SQL Server PIVOT : ms sql server Row Column Pivot 로우 컬럼변환 

 

Summary: in this tutorial, you will learn how to use the SQL Server PIVOT operator to convert rows to columns.

Setting up the goals

For the demonstration, we will use the production.products and production.categories tables from the sample database:

The following query finds the number of products for each product category:

 

SELECT category_name, COUNT(product_id) product_count FROM production.products p INNER JOIN production.categories c ON c.category_id = p.category_id GROUP BY category_name;

Here is the output:

Our goal is to turn the category names from the first column of the output into multiple columns and count the number of products for each category name as the following picture:

In addition, we can add the model year to group the category by model year as shown in the following output:

Introduction to SQL Server PIVOT operator

SQL Server PIVOT operator rotates a table-valued expression. It turns the unique values in one column into multiple columns in the output and performs aggregations on any remaining column values.

You follow these steps to make a query a pivot table:

  • First, select a base dataset for pivoting.
  • Second, create a temporary result by using a derived table or common table expression (CTE)
  • Third, apply the PIVOT operator.

Let’s apply these steps in the following example.

First, select category name and product id from the production.products and production.categories tables as the base data for pivoting:

 

SELECT category_name, product_id FROM production.products p INNER JOIN production.categories c ON c.category_id = p.category_id

Second, create a temporary result set using a derived table:

 

SELECT * FROM ( SELECT category_name, product_id FROM production.products p INNER JOIN production.categories c ON c.category_id = p.category_id ) t

Third, apply the PIVOT operator:

 

SELECT * FROM ( SELECT category_name, product_id FROM production.products p INNER JOIN production.categories c ON c.category_id = p.category_id ) t PIVOT( COUNT(product_id) FOR category_name IN ( [Children Bicycles], [Comfort Bicycles], [Cruisers Bicycles], [Cyclocross Bicycles], [Electric Bikes], [Mountain Bikes], [Road Bikes]) ) AS pivot_table;

This query generates the following output:

경축! 아무것도 안하여 에스천사게임즈가 새로운 모습으로 재오픈 하였습니다.
어린이용이며, 설치가 필요없는 브라우저 게임입니다.
https://s1004games.com

Now, any additional column which you add to the select list of the query that returns the base data will automatically form row groups in the pivot table. For example, you can add the model year column to the above query:

 

SELECT * FROM ( SELECT category_name, product_id, model_year FROM production.products p INNER JOIN production.categories c ON c.category_id = p.category_id ) t PIVOT( COUNT(product_id) FOR category_name IN ( [Children Bicycles], [Comfort Bicycles], [Cruisers Bicycles], [Cyclocross Bicycles], [Electric Bikes], [Mountain Bikes], [Road Bikes]) ) AS pivot_table;

Here is the output:

Generating column values

In the above query, you had to type each category name in the parentheses after the IN operator manually. To avoid this, you can use the QUOTENAME() function to generate the category name list and copy them over the query.

First, generate the category name list:

 

DECLARE @columns NVARCHAR(MAX) = ''; SELECT @columns += QUOTENAME(category_name) + ',' FROM production.categories ORDER BY category_name; SET @columns = LEFT(@columns, LEN(@columns) - 1); PRINT @columns;

The output will look like this:

[Children Bicycles],[Comfort Bicycles],[Cruisers Bicycles],[Cyclocross Bicycles],[Electric Bikes],[Mountain Bikes],[Road Bikes]

In this snippet:

  • The QUOTENAME() function wraps the category name by the square brackets e.g., [Children Bicycles]
  • The LEFT() function removes the last comma from the @columns string.

Second, copy the category name list from the output and paste it to the query.

Dynamic pivot tables

If you add a new category name to the production.categories table, you need to rewrite your query, which is not ideal. To avoid doing this, you can use dynamic SQL to make the pivot table dynamic.

In this query, instead of passing a fixed list of category names to the PIVOT operator, we construct the category name list and pass it to an SQL statement, and then execute this statement dynamically using the stored procedure sp_executesql.

