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SQL PARTITION BY Clause overview
We can use the SQL PARTITION BY clause with the OVER clause to specify the column on which we need to perform aggregation. In the previous …
Source: www.sqlshack.com
Date Published: 7/1/2021
View: 2091
Partition SQL là gì? Cách sử dụng Partition SQL ? » – Inda
Partition SQL theo đúng như tên của nó là việc phân chia một table thành những phần nhỏ theo một logic nhất định, được phân biệt bằng key, …
Source: inda.vn
Date Published: 9/13/2022
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SQL PARTITION BY Clause – Learn How To Use PARTITION …
The PARTITION BY clause is a subclause of the OVER clause. The PARTITION BY clause dives a query’s result set into partitions. The window function is operated …
Source: www.sqltutorial.org
Date Published: 8/18/2021
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When and how to use the SQL PARTITION BY clause
Understanding the Window function · Partition By: This dives the rows or query result set into small partitions. · Order By: This arranges the …
Source: blog.quest.com
Date Published: 2/8/2022
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How to Use the SQL PARTITION BY With OVER | LearnSQL.com
The SQL PARTITION BY expression is a subclause of the OVER clause, which is used in almost all invocations of window functions like AVG() …
Source: learnsql.com
Date Published: 7/6/2022
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SQL PARTITION BY Clause overview
This article will cover the SQL PARTITION BY clause and, in particular, the difference with GROUP BY in a select statement. We will also explore various use cases of SQL PARTITION BY.
We use SQL PARTITION BY to divide the result set into partitions and perform computation on each subset of partitioned data.
Preparing Sample Data
Let us create an Orders table in my sample database SQLShackDemo and insert records to write further queries.
1 2 3 4 5 6 7 8 9 10 Use SQLShackDemo Go CREATE TABLE [ dbo ] . [ Orders ] ( [ orderid ] INT , [ Orderdate ] DATE , [ CustomerName ] VARCHAR ( 100 ) , [ Customercity ] VARCHAR ( 100 ) , [ Orderamount ] MONEY )
I use ApexSQL Generate to insert sample data into this article. Right click on the Orders table and Generate test data.
It launches the ApexSQL Generate. I generated a script to insert data into the Orders table. Execute this script to insert 100 records in the Orders table.
1 2 3 4 5 6 7 USE [ SQLShackDemo ] GO INSERT [ dbo ] . [ Orders ] VALUES ( 216090 , CAST ( N ‘1826-12-19’ AS Date ) , N ‘Edward’ , N ‘Phoenix’ , 4713.8900 ) GO INSERT [ dbo ] . [ Orders ] VALUES ( 508220 , CAST ( N ‘1826-12-09’ AS Date ) , N ‘Aria’ , N ‘San Francisco’ , 9832.7200 ) GO …
Once we execute insert statements, we can see the data in the Orders table in the following image.
We use SQL GROUP BY clause to group results by specified column and use aggregate functions such as Avg(), Min(), Max() to calculate required values.
Group By function syntax
1 2 3 4 SELECT expression , aggregate function ( ) FROM tables WHERE conditions GROUP BY expression
Suppose we want to find the following values in the Orders table
Minimum order value in a city
Maximum order value in a city
Average order value in a city
Execute the following query with GROUP BY clause to calculate these values.
1 2 3 4 5 6 SELECT Customercity , AVG ( Orderamount ) AS AvgOrderAmount , MIN ( OrderAmount ) AS MinOrderAmount , SUM ( Orderamount ) TotalOrderAmount FROM [ dbo ] . [ Orders ] GROUP BY Customercity ;
In the following screenshot, we can see Average, Minimum and maximum values grouped by CustomerCity.
Now, we want to add CustomerName and OrderAmount column as well in the output. Let’s add these columns in the select statement and execute the following code.
1 2 3 4 5 6 SELECT Customercity , CustomerName , OrderAmount , AVG ( Orderamount ) AS AvgOrderAmount , MIN ( OrderAmount ) AS MinOrderAmount , SUM ( Orderamount ) TotalOrderAmount FROM [ dbo ] . [ Orders ] GROUP BY Customercity ;
Once we execute this query, we get an error message. In the SQL GROUP BY clause, we can use a column in the select statement if it is used in Group by clause as well. It does not allow any column in the select clause that is not part of GROUP BY clause.
We can use the SQL PARTITION BY clause to resolve this issue. Let us explore it further in the next section.
SQL PARTITION BY
We can use the SQL PARTITION BY clause with the OVER clause to specify the column on which we need to perform aggregation. In the previous example, we used Group By with CustomerCity column and calculated average, minimum and maximum values.
Let us rerun this scenario with the SQL PARTITION BY clause using the following query.
1 2 3 4 5 SELECT Customercity , AVG ( Orderamount ) OVER ( PARTITION BY Customercity ) AS AvgOrderAmount , MIN ( OrderAmount ) OVER ( PARTITION BY Customercity ) AS MinOrderAmount , SUM ( Orderamount ) OVER ( PARTITION BY Customercity ) TotalOrderAmount FROM [ dbo ] . [ Orders ] ;
In the output, we get aggregated values similar to a GROUP By clause. You might notice a difference in output of the SQL PARTITION BY and GROUP BY clause output.
