Enabling Growth Through Performance
Enabling Growth Through Performance

SQL Server Data Compression

A Deep Dive into Page and Row Compression

Introduction

As businesses continue to amass large quantities of data, efficient data management becomes increasingly important. One critical aspect of data management is data compression, which can help reduce storage costs, improve query performance, and optimize resource usage. Microsoft SQL Server, an industry-leading relational database management system (RDBMS), provides robust data compression capabilities as an Enterprise feature, allowing organizations to effectively manage and utilize their data. In this article, we will explore the two primary data compression methods in SQL Server – page compression and row compression – and discuss their differences and potential benefits.

SQL Server Data Compression

Data compression in SQL Server is an Enterprise Edition feature that allows you to store data more efficiently, reducing storage space and improving I/O performance. By reducing the number of pages that must be read from or written to disk, data compression can reduce the overall I/O workload and help accelerate query execution. SQL Server offers two data compression methods: page compression and row compression.

Page Compression

Page compression is a more aggressive and comprehensive compression technique compared to row compression. It works at the page level, meaning that it compresses an entire database page (8 KB) instead of individual rows. Page compression uses three main techniques: Row Compression, Prefix Compression, and Dictionary Compression.

Row Compression: As part of the page compression process, SQL Server first applies row compression to reduce the storage space required for each row within the page.

Prefix Compression: This technique identifies common column prefixes within each page and replaces them with a single instance, further reducing the storage space.

Dictionary Compression: Lastly, SQL Server looks for repeating values across all columns in the page and creates a shared dictionary of unique values. It then replaces the original values with references to the dictionary, leading to a significant reduction in storage space.

Page compression can lead to significant space savings and improved I/O performance, especially when dealing with large tables with repetitive data. However, it requires additional CPU overhead for compression and decompression, which can offset some of the performance gains in certain scenarios.

Row Compression

Row compression focuses on compressing data at the row level, storing fixed-length data types as variable-length data types to reduce storage requirements. It does not employ the prefix and dictionary compression techniques used in page compression.

Row compression eliminates the storage of unused bits, null values, and zeroes, optimizing storage for each row. While it generally results in smaller storage savings compared to page compression, it also requires less CPU overhead during compression and decompression. This makes row compression an attractive option for workloads where CPU resources are a limiting factor.

It compresses data by optimizing the storage of variable-length data types such as VARCHAR, NVARCHAR, and VARBINARY. This is achieved by storing the data in its most compact form, eliminating any unused or unnecessary space. For example, if a VARCHAR column has a maximum length of 50 characters, but only 10 characters are used in a specific row, row compression will ensure that only the space required for the 10 characters is used.

It’s important to note that row compression does not provide any benefits for fixed-length data types such as INT or CHAR, as these data types always use the same amount of storage space regardless of their content.

SQL Server Data Compression: An Enterprise Feature

Both page and row compression are exclusive features of SQL Server’s Enterprise edition. This means that organizations must invest in the Enterprise version to access these advanced compression techniques. While the cost of the Enterprise edition may be higher, the potential storage and performance benefits provided by data compression can justify the investment for many organizations with large-scale data processing needs.

Monitoring

Monitoring is an important aspect of using SQL Server data compression, most notably CPU time. Using tools such as the Vroom Performance Suite you can monitor your processers and plan capacity accordingly to avoid the negative impact of running without the cycles necessary for your workload.

Examples:

Enable row compression on an existing table:

ALTER TABLE dbo.YourTableName
REBUILD WITH (DATA_COMPRESSION = ROW);

Enable row compression on a specific index in an existing table:

ALTER INDEX YourIndexName
ON dbo.YourTableName
REBUILD WITH (DATA_COMPRESSION = ROW);

Disable row compression on an existing table:

ALTER TABLE dbo.YourTableName
REBUILD WITH (DATA_COMPRESSION = NONE);

Disable row compression on a specific index in an existing table:

ALTER INDEX YourIndexName
ON dbo.YourTableName
REBUILD WITH (DATA_COMPRESSION = NONE);

Enable page compression on an existing table:

ALTER TABLE dbo.YourTableName
REBUILD WITH (DATA_COMPRESSION = PAGE);

Enable page compression on a specific index in an existing table:

ALTER INDEX YourIndexName
ON dbo.YourTableName
REBUILD WITH (DATA_COMPRESSION = PAGE);

Disable page compression on an existing table:

ALTER TABLE dbo.YourTableName
REBUILD WITH (DATA_COMPRESSION = NONE);

Disable page compression on a specific index in an existing table:

ALTER INDEX YourIndexName
ON dbo.YourTableName
REBUILD WITH (DATA_COMPRESSION = NONE);

Keep in mind that enabling or disabling compression using the ALTER TABLE or ALTER INDEX statements requires rebuilding the table or index, which can be resource-intensive and time-consuming for large tables. Schedule these operations during maintenance windows or periods of low database activity to minimize the impact on database performance.

Conclusion

Data compression is an essential feature of SQL Server Enterprise Edition, offering organizations significant storage savings and improved query performance. By understanding the differences between page and row compression, businesses can make informed decisions about which compression method best suits their specific needs and resource constraints. As data volumes continue to grow, effective data compression will remain a critical component of successful data management strategies.

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