 

DECLARE @columns NVARCHAR(MAX) = '', @sql NVARCHAR(MAX) = ''; -- select the category names SELECT @columns+=QUOTENAME(category_name) + ',' FROM production.categories ORDER BY category_name; -- remove the last comma SET @columns = LEFT(@columns, LEN(@columns) - 1); -- construct dynamic SQL SET @sql =' SELECT * FROM ( SELECT category_name, model_year, product_id FROM production.products p INNER JOIN production.categories c ON c.category_id = p.category_id ) t PIVOT( COUNT(product_id) FOR category_name IN ('+ @columns +') ) AS pivot_table;'; -- execute the dynamic SQL EXECUTE sp_executesql @sql;

In this tutorial, you have learned how to use the SQL Server PIVOT table to convert rows to columns.

 

[출처] https://www.sqlservertutorial.net/sql-server-basics/sql-server-pivot/

 

 

본 웹사이트는 광고를 포함하고 있습니다.
광고 클릭에서 발생하는 수익금은 모두 웹사이트 서버의 유지 및 관리, 그리고 기술 콘텐츠 향상을 위해 쓰여집니다.
번호 제목 글쓴이 날짜 조회 수
공지 오라클 기본 샘플 데이터베이스 졸리운_곰 2014.01.02 86959
공지 [SQL컨셉] 서적 "SQL컨셉"의 샘플 데이타 베이스 SAMPLE DATABASE of ORACLE 가을의 곰을... 2013.02.10 79228
공지 [G_SQL] Sample Database 가을의 곰을... 2012.05.20 95967
78 [Kafka] Kafka 한번 살펴보자... Quickstart file 졸리운_곰 2021.06.18 1315
77 Java Kafka Producer, Consumer 예제 구현 Java를 이용하여 Kafka Producer와 Kakfa Consumer를 구현해보자. file 졸리운_곰 2021.06.18 1172
76 Beginner’s Guide to Understand Kafka file 졸리운_곰 2021.06.18 1575
75 [Kafka] Kafka 설치/실행 및 테스트 file 졸리운_곰 2021.06.18 1067
74 [java] [kafka] [Kafka] 개념 및 기본예제 file 졸리운_곰 2021.06.16 2171
73 Getting started with Apache Kafka in Python file 졸리운_곰 2020.09.10 2262
72 [Kafka] 다운로드 및 Quick Start file 졸리운_곰 2020.09.07 1900
71 [Kafka] 기본 개념잡기 file 졸리운_곰 2020.09.07 1720
70 Flume Integration with Kafka file 졸리운_곰 2019.04.16 2086
69 빅데이터: 플럼(Flume) 토폴로지 설계 file 졸리운_곰 2019.04.16 1604
68 실시간 처리를 위한 분산 메시징 시스템 카프카(Kafka) file 졸리운_곰 2018.05.12 1413
67 Flume과 Kafka를 사용한 초당 100만개 로그 수집 테스트 file 졸리운_곰 2018.05.12 1402
66 웹 크롤링 / web crwaling / web scraping / 웹 스크래핑 file 졸리운_곰 2017.07.09 1849
65 빅데이터 단지 몇퍼센트의 예측 정확성을 위하여 장애로 가득찬 빅데이터 시스템을 도입하여야 하는가에 대한 의문! file 졸리운_곰 2017.03.20 1611
64 빅데이터: 플럼(Flume) 토폴로지 설계 file 졸리운_곰 2017.03.20 1422
63 [실시간 분석 시스템] Apache Flume를 활용한 데이터 수집(1) file 졸리운_곰 2017.03.06 1324
62 [실시간 분석 시스템] 데이터 수집 #2 Apache Sqoop을 활용하여 RDBMS 데이터 수집(2) file 졸리운_곰 2017.03.06 1388
61 [실시간 분석 시스템] 데이터 수집 #2 Apache Sqoop을 활용하여 RDBMS 데이터 수집(1) file 졸리운_곰 2017.03.06 1109
60 [실시간 분석 시스템] 데이터 수집 #1 오픈 소스 수집기 비교 file 졸리운_곰 2017.03.06 1752
59 [실시간 분석 시스템] 일단 데이터 들여다 보기 file 졸리운_곰 2017.03.06 1948
대표 김성준 주소 : 경기 용인 분당수지 U타워 등록번호 : 142-07-27414
통신판매업 신고 : 제2012-용인수지-0185호 출판업 신고 : 수지구청 제 123호 개인정보보호최고책임자 : 김성준 sjkim70@stechstar.com
대표전화 : 010-4589-2193 [fax] 02-6280-1294 COPYRIGHT(C) stechstar.com ALL RIGHTS RESERVED