用窗口函数

1. 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)

2. 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)

3. 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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