
For an instructor lead, in-depth look at learning SQL click below.
One of the ways businesses can optimize their operations and save on costs is by managing their data more effectively. SQL Server provides a powerful feature for this: Data Compression. This technology reduces the storage footprint of your data, leading to significant potential savings on storage costs.
Understanding SQL Server Data Compression
Data Compression in SQL Server is a feature that optimizes the storage of data in tables or indexes. It works by reducing the amount of space needed to store data, thereby reducing I/O while retrieving data from the disk. The two forms of compression available are Row and Page compression.
Row and Page Compression
Row compression changes the format of storage to use fewer bytes. It reduces the metadata associated with storing the individual columns in a record.
Page compression, on the other hand, minimizes the data redundancy that exists in the storage of several rows of data on a page. Page compression includes row compression.
Enabling Data Compression
To enable compression, you use the ALTER TABLE or ALTER INDEX statement with the REBUILD PARTITION or ALL keyword with the desired compression setting.
1 2 3 4 |
ALTER TABLE tableName REBUILD PARTITION = ALL WITH (DATA_COMPRESSION = ROW | PAGE); |
Example: Enable Page Compresson
1 2 3 4 5 |
ALTER TABLE SalesOrderDetail REBUILD PARTITION = ALL WITH (DATA_COMPRESSION = PAGE); |
Verifying Compression Settings
After you’ve enabled compression, you can check the compression settings of a table or index using the system function sys.partitions.
1 2 3 4 5 6 7 8 |
SELECT OBJECT_NAME(object_id) AS TableName, index_id, partition_number, data_compression_desc AS CompressionType FROM sys.partitions WHERE OBJECT_NAME(object_id) = 'YourTableName'; |
Conclusion
SQL Server’s Data Compression feature is a powerful tool in the optimization of data storage. By reducing storage costs and improving performance, it contributes to an increase in your bottom line and overall efficiency of operations. However, do note that due to CPU overheads, proper testing should be taken before implementing it in a production environment.