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Microsoft SQL server provides a potent feature known as Data Compression, which can help you to streamline your data storage and improve the performance of your database. Today, we’ll delve into this feature, examining how it works and how you can implement it in your own systems.
What is SQL Server Data Compression?
Data compression in SQL Server is a method of reducing the disk I/O needed to retrieve data from the database. This can lead to significant performance improvements, especially for databases with huge data volumes. Microsoft offers two types of data compression – Row Compression and Page Compression.
How to Implement Data Compression
Let’s start off by assuming we have an existing table in our database known as SalesOrderDetail. If we want to implement row compression on this table, we can do so using the following SQL code:
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ALTER TABLE SalesOrderDetail REBUILD PARTITION = ALL WITH (DATA_COMPRESSION = ROW); |
The above statement will rebuild the table with Row data compression enabled. Similarly, you can use the same ALTER TABLE command to enable Page compression, simply by replacing DATA_COMPRESSION = ROW with DATA_COMPRESSION = PAGE like so:
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ALTER TABLE SalesOrderDetail REBUILD PARTITION = ALL WITH (DATA_COMPRESSION = PAGE); |
Estimating Compression Savings
Before enabling data compression, it’s important to estimate how much space you’ll save. You can do this using the sp_estimate_data_compression_savings stored procedure. Here’s a sample command:
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EXEC sp_estimate_data_compression_savings 'Sales', 'SalesOrderDetail', NULL, NULL, 'ROW'; |
Here, the ‘Sales’ parameter refers to the schema name, ‘SalesOrderDetail’ refers to the table name, and ‘ROW’ at the end signifies the type of data compression we want to estimate for. Replace ‘ROW’ with ‘PAGE’ to estimate page compression savings.
Conclusion
Properly implemented, SQL Server Data Compression can work wonders for your database performance. Not only does it reduce storage costs, but it can also decrease the I/O overhead during querying, potentially making your database operations both faster and more cost-effective. As always, remember to conduct thorough testing in your development environment before introducing significant changes to your production DBs.