Group By SQL PARTITION BY We get a limited number of records using the Group By clause We get all records in a table using the PARTITION BY clause. It gives one row per group in result set. For example, we get a result for each group of CustomerCity in the GROUP BY clause. It gives aggregated columns with each record in the specified table. We have 15 records in the Orders table. In the query output of SQL PARTITION BY, we also get 15 rows along with Min, Max and average values.
In the previous example, we get an error message if we try to add a column that is not a part of the GROUP BY clause.
We can add required columns in a select statement with the SQL PARTITION BY clause. Let us add CustomerName and OrderAmount columns and execute the following query.
1 2 3 4 5 6 7 SELECT Customercity , CustomerName , OrderAmount , AVG ( Orderamount ) OVER ( PARTITION BY Customercity ) AS AvgOrderAmount , MIN ( OrderAmount ) OVER ( PARTITION BY Customercity ) AS MinOrderAmount , SUM ( Orderamount ) OVER ( PARTITION BY Customercity ) TotalOrderAmount FROM [ dbo ] . [ Orders ] ;
We get CustomerName and OrderAmount column along with the output of the aggregated function. We also get all rows available in the Orders table.
In the following screenshot, you can for CustomerCity Chicago, it performs aggregations (Avg, Min and Max) and gives values in respective columns.
Similarly, we can use other aggregate functions such as count to find out total no of orders in a particular city with the SQL PARTITION BY clause.
1 2 3 4 5 6 7 8 SELECT Customercity , CustomerName , OrderAmount , COUNT ( OrderID ) OVER ( PARTITION BY Customercity ) AS CountOfOrders , AVG ( Orderamount ) OVER ( PARTITION BY Customercity ) AS AvgOrderAmount , MIN ( OrderAmount ) OVER ( PARTITION BY Customercity ) AS MinOrderAmount , SUM ( Orderamount ) OVER ( PARTITION BY Customercity ) TotalOrderAmount FROM [ dbo ] . [ Orders ] ;
We can see order counts for a particular city. For example, we have two orders from Austin city therefore; it shows value 2 in CountofOrders column.
PARTITION BY clause with ROW_NUMBER()
We can use the SQL PARTITION BY clause with ROW_NUMBER() function to have a row number of each row. We define the following parameters to use ROW_NUMBER with the SQL PARTITION BY clause.
PARTITION BY column – In this example, we want to partition data on CustomerCity column
– In this example, we want to partition data on column Order By: In the ORDER BY column, we define a column or condition that defines row number. In this example, we want to sort data on the OrderAmount column
1 2 3 4 5 6 7 8 9 10 SELECT Customercity , CustomerName , ROW_NUMBER ( ) OVER ( PARTITION BY Customercity ORDER BY OrderAmount DESC ) AS ” Row Number ” , OrderAmount , COUNT ( OrderID ) OVER ( PARTITION BY Customercity ) AS CountOfOrders , AVG ( Orderamount ) OVER ( PARTITION BY Customercity ) AS AvgOrderAmount , MIN ( OrderAmount ) OVER ( PARTITION BY Customercity ) AS MinOrderAmount , SUM ( Orderamount ) OVER ( PARTITION BY Customercity ) TotalOrderAmount FROM [ dbo ] . [ Orders ] ;
In the following screenshot, we get see for CustomerCity Chicago, we have Row number 1 for order with highest amount 7577.90. it provides row number with descending OrderAmount.
PARTITION BY clause with Cumulative total value
Suppose we want to get a cumulative total for the orders in a partition. Cumulative total should be of the current row and the following row in the partition.
For example, in the Chicago city, we have four orders.
CustomerCity CustomerName Rank OrderAmount Cumulative Total Rows Cumulative Total Chicago Marvin 1 7577.9 Rank 1 +2 14777.51 Chicago Lawrence 2 7199.61 Rank 2+3 14047.21 Chicago Alex 3 6847.66 Rank 3+4 8691.49 Chicago Jerome 4 1843.83 Rank 4 1843.83
In the following query, we the specified ROWS clause to select the current row (using CURRENT ROW) and next row (using 1 FOLLOWING). It further calculates sum on those rows using sum(Orderamount) with a partition on CustomerCity ( using OVER(PARTITION BY Customercity ORDER BY OrderAmount DESC).
1 2 3 4 5 6 7 SELECT Customercity , CustomerName , OrderAmount , ROW_NUMBER ( ) OVER ( PARTITION BY Customercity ORDER BY OrderAmount DESC ) AS ” Row Number ” , CONVERT ( VARCHAR ( 20 ) , SUM ( orderamount ) OVER ( PARTITION BY Customercity ORDER BY OrderAmount DESC ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING ) , 1 ) AS CumulativeTotal ,
Similarly, we can calculate the cumulative average using the following query with the SQL PARTITION BY clause.
1 2 3 4 5 6 7 SELECT Customercity , CustomerName , OrderAmount , ROW_NUMBER ( ) OVER ( PARTITION BY Customercity ORDER BY OrderAmount DESC ) AS ” Row Number ” , CONVERT ( VARCHAR ( 20 ) , AVG ( orderamount ) OVER ( PARTITION BY Customercity ORDER BY OrderAmount DESC ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING ) , 1 ) AS CumulativeAVG
ROWS UNBOUNDED PRECEDING with the PARTITION BY clause
We can use ROWS UNBOUNDED PRECEDING with the SQL PARTITION BY clause to select a row in a partition before the current row and the highest value row after current row.
