EDUCBA

EDUCBA

MENUMENU
  • Free Tutorials
  • Free Courses
  • Certification Courses
  • 360+ Courses All in One Bundle
  • Login

SQL Table Partitioning

Home » Data Science » Data Science Tutorials » SQL Tutorial » SQL Table Partitioning

SQL Table Partitioning

Introduction to SQL Table Partitioning

Table partitioning in standard query language (SQL) is a process of dividing very large tables into small manageable parts or partitions, such that each part has its own name and storage characteristics. Table partitioning helps in significantly improving database server performance as less number of rows have to be read, processed, and returned. We can use partitioning techniques for dividing indexes and index-organized tables also.

Table partitioning can be of two types, namely, vertical partitioning or horizontal partitioning. In vertical partitioning, we divide the table column-wise. While in horizontal partitioning we divide the table row-wise.

Start Your Free Data Science Course

Hadoop, Data Science, Statistics & others

Vertical Partitioning:

e_transactions :

[order_id]
[ordered_at]
[shipped_at]
[order_amount]
[customer_id]
order_status :

[order_id]
[ordered_at]
[shipped_at]

As shown in the illustration above, the e-transactions table has been vertically partitioned and a new partition called “order_status” with columns such as order_id, ordered_at, and shipped_at has been created. Next, let’s have a look at horizontal partitioning.

Horizontal Partitioning:

 e_transactions :

order_id ordered_at shipped_at order_amount customer_id
AJ202001 2020-07-01 15:48:25.223 2020-07-02 4315 CA01
AJ202001 2020-07-02 10:48:00.000 2020-07-06 4315 CA01
AJ202004 2020-07-02 10:48:00.000 2020-07-05 5460 CA06
AJ202003 2020-06-30 06:28:00.000 2020-07-02 1462 CA05
AJ202005 2020-07-01 06:18:00.000 2020-07-03 15646 CA032
AJ202002 2020-07-01 05:10:00.000 2020-07-04 1978 CA07

Partitions :

orders_30_06_2020 :

order_id ordered_at shipped_at order_amount customer_id
AJ202003 2020-06-30 06:28:00.000 2020-07-02 1462 CA05

orders_1_07_2020 :

order_id ordered_at shipped_at order_amount customer_id
AJ202001 2020-07-01 15:48:25.223 2020-07-02 4315 CA01
AJ202005 2020-07-01 06:18:00.000 2020-07-03 15646 CA032
AJ202002 2020-07-01 05:10:00.000 2020-07-04 1978 CA07

orders_2_07_2020 :

order_id ordered_at shipped_at order_amount customer_id
AJ202001 2020-07-02 10:48:00.000 2020-07-06 4315 CA01
AJ202004 2020-07-02 10:48:00.000 2020-07-05 5460 CA06

As shown in the illustration, when we partition a table horizontally its done row wise based on the value of a particular column. In this case, we chose the date part of ordered_at column. Three different partitions have been prepared.

Syntax and parameters

The basic syntax for partitioning a table using range is as follows :

Main Table Creation:

CREATE TABLE main_table_name (
column_1 data type,
column_2 data type,
.
.
. ) PARTITION BY RANGE (column_2);

Partition Table Creation:

CREATE TABLE partition_name
PARTITION OF main_table_name FOR VALUES FROM (start_value) TO (end_value);

The parameters used in the above-mentioned syntax are similar to CREATE TABLE statement, except these :

PARTITION BY RANGE (column_2) : column_2 is the field on the basis of which partitions will be created.
partition_name : name of the partition table

FROM (start_value) TO (end_value) : The range of values in column_2, which forms the part of this partition. Note that start_value is inclusive, while end_value is exclusive.

Here is an example to illustrate it further.

Examples of SQL Table Partitioning

Imagine that you are working as a data engineer for an ecom firm that gets a huge number of orders on a daily basis. You usually store data such as order_id, order_at, customer_id, etc. in a SQL table called “e-transactions”. Since the table has a humongous amount of data in it, the low load speed and high return time, etc. have become a problem for data analysts, who use this table for preparing KPIs on a daily basis. What will you do to improvise this table, so that data analysts can run queries quickly?

A logical step would be partitioning the table into smaller parts. Let’s say we create partitions such that the partition stores data pertaining to specified order dates only. This way, we will have less data in each partition, and working with it will be more fun.

We can partition the table using declarative partitioning ie. by using a PARTITION BY RANGE (column_name) function as shown below.

CREATE TABLE e_transactions(
order_id varchar(255) NULL,
ordered_at date NULL,
shipped_at date NULL,
order_amount numeric(18, 0) NULL,
customer_id varchar(255) NULL
) PARTITION BY RANGE( ordered_at);

SQL Table partitioning 1

The table has been successfully created with provisions for partition.

