In this post I want to walk through a number of SQL Server corruption recovery techniques for when you’re out of luck, have no backups, and the usual methods don’t work. I’ll be using the AdventureWorksLT2008R2 sample database as my victim.
Sometimes you must first do evil, to do good. Such is the case when you want to hone your skills in corruption recovery of SQL Server databases.
When I initially started working on OrcaMDF I had just one goal, to gain a deeper knowledge of MDF file internals than I could through most books available.
I’m slightly late to announce this, but better late than never!
Seeing as most of my presentations are rather technical, I like to start by pointing out the fact that I have no finished higher education (though in progress), no major certifications or recognitions/awards. This leaves a perfect opportunity for me to explain, from the ground up, why I still feel qualified to be standing in front of the audience.
I’ve done it myself, worried about what to do when I exhausted my bigint identity value. I was worried that part of the LSN being a bigint – what would happen when it ran out? Would it perform an integer overflow? The answer? Worrying about exhausting the range of a bigint is not something you should spend time on. Allow me to elaborate.
Every night at around 2AM I get an email from my best friend, confirming that she’s OK. It usually looks something like this:
On my way home from the PASS Summit in Seattle, I had a layover in Amsterdam before continuing onto Copenhagen. For various reasons, we were about one and a half hours delayed, and I arrived in AMS at 9:30, my CPH flight departing at 9:35. As you’d probably guessed, I missed my flight.
In a moment of weakness I pleged to make an absolute fool of myself for this months Meme Monday. I wish I could say that this happened 20 years ago, when I was but a young grasshopper. To my disgrace, this happened fewer years ago than I’d like to admit.
In this post I’ll take a practical approach at talking about what SQL Server Mirroring is, the advantages and considerations that follows.
After testing my code through based on JP’s comments, I’ve realized my implementation was way too naïve and cannot be used for most datasets. For a correct weighted random implementation, see Dems’ answer on StackOverflow.
Update: As noted in the comments, this method does not actually round, it truncates the datetime value.