In the following table, we can see for row 1; it does not have any row with a high value in this partition. Therefore, Cumulative average value is the same as of row 1 OrderAmount.
For Row2, It looks for current row value (7199.61) and highest value row 1(7577.9). It calculates the average for these two amounts.
For Row 3, it looks for current value (6847.66) and higher amount value than this value that is 7199.61 and 7577.90. It calculates the average of these and returns.
CustomerCity CustomerName Rank OrderAmount Cumulative Average Rows Cumulative Average Chicago Marvin 1 7577.9 Rank 1 7577.90 Chicago Lawrence 2 7199.61 Rank 1+2 7388.76 Chicago Alex 3 6847.66 Rank 1+2+3 7208.39 Chicago Jerome 4 1843.83 Rank 1+2+3+4 5867.25
Execute the following query to get this result with our sample data.
1 2 3 4 5 6 7 8 SELECT Customercity , CustomerName , OrderAmount , ROW_NUMBER ( ) OVER ( PARTITION BY Customercity ORDER BY OrderAmount DESC ) AS ” Row Number ” , CONVERT ( VARCHAR ( 20 ) , AVG ( orderamount ) OVER ( PARTITION BY Customercity ORDER BY OrderAmount DESC ROWS UNBOUNDED PRECEDING ) , 1 ) AS CumulativeAvg FROM [ dbo ] . [ Orders ] ;
Conclusion
In this article, we explored the SQL PARTIION BY clause and its comparison with GROUP BY clause. We also learned its usage with a few examples. I hope you find this article useful and feel free to ask any questions in the comments below
Partition SQL là gì? Cách sử dụng Partition SQL ? »
1. PARTITION SQL LÀ GÌ?
Partition SQL theo đúng như tên của nó là việc phân chia một table thành những phần nhỏ theo một logic nhất định, được phân biệt bằng key, key này thường là tên column trong table.
Như chúng ta đã biết mysql và nhiều hệ quản trị cơ sở dữ liệu khác, lưu trữ dữ liệu dưới dạng bảng gồm các hàng và cột. Mỗi lần truy vấn DB engine phải duyệt qua toàn bộ bảng để lấy dữ liệu, điều này tạo ra vấn đề về performance khi bản ghi trong table quá lớn, vấn đề này sẽ được giải quyết khá đơn giản bằng partition, nhờ kỹ thuật này chúng ta sẽ chỉ lấy dữ liệu tại vùng nhất định thay vì toàn bộ table như trước đây. Cùng xem một ví dụ để hiểu hơn về các hoạt động của partition nhé.
Ví dụ chúng ta có table persons chưa hề được tạo partition, và có các trường dữ liệu cụ thể như sau:
Table này khi tạo ra mặc định sẽ được lưu trữ thành 1 chunk trong file system
Khi sử dụng partition, table sẽ được phân chia thành nhiều chunk với key mà chúng ta đã định nghĩa. Ví dụ ở đây mình dùng trường age làm key.
Chúng ta có thể test độ hiệu quả của partition bằng câu truy vấn đơn giản như sau:
select * from persons where age = 24
Trong trường hợp chưa dùng partition thời gian thực thi query là 0.00064 sec.
Do table của chúng ta đang có ít dữ liệu nên độ hiệu quả khó có thể cảm nhận được, tuy nhiên với những cơ sở dữ liệu có hàng triệu bản ghi thì đây thực sự là một giải pháp tuyệt vời.
Như vậy qua ví dụ trên bạn đã phần nào hiểu về Partition là gì và tác dụng của nó như nào, bây giờ chúng ta cùng đi vào chi tiết hơn nhé.
2. CÁCH TẠO PARTITION TRONG SQL
Trước khi tạo partition bạn phải chắc chắn column mà bạn sử dụng được sử dụng thường xuyên trong các truy vấn, thì việc tạo partition mới thực sự có ý nghĩa.
Bạn có thể tạo partition bằng việc sử dụng CREATE TABLE hoặc ALTER TABLE
CREATE [TEMPORARY] TABLE [IF NOT EXISTS] tbl_name (create_definition,…) [table_options] [partition_options]
3. CÁC KIỂU PARTITION SQL CHÍNH
Range partitioning
List partitioning
Columns partitioning
Hash partitioning
Key partitioning
Subpartitioning
Trong phạm vi bài viết và sự hiểu biết mình sẽ tập trung làm rõ 2 loại partition là Range partitioning, List partitioning. Các loại còn lại hẹn các bạn trong một bài viết khác.
