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Horizontal Partitioning
MySQL Tutorial
This SQL tutorial is structured for beginners to guide them from the foundational concepts to advanced data manipulation and querying techniques in SQL. By the end of this tutorial, you will have developed a robust understanding of SQL, equipping you with the knowledge to efficiently manage and analyze data across various database systems. This guide sets the stage for your growth into a skilled data professional, ready to tackle complex data challenges and contribute to the field of data analysis and database management.
Horizontal Partitioning
The MySQL Partitioning is a technique that can be used to divide a database table into smaller tables i.e. partitions. These smaller tables are stored in different physical locations and are treated as separate tables. Thus, the data in these smaller tables can be accessed and managed individually.
But note that, even if the data smaller tables is managed separately, they are not independent tables; i.e., they are still a part of main table.
There are two forms of partitioning in MySQL: Horizontal Partitioning and Vertical Partitioning.
MySQL Horizontal Partitioning
The MySQL Horizontal partitioning is used to divide the table rows into multiple partitions. Since it divides the rows, all the columns will be present in each partition. All the partitions can be accessed individually or collectively.
There are several types of MySQL horizontal partitioning methods
MySQL Range Partitioning
The MySQL RANGE partitioning is used to divide a table into partitions based on a specific range of column values. Each table partition contains rows with column values falling within that defined range.
Example
Let us create a table named CUSTOMERS and partition it by the AGE column into four partitions: P1, P2, P3, and P4 using the "PARTITION BY RANGE" clause
CREATE TABLE CUSTOMERS( ID int not null, NAME varchar(40) not null, AGE int not null, ADDRESS char(25) not null, SALARY decimal(18, 2) ) PARTITION BY RANGE (AGE) ( PARTITION P1 VALUES LESS THAN (20), PARTITION P2 VALUES LESS THAN (30), PARTITION P3 VALUES LESS THAN (40), PARTITION P4 VALUES LESS THAN (50));Here, we are inserting rows into the above created table
INSERT INTO CUSTOMERS VALUES (1, 'Ramesh', 19, 'Ahmedabad', 2000.00 ),(2, 'Khilan', 25, 'Delhi', 1500.00 ),(3, 'kaushik', 23, 'Kota', 2000.00 ),(4, 'Chaitali', 31, 'Mumbai', 6500.00 ),(5, 'Hardik', 35, 'Bhopal', 8500.00 ),(6, 'Komal', 47, 'MP', 4500.00 ),(7, 'Muffy', 43, 'Indore', 10000.00 );Following is the CUSTOMERS table obtained
Now that we have some data in the CUSTOMERS table, we can display the partition status to see how the data is distributed among the partitions using the following query
SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME='CUSTOMERS';The above query will show us the number of rows in each partition. For example, P1 has 1 row, P2 has 2 rows, P3 has 2 rows, and P4 has 2 rows as shown below
Displaying Partitions
We can also display data from specific partitions using the PARTITION clause. For instance, to retrieve data from partition P1, we use the following query
SELECT * FROM CUSTOMERS PARTITION (p1);It will display all the records in partition P1
Similarly, we can display other partitions using the same syntax.
Handling Data Outside the Range
If we attempt to insert a value into the AGE column that doesn't fall within any of the defined partitions, it will fail with an error, as shown below
INSERT INTO CUSTOMERS VALUES (8, 'Brahmi', 70, 'Hyderabad', 19000.00 );Following is the error obtained
ERROR 1526 (HY000): Table has no partition for value 70Truncating Partitions
We can also manage partitions by truncating them if needed. For example, to empty partition P2, we can use the following query
ALTER TABLE CUSTOMERS TRUNCATE PARTITION p2;The output obtained is as shown below
Query OK, 0 rows affected (0.03 sec)This will remove all data from partition P2, making it empty as shown below
SELECT * FROM CUSTOMERS PARTITION (p2);Following is the output produced
Empty set (0.00 sec)We can verify the CUSTOMERS table using the following SELECT query
SELECT * FROM CUSTOMERS;We can see in the table below that the rows belonging to p2 partition are deleted
MySQL List Partitioning
The MySQL List Partitioning is used to divide the table into partitions based on a discrete set of values for a specific column. Each partition contains rows that match a particular value within the defined set.
Example
In this example, we will create a table named STUDENTS and divide it into four partitions (P1, P2, P3, and P4) based on the "DEPARTMENT_ID" column using the "PARTITION BY LIST" clause
CREATE TABLE STUDENTS( ID int, NAME varchar(50), DEPARTMENT varchar(50), DEPARTMENT_ID int ) PARTITION BY LIST(DEPARTMENT_ID)( PARTITION P1 VALUES IN (3, 5, 6, 7, 9), PARTITION P2 VALUES IN (13, 15, 16, 17, 20), PARTITION P3 VALUES IN (23, 25, 26, 27, 30), PARTITION P4 VALUES IN (33, 35, 36, 37, 40));Here, we are inserting rows into the above-created table
INSERT INTO STUDENTS VALUES (1, 'Ramesh', "cse", 5),(2, 'Khilan', "mech", 20),(3, 'kaushik', "ece", 17),(4, 'Chaitali', "eee", 33),(5, 'Hardik', "IT", 36),(6, 'Komal', "Hotel management", 40),(7, 'Muffy', "Fashion", 23);Following is the STUDENTS table obtained
We can display the partition status of the STUDENTS table to see how the data is distributed among partitions using the following query
SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME='STUDENTS';The output of this query will show the number of rows in each partition. For instance, P1 has 1 row, P2 has 2 rows, P3 has 1 row, and P4 has 3 rows
MySQL Hash Partitioning
The MySQL HASH partitioning is used to divide the table data into partitions using a hash function based on a specific column(s). The data will be evenly distributed among the partitions.
