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In the world of databases, partitioning is a common technique used to manage large tables and indexes. It breaks down data into smaller, more manageable pieces, called partitions. Each partition can be managed and optimized independently. This article will delve into the various MySQL partitioning techniques, and how they can be implemented using technologies such as SQL Server 2016, SQL Server 2017, MySQL 5.7, DB2 11.1, and Azure SQL.

MySQL 5.7 introduced native partitioning to its system. The RANGE partitioning type allows data to be divided into partitions, each consisting of rows that fall within a given range. The syntax for this is straightforward. For instance, if you have a sales table and you want to partition it by year, you could write:

CREATE TABLE sales (
    id INT NOT NULL,
    purchase_date DATE NOT NULL
)
PARTITION BY RANGE( YEAR(purchase_date) ) (
    PARTITION p0 VALUES LESS THAN (1991),
    PARTITION p1 VALUES LESS THAN (1992),
    PARTITION p2 VALUES LESS THAN (1993)
);

SQL Server 2016 and 2017 also offer partitioning capabilities. These versions introduced the concept of partition functions and partition schemes. Partition functions define the data distribution of a table or index across partitions and partition schemes map the partitions onto filegroups. Here is an example of a partition function in SQL Server:

CREATE PARTITION FUNCTION myRangePF (int)
AS RANGE RIGHT FOR VALUES (1, 100, 200);

DB2 11.1 also supports partitioning, providing options such as range, list, and hash partitioning. However, the syntax is a bit different. For a range partitioning, for example, the syntax would look like this:

CREATE TABLE sales (
    id INT NOT NULL,
    purchase_date DATE NOT NULL
)
PARTITION BY RANGE (purchase_date)
    (STARTING '1/1/1990' ENDING '12/31/1993' EVERY 1 YEAR);

Azure SQL, the cloud-based version of SQL Server, also supports partitioning. The syntax is similar to SQL Server, with partition functions and schemes. However, Azure SQL adds the concept of elastic pools, which enable the sharing of resources across a pool of databases, and can work together with partitioning for better resource management.

Partitioning is a powerful tool for database management, providing a way to handle large data sets more effectively. Whether you're using MySQL, SQL Server, DB2, or Azure SQL, understanding partitioning techniques can help you optimize your database operations and performance.