4. RANGE PARTITIONING
Range partitioning hiểu đơn giản là phân vùng theo khoảng mà bạn muốn sử dụng, tức là chia table ra thành nhiều khoảng giá trị, các khoảng giá trị này phải liên tiếp và không chồng chéo lên nhau, ví dụ trong 1 năm bạn có 12 tháng, chúng ta có thể chia thành 12 khoảng liên tiếp nhau như
p1: 01-01-2020 đến 31-01-2020
p2: 01-02-2020 đến 29-02-2020
p3: 01-03-2020 đến 30-03-2020
Mục đích của chia vùng theo khoảng sẽ giúp việc insert và tìm kiếm nhanh hơn rất nhiều, khi insert nếu có giá trị nằm trong khoảng nào thì nó sẽ được insert vào trong đúng khoảng đã định nghĩa, và khi tìm kiếm cũng vậy. Việc tạo range partitioning yêu cầu từ khóa VALUE LESS THAN để chỉ định phạm vi cần sử dụng . Cùng xem ví dụ cụ thể sau:
mysql> CREATE TABLE sales (no INT NOT NULL, date TIMESTAMP NOT NULL, code VARCHAR(15) NOT NULL, amount INT NOT NULL) PARTITION BY RANGE (amount) ( PARTITION p0 VALUES LESS THAN (100), PARTITION p1 VALUES LESS THAN (300), PARTITION p2 VALUES LESS THAN (700), PARTITION p3 VALUES LESS THAN (1000)); Query OK, 0 rows affected (1.34 sec)
Ở đây chúng ta đã tạo ra một table sales cùng với 4 partition được chỉ định rõ phạm vi sử dụng, p0 là sẽ lưu trữ những record có amount < 100. p2 sẽ có 100 <= amount < 300 Tương tự với các partition còn lại. Bây giờ chúng ta cùng insert vào table này mysql> INSERT INTO sales VALUES (1, ‘2013-01-02’, ‘C001’, 50), (2, ‘2013-01-25’, ‘C003’, 80), (3, ‘2013-02-15’, ‘C012’, 250), (4, ‘2013-03-26’, ‘C345’, 300), (5, ‘2013-04-19’, ‘C234’, 400), (6, ‘2013-05-31’, ‘C743’, 500), (7, ‘2013-06-11’, ‘C234’, 750), (8, ‘2013-07-24’, ‘C003′, 800),
và khi select ra chúng ta cùng xem kết quả
mysql> SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME=’sales’; +—————-+————+ | PARTITION_NAME | TABLE_ROWS | +—————-+————+ | p0 | 2 | | p1 | 1 | | p2 | 3 | | p3 | 2 | +—————-+————+
5. List partitioning
Loại này khác với loại range một chút là nó không phân chia theo khoảng nữa mà nó nhặt những phần tử được chỉ định tạo thành 1 danh sách, loại này chúng ta sẽ dùng từ khóa VALUES IN (list_value) để tạo partition . cũng lấy ví dụ trên nhưng thêm một chút trong bảng sales chúng ta có thêm cột mã nhân viên sale là saler_id để biết là nhân viên nào đã bán, và ví dụ trong một công ty chúng ta có 10 nhân viên sale, cùng với 3 nhóm sale, Mỗi nhóm làm việc ở một đoạn đường do cấp trên yêu cầu.
Ví dụ:
Nhóm A làm việc ở Phạm Văn Đồng
Nhóm B làm việc ở Trần Duy Hưng
Nhóm C làm việc ở Láng
Bây giờ cùng tạo table sales như sau
mysql> CREATE TABLE sales (no INT NOT NULL, date TIMESTAMP NOT NULL, code VARCHAR(15) NOT NULL, amount INT NOT NULL, saler_id INT NOT NULL) PARTITION BY LIST(saler_id) ( PARTITION pA VALUES IN (1,2,5), PARTITION pB VALUES IN (3,4,8), PARTITION pC VALUES IN (6,7,9,10)); Query OK, 0 rows affected (1.34 sec)
6. DELETE PARTITION
Nếu trong quá trình vận hàng hệ thống bạn không cần một lượng data nào đó nữa thì có thể xóa đi bằng cách xóa chính partition đã định nghĩa
MySQL> ALTER TABLE sales TRUNCATE PARTITION p0;
7. Kết luận
Như vậy trong bài viết này mình đã giới thiệu cho các bạn khái niệm, mục đích sử dụng của Mysql partition , và 2 loại partition chính hay được sử dụng là Range partition và List partition . Hy vọng đây sẽ là kiến thức bổ ích giúp các bạn tối ưu truy vấn trong quá trình làm dự án với những database lơn. Bài viết có thể còn nhiều thiếu sót , cũng như việc sử dụng từ ngữ partition và partitioning còn nhầm lẫn mong các bạn đóng góp và hẹn các bạn trong bài viết sớm nhất về 4 loại mysql partitioning còn lại.
SQL PARTITION BY Clause
Summary: in this tutorial, you will learn how to use the SQL PARTITION BY clause to change how the window function calculates the result.
SQL PARTITION BY clause overview
The PARTITION BY clause is a subclause of the OVER clause. The PARTITION BY clause divides a query’s result set into partitions. The window function is operated on each partition separately and recalculate for each partition.
The following shows the syntax of the PARTITION BY clause:
window_function ( expression ) OVER ( PARTITION BY expression1, expression2, … order_clause frame_clause ) Code language: SQL (Structured Query Language) ( sql )
You can specify one or more columns or expressions to partition the result set. The expression1 , expression1 , etc., can only refer to the columns derived by the FROM clause. They cannot refer to expressions or aliases in the select list.
The expressions of the PARTITION BY clause can be column expressions, scalar subquery, or scalar function. Note that a scalar subquery and scalar function always returns a single value.
If you omit the PARTITION BY clause, the whole result set is treated as a single partition.
PARTITION BY vs. GROUP BY
The GROUP BY clause is used often used in conjunction with an aggregate function such as SUM() and AVG() . The GROUP BY clause reduces the number of rows returned by rolling them up and calculating the sums or averages for each group.
