Partitioning in SQL

-- MySQL Range Partitioning Example
CREATE TABLE Employees (
    id INT,
    name VARCHAR(100),
    salary INT
)
PARTITION BY RANGE (salary) (
    PARTITION p1 VALUES LESS THAN (30000),
    PARTITION p2 VALUES LESS THAN (60000),
    PARTITION p3 VALUES LESS THAN (100000)
);

• 1Divides large tables into smaller parts.
• 2Improves query performance.
• 3Each partition is stored separately.
• 4Still treated as one table logically.

• 1Range partitioning.
• 2List partitioning.
• 3Hash partitioning.
• 4Composite partitioning.

• 1Data is distributed based on key.
• 2Each partition stores subset of data.
• 3Queries scan only relevant partitions.
• 4Reduces I/O operations.

• 1Faster query performance.
• 2Easy data management.
• 3Improved scalability.
• 4Efficient maintenance.

• 1Complex setup.
• 2Requires careful design.
• 3Not useful for small tables.
• 4Possible performance issues if misused.

• 1Big data applications.
• 2Financial systems.
• 3Log management systems.
• 4Analytics platforms.

• 1Large e-commerce order tables.
• 2Financial transaction systems.
• 3Log data storage systems.
• 4Analytics and reporting systems.
• 5Time-based data management.

• 1Over-partitioning small tables.
• 2Choosing wrong partition key.
• 3Ignoring query patterns.
• 4Poor maintenance of partitions.

• 1Choose partition key wisely.
• 2Partition large tables only.
• 3Align partitions with query filters.
• 4Monitor performance regularly.