MySQL Partitioning for Large Datasets

Introduction

In this lab, you will learn how to implement MySQL partitioning for large datasets to improve query performance and data management. The lab focuses on partitioning a table by range, specifically using the sale_date column of a sales table.

You will begin by connecting to the MySQL server and creating a sales_data database. Then, you'll create the sales table, partitioning it by the year of the sale_date into partitions for 2020, 2021, 2022, 2023, and a future partition. Subsequent steps will cover querying data from specific partitions, reorganizing partitions using ALTER TABLE, and checking the impact of partitioning on query speed.

Skills Graph

Partition a Table by Range

In this step, we will learn how to partition a table by range in MySQL. Partitioning by range is useful when you want to divide your data into logical groups based on a range of values, such as dates, IDs, or other numerical data. This can improve query performance and make data management easier.

First, let's connect to the MySQL server. Open a terminal in the LabEx VM. You should already be in the ~/project directory.

mysql -u root -p

When prompted, enter the root password. If you haven't set a root password, just press Enter.

Now, let's create a database named sales_data:

CREATE DATABASE sales_data;
USE sales_data;

Next, we'll create a table named sales with columns for sale_id, sale_date, and amount. We'll partition this table by the sale_date column.

CREATE TABLE sales (
    sale_id INT PRIMARY KEY,
    sale_date DATE NOT NULL,
    amount DECIMAL(10, 2) NOT NULL
)
PARTITION BY RANGE (YEAR(sale_date)) (
    PARTITION p2020 VALUES LESS THAN (2021),
    PARTITION p2021 VALUES LESS THAN (2022),
    PARTITION p2022 VALUES LESS THAN (2023),
    PARTITION p2023 VALUES LESS THAN (2024),
    PARTITION pFuture VALUES LESS THAN MAXVALUE
);

Let's break down this CREATE TABLE statement:

• PARTITION BY RANGE (YEAR(sale_date)): This specifies that we are partitioning the table by range, using the year of the sale_date column.
• PARTITION p2020 VALUES LESS THAN (2021): This creates a partition named p2020 that will contain all rows where the year of sale_date is less than 2021 (i.e., year 2020).
• PARTITION p2021 VALUES LESS THAN (2022): This creates a partition named p2021 that will contain all rows where the year of sale_date is less than 2022 (i.e., year 2021).
• PARTITION p2022 VALUES LESS THAN (2023): This creates a partition named p2022 that will contain all rows where the year of sale_date is less than 2023 (i.e., year 2022).
• PARTITION p2023 VALUES LESS THAN (2024): This creates a partition named p2023 that will contain all rows where the year of sale_date is less than 2024 (i.e., year 2023).
• PARTITION pFuture VALUES LESS THAN MAXVALUE: This creates a partition named pFuture that will contain all rows where the year of sale_date is greater than or equal to 2024. MAXVALUE ensures that any future years will be included in this partition.

Now, let's insert some sample data into the sales table:

INSERT INTO sales (sale_id, sale_date, amount) VALUES
(1, '2020-12-31', 100.00),
(2, '2021-01-15', 150.00),
(3, '2021-12-25', 200.00),
(4, '2022-06-01', 120.00),
(5, '2022-12-31', 180.00),
(6, '2023-03-10', 250.00),
(7, '2023-09-20', 300.00),
(8, '2024-01-01', 350.00);

To verify that the data has been inserted into the correct partitions, you can use the EXPLAIN PARTITIONS statement. For example:

EXPLAIN PARTITIONS SELECT * FROM sales WHERE sale_date BETWEEN '2021-01-01' AND '2021-12-31';

This will show you which partition(s) MySQL will use to execute the query. You should see that it only uses the p2021 partition.

You can also check the number of rows in each partition using the following query:

SELECT
    PARTITION_NAME,
    TABLE_ROWS
FROM
    INFORMATION_SCHEMA.PARTITIONS
WHERE
    TABLE_SCHEMA = 'sales_data' AND TABLE_NAME = 'sales';

This query retrieves the partition name and the number of rows in each partition for the sales table in the sales_data database.

+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p2020          |          1 |
| p2021          |          2 |
| p2022          |          2 |
| p2023          |          2 |
| pFuture        |          1 |
+----------------+------------+

This confirms that the data has been correctly partitioned based on the sale_date.

Query Data from a Specific Partition

In this step, we will learn how to query data from a specific partition in MySQL. This can be useful when you only need to retrieve data from a particular time period or range of values, as it can significantly improve query performance by limiting the amount of data that MySQL needs to scan.