For example, the following statement returns the average salary of employees by departments:
SELECT department_id, ROUND ( AVG (salary)) avg_department_salary FROM employees GROUP BY department_id ORDER BY department_id; Code language: SQL (Structured Query Language) ( sql )
The following picture shows the result:
The PARTITION BY clause divides the result set into partitions and changes how the window function is calculated. The PARTITION BY clause does not reduce the number of rows returned.
The following statement returns the employee’s salary and also the average salary of the employee’s department:
SELECT first_name, last_name, department_id, ROUND ( AVG (salary) OVER ( PARTITION BY department_id )) avg_department_salary FROM employees; Code language: SQL (Structured Query Language) ( sql )
Here is the partial output:
In simple words, the GROUP BY clause is aggregate while the PARTITION BY clause is analytic.
In this tutorial, you have learned about the SQL PARTITION BY clause that changes how the window function’s result is calculated.
When and how to use the SQL PARTITION BY clause
In this article, we will explore when and how to use the SQL PARTITION BY clause and compare it to using the GROUP BY clause.
Understanding the Window function
Database users use aggregate functions such as MAX(), MIN(), AVERAGE() and COUNT() for performing data analysis. These functions operate on an entire table and return single aggregated data using the GROUP BY clause. Sometimes, we require aggregated values over a small set of rows. In this case, the Window function combined with the aggregate function helps achieve the desired output. The Window function uses the OVER() clause, and it can include the following functions:
Partition By: This divides the rows or query result set into small partitions.
This divides the rows or query result set into small partitions. Order By: This arranges the rows in ascending or descending order for the partition window. The default order is ascending.
This arranges the rows in ascending or descending order for the partition window. The default order is ascending. Row or Range: You can further limit the rows in a partition by specifying the start and endpoints.
In this article, we will focus on exploring the SQL PARTITION BY clause.
Preparing sample data
Suppose we have a table [SalesLT].[Orders] that stores customer order details. It has a column [City] that specifies the customer city of where the order was placed.
CREATE TABLE [SalesLT].[Orders] ( orderid INT, orderdate DATE, customerName VARCHAR(100), City VARCHAR(50), amount MONEY ) INSERT INTO [SalesLT].[Orders] SELECT 1,’01/01/2021′,’Mohan Gupta’,’Alwar’,10000 UNION ALL SELECT 2,’02/04/2021′,’Lucky Ali’,’Kota’,20000 UNION ALL SELECT 3,’03/02/2021′,’Raj Kumar’,’Jaipur’,5000 UNION ALL SELECT 4,’04/02/2021′,’Jyoti Kumari’,’Jaipur’,15000 UNION ALL SELECT 5,’05/03/2021′,’Rahul Gupta’,’Jaipur’,7000 UNION ALL SELECT 6,’06/04/2021′,’Mohan Kumar’,’Alwar’,25000 UNION ALL SELECT 7,’07/02/2021′,’Kashish Agarwal’,’Alwar’,15000 UNION ALL SELECT 8,’08/03/2021′,’Nagar Singh’,’Kota’,2000 UNION ALL SELECT 9,’09/04/2021′,’Anil KG’,’Alwar’,1000 Go
Let’s say we want to know the total orders value by location (City). For this purpose, we use the SUM() and GROUP BY function as shown below.
SELECT City AS CustomerCity ,sum(amount) AS totalamount FROM [SalesLT].[Orders] GROUP BY city ORDER BY city
In the result set, we cannot use the non-aggregated columns in the SELECT statement. For example, we cannot display [CustomerName] in the output because it is not included in the GROUP BY clause.
SQL Server gives the following error message if you try to use the non-aggregated column in the column list.
SELECT City AS CustomerCity, CustomerName,amount, SUM(amount) OVER(PARTITION BY city) TotalOrderAmount FROM [SalesLT].[Orders]
As shown below, the PARTITION BY clause creates a smaller window (set of data rows), performs the aggregation and displays it. You can also view non-aggregated columns as well in this output.
Similarly, you can use functions AVG(), MIN(), MAX() to calculate the average, minimum and maximum amount from the rows in a window.
SELECT City AS CustomerCity, CustomerName,amount, SUM(amount) OVER(PARTITION BY city) TotalOrderAmount, Avg(amount) OVER(PARTITION BY city) AvgOrderAmount, Min(amount) OVER(PARTITION BY city) MinOrderAmount, MAX(amount) OVER(PARTITION BY city) MaxOrderAmount FROM [SalesLT].[Orders]
Using the SQL PARTITION BY clause with the ROW_NUMBER() function
Previously, we got the aggregated values in a window using the PARTITION BY clause. Suppose that instead of the total, we require the cumulative total in a partition.
A cumulative total works in the following ways.
Row Cumulative total 1 Rank 1+ 2 2 Rank 2+3 3 Rank 3+4
The row rank is calculated using the function ROW_NUMBER(). Let’s first use this function and view the row ranks.
The ROW_NUMBER() function uses the OVER and PARTITION BY clause and sorts results in ascending or descending order. It starts ranking rows from 1 per the sorting order.
SELECT City AS CustomerCity, CustomerName,amount, ROW_NUMBER() OVER(PARTITION BY city ORDER BY amount DESC) AS [Row Number] FROM [SalesLT].[Orders]
For example, in the [Alwar] city, the row with the highest amount (25000.00) is in row 1. As shown below, it ranks rows in the window specified by the PARTITION BY clause. For example, we have three different cities [Alwar], [Jaipur] and [Kota], and each window (city) gets its row ranks.