  • Creating Table Partitions

 Next, let us create partition tables for e-transactions table.

CREATE TABLE orders_2020_07_01
PARTITION OF e_transactions FOR VALUES FROM ('2020-07-01') TO ('2020-07-02');
CREATE TABLE orders_2020_07_02
PARTITION OF e_transactions FOR VALUES FROM ('2020-07-02') TO ('2020-07-03');

e-transactions table 1

we have successfully created two partitions. The newly created partitions can be seen in the browser window.

e-transactions table 2

Now, let us check the functionality of these table partitions by inserting a few records in the e-transactions table.

INSERT INTO public.e_transactions(
order_id, ordered_at, shipped_at, order_amount, customer_id)
VALUES (1,'2020-07-01','2020-07-02',456,'1'),
(2,'2020-07-02','2020-07-03',631,'1');

e-transactions table 3

Let’s check if the records are stored in designated partitions or not. Our good old friend SELECT statement is here to help us.

SELECT * FROM orders_2020_07_01;

e-transactions table 4

SELECT * FROM orders_2020_07_02;

Creating Table Partitions 5

We can clearly observe from the results of SELECT queries that partitions have been successfully created and used.

  • Removing Table Partitions

Let us assume that we no longer require one of the partitions. We can drop a partition by using the ALTER command.

ALTER TABLE e_transactions DETACH PARTITION orders_2020_07_01;

Removing Table Partitions

Now, the e_transactions table has only one partition as shown in the image below:

e - transcation

But, the orders_2020_07_01 has become a regular table now.

orders_2020_07_01

In this case, we have used the ALTER command to remove a partition from the table but a DROP command can be used instead. However, we should note that the ALTER command will keep the partition table as a regular table after removing it from the partitions of the table, but DROP will remove it from the database. Hence, the former is the most preferred command.

Conclusion

Table partitioning is a method used in relational databases to divide a huge table into several smaller parts. This helps in executing queries on a huge table faster.

Recommended Articles

This is a guide to SQL Table Partitioning. Here we discuss the Introduction, syntax, parameters, and Examples, and code implementation. You may also have a look at the following articles to learn more –

  1. SQL Select Top
  2. SQL Cluster
  3. SQL Merge Two Tables
  4. SQL DROP TRIGGER

MS SQL Training (13 Courses, 11+ Projects)