Example
In the following query, we are creating a table with the name EMPLOYEES with four partitions based on the "id" column using the PARTITION BY HASH clause
CREATE TABLE EMPLOYEES ( id INT NOT NULL, name VARCHAR(50) NOT NULL, department VARCHAR(50) NOT NULL, salary INT NOT NULL ) PARTITION BY HASH(id) PARTITIONS 4;Here, we are inserting rows into the above-created table
INSERT INTO EMPLOYEES VALUES (1, 'Varun', 'Sales', 50000),(2, 'Aarohi', 'Marketing', 60000),(3, 'Paul', 'IT', 70000),(4, 'Vaidhya', 'Finance', 80000),(5, 'Nikhil', 'Sales', 55000),(6, 'Sarah', 'Marketing', 65000),(7, 'Tim', 'IT', 75000),(8, 'Priya', 'Finance', 85000);The EMPLOYEES table obtained is as follows
The records are evenly distributed among four partitions based on the "id" column. You can verify the partition status using the following SELECT query
SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME='EMPLOYEES';The table obtained is as follows
Key Partitioning
The MySQL key partitioning is used to divide the table data into partitions based on the values of the primary key or a unique key.
Example
In the following query, we are creating a table with the name PERSON with Key partitioning on the "id" column. We have divided the table into four partitions, and the primary key is "id"
CREATE TABLE PERSON ( id INT NOT NULL, name VARCHAR(50) NOT NULL, email VARCHAR(50) NOT NULL, address VARCHAR(100) NOT NULL, PRIMARY KEY (id) ) PARTITION BY KEY(id) PARTITIONS 4;Here, we are inserting rows into the above-created table
INSERT INTO PERSON VALUES (1, 'Krishna', 'Krishna@tutorialspoint.com', 'Ayodhya'),(2, 'Kasyap', 'Kasyap@tutorialspoint.com', 'Ayodhya'),(3, 'Radha', 'Radha@tutorialspoint.com', 'Ayodhya'),(4, 'Sarah', 'Sarah@tutorialspoint.com', 'Sri Lanka'),(5, 'Sita', 'Sita@tutorialspoint.com', 'Sri Lanka'),(6, 'Arjun', 'Arjun@tutorialspoint.com', 'India'),(7, 'Hanuman', 'Hanuman@tutorialspoint.com', 'Sri Lanka'),(8, 'Lakshman', 'Lakshman@tutorialspoint.com', 'Sri Lanka');Following is the PERSON table obtained
Again, the data is evenly distributed among partitions based on the "id" column, and you can verify the partition status using the query given below
SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME='PERSON';The output obtained is as shown below
MySQL Sub-partitioning
The MySQL subpartitioning is used to further divide partitions based on another column, often used in conjunction with other partitioning methods like RANGE or HASH.
Example
Let us create a CUSTOMER_ORDERS table with RANGE partitioning on the "order_date" column, and then we will subpartition by hashing on the month of "order_date"
CREATE TABLE CUSTOMER_ORDERS ( order_id INT NOT NULL, customer_name VARCHAR(50) NOT NULL, order_date DATE NOT NULL, order_status VARCHAR(20) NOT NULL ) PARTITION BY RANGE (YEAR(order_date)) SUBPARTITION BY HASH(MONTH(order_date)) SUBPARTITIONS 2( PARTITION p0 VALUES LESS THAN (2022), PARTITION p1 VALUES LESS THAN (2023), PARTITION p2 VALUES LESS THAN (2024));Here, we are inserting rows into the above-created table
INSERT INTO CUSTOMER_ORDERS VALUES (1, 'John', '2021-03-15', 'Shipped'),(2, 'Bob', '2019-01-10', 'Delivered'),(3, 'Johnson', '2023-01-10', 'Delivered'),(4, 'Jake', '2020-01-10', 'Delivered'),(5, 'Smith', '2022-05-01', 'Pending'),(6, 'Rob', '2023-01-10', 'Delivered');Following is the CUSTOMERS_ORDERS table obtained
You can display the CUSTOMER_ORDERS table and verify the partition status using the following query
SELECT PARTITION_NAME, TABLE_ROWS FROM INFORMATION_SCHEMA.PARTITIONS WHERE TABLE_NAME='CUSTOMER_ORDERS';Following is the table obtained