To calculate the cumulative total, we use the following arguments.
CURRENT ROW: It specifies the starting and ending point in the specified range.
1 following: It specifies the number of rows (1) to follow from the current row.
SELECT City AS CustomerCity, CustomerName,amount, ROW_NUMBER() OVER(PARTITION BY city ORDER BY amount DESC) AS [Row Number], SUM(amount) OVER(PARTITION BY city ORDER BY amount DESC ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING) AS CumulativeSUM FROM [SalesLT].[Orders]
The following image shows that you get a cumulative total instead of an overall total in a window specified by the PARTITION BY clause.
If we use ROWS UNBOUNDED PRECEDING in the SQL PARTITION BY clause, it calculates the cumulative total in the following way. It uses the current rows along with the rows having the highest values in the specified window.
Row Cumulative total 1 Rank 1 2 Rank 1+2 3 Rank 1+2+3
SELECT City AS CustomerCity, CustomerName,amount, ROW_NUMBER() OVER(PARTITION BY city ORDER BY amount DESC) AS [Row Number], SUM(amount) OVER(PARTITION BY city ORDER BY amount DESC ROWS UNBOUNDED PRECEDING) AS CumulativeSUM FROM [SalesLT].[Orders]
Comparing the GROUP BY and SQL PARTITION BY clause
GROUP BY PARTITION BY It returns one row per group after calculating the aggregate values. It returns all rows from the SELECT statement along with additional columns of aggregated values. We cannot use the non-aggregated column in the SELECT statement. We can use required columns in the SELECT statement, and it does not produce any errors for the non-aggregated column. It requires using the HAVING clause to filter records from the SELECT statement. The PARTITION function can have additional predicates in the WHERE clause apart from the columns used in the SELECT statement. The GROUP BY is used in regular aggregates. PARTITION BY is used in windowed aggregates. We cannot use it for calculating row numbers or their ranks. It can calculate row numbers and their ranks in the smaller window.
Putting it to use
It’s recommended to use the SQL PARTITION BY clause while working with multiple data groups for the aggregated values in the individual group. Similarly, it can be used to view original rows with the additional column of aggregated values.
How to Use the SQL PARTITION BY With OVER
At the heart of every window function call is an OVER clause that defines how the windows of the records are built. Within the OVER clause, there may be an optional PARTITION BY subclause that defines the criteria for identifying which records to include in each window. Read on and take an important step in growing your SQL skills!
What Is the PARTITION BY Clause in SQL?
The SQL PARTITION BY expression is a subclause of the OVER clause, which is used in almost all invocations of window functions like AVG() , MAX() , and RANK() . As many readers probably know, window functions operate on window frames which are sets of rows that can be different for each record in the query result. This is where the SQL PARTITION BY subclause comes in: it is used to define which records to make part of the window frame associated with each record of the result.
This article explains the SQL PARTITION BY and its uses with examples. Since it is deeply related to window functions, you may first want to read some articles on window functions, like “SQL Window Function Example With Explanations” where you find a lot of examples. If you want to learn more about window functions, there is also an interesting article with many pointers to other window functions articles.
The first thing to focus on is the syntax. Here’s how to use the SQL PARTITION BY clause:
SELECT , OVER(PARTITION BY [ORDER BY ]) FROM table;
Let’s look at an example that uses a PARTITION BY clause. We will use the following table called car_list_prices :
car_make car_model car_type car_price Ford Mondeo premium 18200 Renault Fuego sport 16500 Citroen Cactus premium 19000 Ford Falcon low cost 8990 Ford Galaxy standard 12400 Renault Megane standard 14300 Citroen Picasso premium 23400
For each car, we want to obtain the make, the model, the price, the average price across all cars, and the average price over the same type of car (to get a better idea of how the price of a given car compared to other cars). Here’s the query:
SELECT car_make, car_model, car_price, AVG(car_price) OVER() AS “overall average price”, AVG(car_price) OVER (PARTITION BY car_type) AS “car type average price” FROM car_list_prices
The result of the query is the following:
car_make car_model car_price overall average price car type average price Ford Mondeo 18200 16112.85 8990.00 Renault Fuego 16500 16112.85 20200.00 Citroen Cactus 19000 16112.85 20200.00 Ford Falcon 8990 16112.85 20200.00 Ford Galaxy 12400 16112.85 16500.00 Renault Megane 14300 16112.85 13350.00 Citroen Picasso 23400 16112.85 13350.00
The above query uses two window functions. The first is used to calculate the average price across all cars in the price list. It uses the window function AVG() with an empty OVER clause as we see in the following expression:
AVG(car_price) OVER() AS “overall average price”
The second window function is used to calculate the average price of a specific car_type like standard, premium, sport, etc. This is where we use an OVER clause with a PARTITION BY subclause as we see in this expression:
AVG(car_price) OVER (PARTITION BY car_type) AS “car type average price”
The window functions are quite powerful, right? If you’d like to learn more by doing well-prepared exercises, I suggest the course Window Functions, where you can learn about and become comfortable with using window functions in SQL databases.