First, ensure you are connected to the MySQL server and using the sales_data database. If you closed the connection in the previous step, reconnect using:

mysql -u root -p

Enter the root password if prompted. Then, select the sales_data database:

USE sales_data;

Now, let's query data from the p2021 partition. We can do this by adding a WHERE clause that filters the data based on the partitioning key. In our case, the partitioning key is the year of the sale_date.

SELECT * FROM sales WHERE YEAR(sale_date) = 2021;

This query will retrieve all rows from the sales table where the year of the sale_date is 2021. Because the table is partitioned by year, MySQL will only need to scan the p2021 partition to execute this query.

You can verify this by using the EXPLAIN PARTITIONS statement:

EXPLAIN PARTITIONS SELECT * FROM sales WHERE YEAR(sale_date) = 2021;

The output of this statement will show you which partition(s) MySQL will use to execute the query. You should see that it only uses the p2021 partition.

+----+-------------+-------+------------+------+---------------+------+---------+------+------+-----------------------------------+
| id | select_type | table | partitions | type | possible_keys | key  | key_len | ref  | rows | Extra                             |
+----+-------------+-------+------------+------+---------------+------+---------+------+------+-----------------------------------+
|  1 | SIMPLE      | sales | p2021      | ALL  | NULL          | NULL | NULL    | NULL |    2 | Using where                       |
+----+-------------+-------+------------+------+---------------+------+---------+------+------+-----------------------------------+

As you can see, the partitions column shows p2021, indicating that only the p2021 partition was accessed.

You can also query data from multiple partitions by using the IN operator in the WHERE clause:

SELECT * FROM sales WHERE YEAR(sale_date) IN (2021, 2022);

This query will retrieve all rows from the sales table where the year of the sale_date is either 2021 or 2022. MySQL will scan both the p2021 and p2022 partitions to execute this query.

Again, you can verify this using EXPLAIN PARTITIONS:

EXPLAIN PARTITIONS SELECT * FROM sales WHERE YEAR(sale_date) IN (2021, 2022);

The output will show that both p2021 and p2022 partitions are accessed.

+----+-------------+-------+---------------+------+---------------+------+---------+------+------+-----------------------------------+
| id | select_type | table | partitions    | type | possible_keys | key  | key_len | ref  | rows | Extra                             |
+----+-------------+-------+---------------+------+---------------+------+---------+------+------+-----------------------------------+
|  1 | SIMPLE      | sales | p2021,p2022   | ALL  | NULL          | NULL | NULL    | NULL |    4 | Using where                       |
+----+-------------+-------+---------------+------+---------------+------+---------+------+------+-----------------------------------+

By querying data from specific partitions, you can significantly reduce the amount of data that MySQL needs to process, leading to faster query execution times. This is especially beneficial for large tables with millions or billions of rows.

Reorganize Partitions with ALTER TABLE

In this step, we will learn how to reorganize partitions using the ALTER TABLE statement in MySQL. Reorganizing partitions can be useful for various reasons, such as merging partitions, splitting partitions, or adding new partitions.

First, ensure you are connected to the MySQL server and using the sales_data database. If you closed the connection in the previous step, reconnect using:

mysql -u root -p

Enter the root password if prompted. Then, select the sales_data database:

USE sales_data;

Let's start by adding a new partition for the year 2024. Currently, all sales data from 2024 onwards are stored in the pFuture partition. We can create a specific partition for 2024 using the ALTER TABLE statement with the ADD PARTITION clause:

ALTER TABLE sales ADD PARTITION (PARTITION p2024 VALUES LESS THAN (2025));

This statement adds a new partition named p2024 that will contain all rows where the year of sale_date is less than 2025 (i.e., year 2024). After adding the partition, we need to reorganize the pFuture partition to exclude the year 2024.

ALTER TABLE sales REORGANIZE PARTITION pFuture INTO (
    PARTITION p2025 VALUES LESS THAN (2026),
    PARTITION pFuture VALUES LESS THAN MAXVALUE
);

This command splits the pFuture partition into p2025 and a new pFuture partition. p2025 will contain data for the year 2025, and the new pFuture will contain data for 2026 and beyond.

Now, let's verify the partition structure:

SELECT
    PARTITION_NAME,
    TABLE_ROWS
FROM
    INFORMATION_SCHEMA.PARTITIONS
WHERE
    TABLE_SCHEMA = 'sales_data' AND TABLE_NAME = 'sales';

You should see the new p2024, p2025 and updated pFuture partitions in the output.

