Data Compression: Strategy, Capacity Planning and Best Practices
Hot off the presses, the SQL CAT team has just published a new whitepaper for which I had the opportunity to provide a technical review. The author is SQL CAT best practices maven Sanjay Mishra. Contributors include SQL CAT member Sunil Agarwal and architects Marcel van der Holst & Peter Carlin. Besides yours truly, tech reviewers were Stuart Ozer, Lindsey Allen, Juergen Thomas, Thomas Kejser, Burzin Patel, Mike Ruthruff, & Prem Mehra of SQL CAT as well as Joseph Sack, Cameron Gardiner, MVP Glenn Berry, Paul Randal (SQLskills.com), & David P Smith (ServiceU Corporation).
Put the Big Squeeze on Your Data
The data compression feature in the Microsoft SQL Server 2008 database software can help reduce the size of the database as well as improve the performance of I/O intensive workloads. However, extra CPU resources are required on the database server to compress and decompress the data, while data is exchanged with the application. Therefore, it is important to understand the workload characteristics when deciding which tables to compress. This white paper provides guidance on the following:
How to decide which tables and indexes to compress
How to estimate the resources required to compress a table
How to reclaim space released by data compression
The performance impacts of data compression on typical workloads
See Sanjay’s post at the SQL CAT blog For more information, refer to the whitepaper Data Compression: Strategy, Capacity Planning and Best Practices.
The first thing to do in a cardiac arrest is to take your own pulse.
—The Fat Man, House of God, Samuel Shem