Configure data compression

Configuring data compression is an essential aspect of administering Microsoft Azure SQL solutions. Compression helps optimize storage and enhance query performance by reducing the storage footprint of your database. In this article, we will explore how to configure data compression in Azure SQL databases.

Row-Level Compression

Row-level compression compresses data at the individual row level and is suitable for tables with repetitive data patterns. This compression type eliminates redundant portions within a column, resulting in storage savings. To enable row-level compression for a table, you can use the following T-SQL statement:

ALTER TABLE [Schema_Name].[Table_Name] REBUILD WITH (DATA_COMPRESSION = ROW);

Replace [Schema_Name] with the target schema name and [Table_Name] with the name of the table you want to compress.

Page-Level Compression

Page-level compression compresses data at the page level. It identifies repetitive patterns across multiple rows within a page and removes redundant information. Page-level compression provides higher compression ratios than row-level compression but requires more CPU overhead during data retrieval and modification operations. To enable page-level compression for a table, use the following T-SQL statement:

ALTER TABLE [Schema_Name].[Table_Name] REBUILD WITH (DATA_COMPRESSION = PAGE);

Again, replace [Schema_Name] and [Table_Name] with appropriate values.

Compression for Indexes

In addition to table compression, you can apply compression to indexes in Azure SQL databases. Index compression helps reduce the storage footprint and improves query performance. To configure compression for an index, you can leverage the following T-SQL statement:

ALTER INDEX [Index_Name] ON [Schema_Name].[Table_Name] REBUILD WITH (DATA_COMPRESSION = [ROW | PAGE]);

Replace [Index_Name], [Schema_Name], and [Table_Name] with the actual names in your database.

It’s worth noting that determining the suitable compression type (row or page) depends on your specific workload characteristics. While row-level compression provides better performance for transactional workloads, page-level compression is more appropriate for data warehousing scenarios or read-intensive workloads.

To evaluate the effectiveness of compression on your tables and indexes, you can utilize the sys.dm_db_index_physical_stats dynamic management view. It provides information about the amount of space occupied by a table or index before and after compression.

In summary, data compression plays a vital role in optimizing storage and improving query performance in Azure SQL databases. By efficiently compressing your data at the row or page level, you can reduce storage costs, enhance query speed, and utilize system resources effectively. Consider the nature of your workload and apply compression accordingly to achieve the best results.