+----------------+------------+
| PARTITION_NAME | TABLE_ROWS |
+----------------+------------+
| p2020          |          1 |
| p2021          |          2 |
| p2022          |          2 |
| p2023          |          2 |
| p2024          |          1 |
| p2025          |          0 |
| pFuture        |          0 |
+----------------+------------+

Next, let's see how to merge partitions. Suppose we want to merge the p2020 and p2021 partitions into a single partition named p2020_2021. We can do this using the ALTER TABLE statement with the REORGANIZE PARTITION clause:

ALTER TABLE sales REORGANIZE PARTITION p2020, p2021 INTO (
    PARTITION p2020_2021 VALUES LESS THAN (2022)
);

This statement merges the p2020 and p2021 partitions into a new partition named p2020_2021. The VALUES LESS THAN (2022) clause specifies that this partition will contain all rows where the year of sale_date is less than 2022.

Now, let's verify the partition structure again:

SELECT
    PARTITION_NAME,
    TABLE_ROWS
FROM
    INFORMATION_SCHEMA.PARTITIONS
WHERE
    TABLE_SCHEMA = 'sales_data' AND TABLE_NAME = 'sales';

You should see that the p2020 and p2021 partitions have been replaced by the p2020_2021 partition.

+------------------+------------+
| PARTITION_NAME   | TABLE_ROWS |
+------------------+------------+
| p2020_2021       |          3 |
| p2022            |          2 |
| p2023            |          2 |
| p2024            |          1 |
| p2025            |          0 |
| pFuture          |          0 |
+------------------+------------+

Finally, let's see how to drop a partition. Suppose we want to remove the p2025 partition. We can do this using the ALTER TABLE statement with the DROP PARTITION clause:

ALTER TABLE sales DROP PARTITION p2025;

Now, let's verify the partition structure one last time:

SELECT
    PARTITION_NAME,
    TABLE_ROWS
FROM
    INFORMATION_SCHEMA.PARTITIONS
WHERE
    TABLE_SCHEMA = 'sales_data' AND TABLE_NAME = 'sales';

You should see that the p2025 partition has been removed.

+------------------+------------+
| PARTITION_NAME   | TABLE_ROWS |
+------------------+------------+
| p2020_2021       |          3 |
| p2022            |          2 |
| p2023            |          2 |
| p2024            |          1 |
| pFuture          |          0 |
+------------------+------------+

By using the ALTER TABLE statement with various clauses, you can easily reorganize your partitions to meet your changing data management needs.

Check Partition Impact on Query Speed

In this step, we will investigate how partitioning impacts query speed in MySQL. While our current dataset is small, the principles demonstrated here are crucial for understanding the benefits of partitioning with larger datasets.

First, ensure you are connected to the MySQL server and using the sales_data database. If you closed the connection in the previous step, reconnect using:

mysql -u root -p

Enter the root password if prompted. Then, select the sales_data database:

USE sales_data;

To measure query execution time, we'll use the SET profiling = 1; command. This enables profiling, which allows us to see the resources used by each query.

SET profiling = 1;

Now, let's run a query that targets a specific partition. We'll select all sales from the year 2023:

SELECT * FROM sales WHERE YEAR(sale_date) = 2023;

Next, let's run a query that would potentially scan all partitions (though, with our limited data, it might still be optimized). We'll select all sales with an amount greater than 200:

SELECT * FROM sales WHERE amount > 200;

Now, let's examine the profiling information to see the execution time of each query. We can do this using the SHOW PROFILES; command:

SHOW PROFILES;

The output will show a list of queries that were executed with profiling enabled, along with their duration.

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To get more detailed information about a specific query, use the SHOW PROFILE [query_id]; command, replacing [query_id] with the ID of the query you want to examine. You can find the query ID from the SHOW PROFILES output. For example, if the query ID for the first query was 1, you would run:

SHOW PROFILE 1;

This will show a breakdown of the query execution, including the time spent in each stage (e.g., preparing, sending data, etc.).

By comparing the profiling information for queries that target specific partitions with queries that scan all partitions, you can observe the impact of partitioning on query speed. Keep in mind that with a small dataset, the differences might be minimal. However, with larger datasets, the performance improvements from partitioning can be significant.

Finally, disable profiling:

SET profiling = 0;

This step demonstrated how to use MySQL profiling to analyze query execution time and understand the performance benefits of partitioning. Remember that the effectiveness of partitioning depends on the size of your data and the types of queries you are running.

Summary

In this lab, we learned how to partition a table by range in MySQL, focusing on improving query performance and data management. We connected to the MySQL server, created a database named sales_data, and then created a table named sales partitioned by the year of the sale_date column. This involved defining partitions for specific year ranges (2020, 2021, 2022, 2023) and a future partition for values exceeding the defined ranges.

The CREATE TABLE statement demonstrated how to use PARTITION BY RANGE to divide data based on the year of the sale_date, assigning rows to specific partitions based on their year. This partitioning strategy allows for more efficient querying and management of large datasets by logically grouping data based on date ranges.