Going Deep With the SQL PARTITION BY Clause
The GROUP BY clause groups a set of records based on criteria. This allows us to apply a function (for example, AVG() or MAX() ) to groups of records to yield one result per group.
As an example, say we want to obtain the average price and the top price for each make. Use the following query:
SELECT car_make, AVG(car_price) AS average_price, MAX(car_price) AS top_price FROM car_list_prices GROUP BY car_make
Here is the result of this query:
car_make average_price top_price Ford 13196 18200 Renault 15400 16500 Citroen 21200 23400
Compared to window functions, GROUP BY collapses individual records into a group. As a consequence, you cannot refer to any individual record field; that is, only the columns in the GROUP BY clause can be referenced.
For example, say you want to create a report with the model, the price, and the average price of the make. You cannot do this by using GROUP BY , because the individual records of each model are collapsed due to the clause GROUP BY car_make . For something like this, you need to use window functions, as we see in the following example:
SELECT car_make, car_model, car_price, AVG(car_price) OVER (PARTITION BY car_make) AS average_make FROM car_list_prices
The result of this query is the following:
car_make car_model car_price average_make Citroen Picasso 23400 21200 Citroen Cactus 19000 21200 Ford Galaxy 12400 13196 Ford Falcon 8990 13196 Ford Mondeo 18200 13196 Renault Megane 14300 15400 Renault Fuego 16500 15400
For those who want to go deeper, I suggest the article ““What Is the Difference Between a GROUP BY and a PARTITION BY?” with plenty of examples using aggregate and window functions.
In addition to the PARTITION BY clause, there is another clause called ORDER BY that establishes the order of the records within the window frame. Some window functions require an ORDER BY . For example, the LEAD() and the LAG() window functions need the record window to be ordered since they access the preceding or the next record from the current record.
A window frame is composed of several rows defined by the criteria in the PARTITION BY clause. However, we can specify limits or bounds to the window frame as we see in the following image:
The lower and upper bounds in the OVER clause may be:
UNBOUNDED PRECEDING
n PRECEDING
CURRENT ROW
n FOLLOWING
UNBOUNDED FOLLOWING
When we do not specify any bound in an OVER clause, its window frame is built based on some default boundary values. They depend on the syntax used to call the window function. The following table shows the default bounds of the window frame.
Syntax used First Row in Window Last Row in Window Just empty OVER() clause UNBOUNDED PRECEDING UNBOUNDED FOLLOWING OVER(PARTITION BY …) UNBOUNDED PRECEDING UNBOUNDED FOLLOWING OVER(PARTITION BY … ORDER BY …) UNBOUNDED PRECEDING CURRENT ROW
There is a detailed article called “SQL Window Functions Cheat Sheet” where you can find a lot of syntax details and examples about the different bounds of the window frame.
The SQL PARTITION BY Clause in Action
In this section, we show some examples of the SQL PARTITION BY clause. All are based on the table paris_london_flights , used by an airline to analyze the business results of this route for the years 2018 and 2019. Here’s a subset of the data:
aircraft_make aircarft_model flight_number scheduled_departure real_departure scheduled_arrival num_of_passengers total_revenue Boeing 757 300 FLP003 2019-01-30 15:00:00 2019-01-30 15:00:00 2019-01-30 15:00:00 260 82630.10 Boeing 737 200 FLP003 2019-02-01 15:00:00 2019-02-01 15:10:00 2019-02-01 15:55:00 195 58459.34 Airbus A500 FLP003 2019-02-01 15:00:00 2019-02-01 15:03:00 2019-02-01 15:03:55 312 91570.87 Airbus A500 FLP001 2019-10-28 05:00:00 2019-10-28 05:04:00 2019-10-28 05:55:00 298 87943.00 Boeing 737 200 FLP002 2019-10-28 09:00:00 2019-10-28 09:00:00 2019-10-28 09:55:00 178 56342.45
Example 1
The first query generates a report including the flight_number, aircraft_model with the quantity of passenger transported, and the total revenue. The query is below:
SELECT DISTINCT flight_number, aircraft_model, SUM(num_of_passengers) OVER (PARTITION BY flight_number, aircraft_model) AS total_passengers, SUM(total_revenue) OVER (PARTITION BY flight_number, aircraft_model) AS total_revenue FROM paris_london_flights ORDER BY flight_number, aircraft_model;
Since the total passengers transported and the total revenue are generated for each possible combination of flight_number and aircraft_model , we use the following PARTITION BY clause to generate a set of records with the same flight number and aircraft model:
OVER (PARTITION BY flight_number, aircraft_model)
Then, for each set of records, we apply window functions SUM(num_of_passengers) and SUM(total_revenue) to obtain the metrics total_passengers and total_revenue shown in the next result set.
flight_number aircraft_model total_passengers total_revenue FLP001 737 200 20481 6016060.82 FLP001 757 300 18389 5361126.23 FLP001 Airbus A500 53872 15892165.58 FLP002 737 200 21660 6297197.71 FLP002 757 300 16869 4951475.86 FLP002 Airbus A500 54627 16004812.16 FLP003 737 200 20098 5874892.44 FLP003 757 300 15708 4573379.28 FLP003 Airbus A500 57533 16712475.04
Example 2
In the next query, we show how the business evolves by comparing metrics from one month with those from the previous month. We create a report using window functions to show the monthly variation in passengers and revenue.
