Data Compression in SQL
How to Store More and Query Faster in SQL
In modern database systems, data volumes are growing rapidly, and managing storage efficiently has become a critical challenge. Data compression plays a key role in reducing storage costs, improving query performance, and optimising resource usage. By minimising the physical size of stored data, compression helps reduce disk I/O, which can significantly speed up queries (especially for large datasets).
SQL databases offer various built-in compression techniques, designed to handle different types of workloads. Some methods are optimised for transactional databases (OLTP), while others are better suited for analytical processing (OLAP). Choosing the right compression strategy depends on factors such as data structure, read vs. write frequency, and performance requirements.
In this post, we’ll explore:
What is Data Compression?
Benefits of Data Compression in SQL.
Types of Compression in SQL.
Comparison of SQL Compression Techniques.
When to Use Compression and When to Avoid.
By the end, you’ll have a solid understanding of how SQL databases handle compression and how you can apply it effectively to optimise your data storage and performance.
What is Data Compression?
Data compression is the process of reducing the physical storage space required to store data in a database. It achieves this by removing redundancy, encoding repeating values more efficiently, or restructuring data storage formats to optimise space usage. In relational databases, compression plays a crucial role in improving storage efficiency, query performance, and overall system resource utilisation.
How Compression Works in SQL Databases
Relational databases store data in tables, where each row has multiple columns. These tables are structured into pages (blocks of data read and written together), and when a query runs, the database engine retrieves the required pages from the disk.
Compression reduces the number of pages needed to store the same amount of information, which leads to:
Faster queries (less data to scan and transfer).
Lower storage costs.
Improved memory utilisation.
Compression in OLTP vs. OLAP Workloads
SQL databases typically serve two main types of workloads, and compression behaves differently in each:
OLTP (Online Transaction Processing)
Used in applications with frequent inserts, updates, and deletes (e.g., banking, e-commerce).
Compression must balance space-saving with write performance (too much compression can slow down transactions).
Row-level and page-level compression are commonly used.
OLAP (Online Analytical Processing)
Used for large-scale data analysis and reporting (e.g., dashboards, business intelligence).
Compression is highly beneficial because data is read more often than written.
Columnar compression (like Columnstore indexes) provides significant performance gains.
For more details about row-based and columnar storage, you can check out the below post.
How SQL Databases Handle Compression
Different relational databases have built-in compression mechanisms, often optimised for specific use cases:
SQL Server: Supports row, page, and column store compression.
PostgreSQL: Uses TOAST (The Oversized-Attribute Storage Technique) for large data types.
MySQL: Supports compressed InnoDB tables using zlib compression.
Oracle: Implements Hybrid Columnar Compression (HCC) for analytical workloads.
Benefits of Data Compression in SQL
Data compression is more than just a way to save disk space. It plays a vital role in improving query performance, reducing costs, and optimising resource usage in relational databases.
1. Reduced Storage Costs
Compressed data takes up less space, leading to lower disk storage requirements. This is especially valuable for:
Cloud databases (where storage costs scale with usage)
On-premise databases with limited disk capacity
Data warehouses that store large historical datasets
2. Faster Query Performance
Compression reduces I/O overhead, meaning fewer data pages need to be read from disk. This results in:
Faster SELECT queries
Reduced memory usage, allowing more data to fit into the cache
Improved backup and restore speeds
3. Optimised Disk and Network Usage
Compressed data not only takes up less disk space but also reduces network transfer times, which is very useful when:
Replicating databases across servers
Running distributed queries on cloud platforms
Performing backups and migrations
4. Improved Performance in OLAP
For data warehouses and analytics, compression is often a must-have.
Columnstore compression in SQL Server and PostgreSQL allows databases to store columnar data in a more compact format.
Run-length encoding (RLE) and dictionary compression can drastically reduce storage needs for repetitive values.