SQL Server Central Webinar Series #24: Prepare for When Disaster Strikes
Our next webinar on July 23rd has Steve Jones presenting on some disaster stories with ideas on how you might prevent, or mitigate the effects, of those problem situations.
Our next webinar on July 23rd has Steve Jones presenting on some disaster stories with ideas on how you might prevent, or mitigate the effects, of those problem situations.
Understanding thousands of lines of code of an existing database is very time-consuming and tedious, prone to inadvertent oversight of key logic points. We are looking at advanced ways to provide greater insight into your code base while reducing the numerous demands placed on the developer. Please help us by completing this short survey to help us define the requirements.
A table does not so much 'have' a clustered index as a table 'is' a clustered index.
It is very difficult to pinpoint what ails a server, just by looking at a single snapshot of the data, or to spot retrospectively what caused the problem by examining aggregated data for the server, collected over many months. The answer? Baselines.
This metric measures the amount of memory used in the buffer cache by the largest object (based on the number of pages). It checks the sys.dm_os_buffer_descriptors to identify the object, and returns the relative percentage used. You should use this metric if you want to monitor what is in the buffer area, or if you are having performance-related disk read problems.
Cube developers often flippantly use the term XMLA in their discussion of maintaining, scripting, backing up, and restoring cubes and other SSAS objects; what exactly is XMLA and how can it be used? Check out this tip to learn more.
We don't often predict for disasters, which is good since we rarely have extensive plans for dealing with them. If we do, do we actually test our plans, or test the systems under full load?
Another in the series of articles to help you "fill in the cracks" in your T-SQL knowledge. MVP Jeff Moden shows us a super simple, high performance method to solve this timeless problem.
We're trialling a new resource for SQL beginners and we'd like to know what you think.
Sometimes recovery simply entails rerunning a failed process.
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What is the range for the result from the EDIT_DISTANCE_SIMILARITY() function in SQL Server 2025?
See possible answers