WITH year_month_data AS ( SELECT DISTINCT EXTRACT(YEAR FROM scheduled_departure) AS year, EXTRACT(MONTH FROM scheduled_departure) AS month, SUM(number_of_passengers) OVER (PARTITION BY EXTRACT(YEAR FROM scheduled_departure), EXTRACT(MONTH FROM scheduled_departure) ) AS passengers FROM paris_london_flights ORDER BY 1, 2 ) SELECT year, month, passengers, LAG(passengers) OVER (ORDER BY year, month) passengers_previous_month, passengers – LAG(passengers) OVER (ORDER BY year, month) AS passengers_delta FROM year_month_data;
In the query above, we use a WITH clause to generate a CTE (CTE stands for common table expressions and is a type of query to generate a virtual table that can be used in the rest of the query). We populate data into a virtual table called year_month_data , which has 3 columns: year , month , and passengers with the total transported passengers in the month.
Then, the second query (which takes the CTE year_month_data as an input) generates the result of the query. The column passengers contains the total passengers transported associated with the current record. With the LAG(passenger) window function, we obtain the value of the column passengers of the previous record to the current record. We ORDER BY year and mont h:
LAG(passengers) OVER (ORDER BY year, month) passengers_previous_month
It obtains the number of passengers from the previous record, corresponding to the previous month. Then, we have the number of passengers for the current and the previous months. Finally, in the last column, we calculate the difference between both values to obtain the monthly variation of passengers.
year month passengers passengers_previous_month passengers_delta 2018 12 11469 null null 2019 1 24723 11469 13254 2019 2 22536 24723 -2187 2019 3 24994 22536 2458 2019 4 24408 24994 -586 2019 5 23998 24408 -410 2019 6 23793 23998 -205 2019 7 24816 23793 1023 2019 8 24334 24816 -482 2019 9 23719 24334 -615 2019 10 24989 23719 1270 2019 11 24371 24989 -618 2019 12 1087 24371 -23284
Example 3
For our last example, let’s look at flight delays. We want to obtain different delay averages to explain the reasons behind the delays.
We use a CTE to calculate a column called month_delay with the average delay for each month and obtain the aircraft model. Then in the main query, we obtain the different averages as we see below:
WITH paris_london_delays AS ( SELECT DISTINCT aircraft_model, EXTRACT(YEAR FROM scheduled_departure) AS year, EXTRACT(MONTH FROM scheduled_departure) AS month, AVG(real_departure – scheduled_departure) AS month_delay FROM paris_london_flights GROUP BY 1, 2, 3 ) SELECT DISTINCT aircraft_model, year, month, month_delay AS monthly_avg_delay, AVG(month_delay) OVER (PARTITION BY aircraft_model, year) AS year_avg_delay, AVG(month_delay) OVER (PARTITION BY year) AS year_avg_delay_all_models, AVG(month_delay) OVER (PARTITION BY aircraft_model, year ORDER BY month ROWS BETWEEN 3 PRECEDING AND CURRENT ROW ) AS rolling_average_last_4_months FROM paris_london_delays ORDER BY 1,2,3
This query calculates several averages. The first is the average per aircraft model and year, which is very clear. The second is the average per year across all aircraft models. Note we only use the column year in the PARTITION BY clause. The third and last average is the rolling average, where we use the most recent 3 months and the current month (i.e., row) to calculate the average with the following expression:
AVG(month_delay) OVER (PARTITION BY aircraft_model, year ORDER BY month ROWS BETWEEN 3 PRECEDING AND CURRENT ROW ) AS rolling_average_last_4_months
The clause ROWS BETWEEN 3 PRECEDING AND CURRENT ROW in the PARTITION BY restricts the number of rows (i.e., months) to be included in the average: the previous 3 months and the current month. You can see a partial result of this query below:
aircraft_model year month month_delay year_avg_delay year_avg_delay_all_models rolling_average_last_4_months 737 200 2018 12 00:02:13.84 00:02:13.84 00:03:13.70 00:02:13.84 737 200 2019 1 00:02:16.80 00:02:36.59 00:02:34.12 00:02:16.80 737 200 2019 2 00:02:35.00 00:02:36.59 00:02:34.12 00:02:25.90 737 200 2019 3 00:01:38.40 00:02:36.59 00:02:34.12 00:02:10.06 737 200 2019 4 00:04:00.00 00:02:36.59 00:02:34.12 00:02:37.55 737 200 2019 5 00:03:12.72 00:02:36.59 00:02:34.12 00:02:51.53 737 200 2019 6 00:02:21.42 00:02:36.59 00:02:34.12 00:02:48.13
The article “The RANGE Clause in SQL Window Functions: 5 Practical Examples” explains how to define a subset of rows in the window frame using RANGE instead of ROWS, with several examples. Another interesting article is “Common SQL Window Functions: Using Partitions With Ranking Functions” in which the PARTITION BY clause is covered in detail.
The Power of Window Functions and the SQL PARTITION BY
Window functions are a very powerful resource of the SQL language, and the SQL PARTITION BY clause plays a central role in their use. In this article, we have covered how this clause works and showed several examples using different syntaxes.
Before closing, I suggest an Advanced SQL course, where you can go beyond the basics and become a SQL master. If you want to read about the OVER clause, there is a complete article about the topic: “How to Define a Window Frame in SQL Window Functions.” Improve your skills and grow your assets!
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