13 Online Courses

11 Hands-on Projects

62+ Hours

Verifiable Certificate of Completion

Lifetime Access

Learn More

0 Shares
Share
Tweet
Share
Primary Sidebar
SQL Tutorial
  • Advanced
    • Aggregate Functions in SQL
    • IF ELSE Statement in SQL
    • SQL CASE Statement
    • SQL While Loop
    • SQL INSTR()
    • What is Procedure in SQL
    • Stored Procedure in SQL?
    • SQL Server Constraints
    • SQL DELETE ROW
    • Column in SQL
    • Table in SQL
    • SQL Virtual Table
    • SQL Merge Two Tables
    • SQL Table Partitioning
    • SQL Temporary Table
    • SQL Clone Table
    • SQL Rename Table
    • SQL LOCK TABLE
    • SQL Mapping
    • Cursors in SQL
    • AND in SQL
    • Wildcard in SQL
    • SQL FETCH NEXT
    • SQL Views
    • Triggers in SQL
    • SQL UPDATE Trigger
    • SQL AFTER UPDATE Trigger
    • SQL Update Statement
    • SQL DROP TRIGGER
    • Views in MySQL
    • SQL COMMIT
    • Distinct Keyword in SQL
    • PARTITION BY in SQL
    • SQL Set Operators
    • SQL UNION ALL
    • Metadata in SQL
    • SQL Bulk Insert
    • Array in SQL
    • SQL REGEXP
    • JSON in SQL
    • SQL For loop
    • EXPLAIN in SQL
    • SQL Cluster
    • SQL Backup
    • SQL Pattern Matching
    • SQL Users
    • ISNULL SQL Server
    • SQL Import CSV
  • Basic
    • What is SQL
    • Careers in SQL
    • Careers in SQL Server
    • IS SQL Microsoft?
    • SQL Management Tools
    • What is SQL Developer
    • Uses of SQL
    • How to Install SQL Server
    • What is SQL Server
    • Database in SQL
    • SQL Data Types
    • SQL Keywords
    • Composite Key in SQL
    • SQL Constraints
    • Transactions in SQL
    • First Normal Form
    • SQL Server Data Types
    • SQL Administration
    • SQL Variables
    • Cheat sheet SQL
  • Operators
    • SQL Operators
    • SQL Arithmetic Operators
    • SQL Logical Operators
    • SQL String Operators
    • Ternary Operator in SQL
  • Commands
    • SQL Commands
    • SQL Alter Command
    • SQL Commands Update
    • SQL DML Commands
    • FETCH in SQL
  • Clause
    • SQL Clauses
    • SQL IN Operator
    • SQL LIKE Clause
    • SQL NOT Operator
    • SQL Minus
    • SQL WHERE Clause
    • SQL with Clause
    • SQL HAVING Clause
    • GROUP BY clause in SQL
    • ORDER BY Clause in SQL
    • SQL ORDER BY CASE
    • SQL ORDER BY DATE
    • SQL ORDER BY Alphabetical
    • SQL ORDER BY Ascending
    • SQL GROUP BY Month
    • SQL GROUP BY Multiple Columns
    • SQL GROUP BY DAY
    • SQL GROUPING SETS
  • Queries
    • SQL Insert Query
    • SQL SELECT Query
    • SQL SELECT RANDOM
    • SQL Except Select
    • SQL Subquery
    • SQL SELECT DISTINCT
    • SQL WITH AS Statement
  • Keys
    • SQL Keys
    • Primary Key in SQL
    • Foreign Key in SQL
    • Unique Key in SQL
    • Alternate Key in SQL
  • Functions
    • SQL Date Function
    • SQL String Functions
    • SQL Compare String
    • SQL Window Functions
    • SQL Timestamp
    • SQL TO_DATE()
    • SQL DATEADD()
    • ANY in SQL
    • LIKE Query in SQL
    • BETWEEN in SQL
    • LTRIM() in SQL
    • TOP in SQL
    • SQL Select Top
    • Merge SQL
    • SQL TRUNCATE()
    • SQL UNION
    • SQL ALL
    • SQL INTERSECT
    • SQL Alias
    • SQL Server Substring
    • CUBE in SQL
    • SQL RANK()
    • SQL CTE
    • SQL LAG()
    • SQL MID
    • SQL avg()
    • SQL WEEK
    • SQL DELETE
    • SQL DATEPART()
    • SQL DECODE()
    • SQL DENSE_RANK()
    • SQL NTILE()
    • SQL NULLIF()
    • SQL Stuff
    • SQL Ceiling
    • SQL EXISTS
    • SQL LEAD()
    • SQL ROW_NUMBER
    • SQL Server Replace
    • T-SQL INSERT
    • SQL Ranking Function
  • Joins
    • Join Query in SQL
    • Types of Joins in SQL
    • Types of Joins in SQL Server
    • SQL Inner Join
    • SQL Join Two Tables
    • SQL Delete Join
    • SQL Left Join
    • SQL Right Join
    • SQL Cross Join
    • SQL Outer Join
    • SQL Full Join
    • SQL Self Join
    • Natural Join SQL
    • SQL Multiple Join
  • Interview Questions
    • SQL Interview Questions
    • Advance SQL Interview Questions
    • SQL Joins Interview Questions
    • SQL Server Interview Questions

Related Courses

JDBC Training Course

PHP course

Windows 10 Training

SQL Course Training

PL/SQL Certification Courses

Oracle Certification Courses

Footer
About Us
  • Blog
  • Who is EDUCBA?
  • Sign Up
  • Corporate Training
  • Certificate from Top Institutions
  • Contact Us
  • Verifiable Certificate
  • Reviews
  • Terms and Conditions
  • Privacy Policy
  •  
Apps
  • iPhone & iPad
  • Android
Resources
  • Free Courses
  • Database Management
  • Machine Learning
  • All Tutorials
Certification Courses
  • All Courses
  • Data Science Course - All in One Bundle
  • Machine Learning Course
  • Hadoop Certification Training
  • Cloud Computing Training Course
  • R Programming Course
  • AWS Training Course
  • SAS Training Course

© 2020 - EDUCBA. ALL RIGHTS RESERVED. THE CERTIFICATION NAMES ARE THE TRADEMARKS OF THEIR RESPECTIVE OWNERS.

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you
Book Your One Instructor : One Learner Free Class

Let’s Get Started

This website or its third-party tools use cookies, which are necessary to its functioning and required to achieve the purposes illustrated in the cookie policy. By closing this banner, scrolling this page, clicking a link or continuing to browse otherwise, you agree to our Privacy Policy

EDUCBA

*Please provide your correct email id. Login details for this Free course will be emailed to you
EDUCBA Login

Forgot Password?

EDUCBA
Free Data Science Course

Hadoop, Data Science, Statistics & others

*Please provide your correct email id. Login details for this Free course will be emailed to you

Special Offer - MS SQL Training (13 Courses, 11+ Projects) Learn More