In-memory database technology is catching on for its performance and analytics capabilities, but what are some...
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considerations when it comes to choosing storage for such an environment? To find out, SearchStorage asked David Floyer, co-founder and chief technology officer at Marlborough, Mass.-based research and analysis firm Wikibon, to assess the hot technology of in-memory databases from the vantage point of data storage.
From a storage perspective, what is the greatest challenge associated with an in-memory database?
David Floyer: The biggest single mistake made on in-memory databases is having slow recovery times. Let's say you had a 10 TB [terabyte] database. Something goes wrong, and you have to restart. If you have to get that database in 4 KB blocks with an average latency of 20 milliseconds, it would be a minimum of 14 hours (assuming no recovery problems) before you can get your database back up again.
One of the challenges of recovering an in-memory database is that you have to refresh all that memory, and you have to get it from disk. You need a very high bandwidth recovery mechanism so that you can reload all the data you've got in [dynamic RAM]. Usually it's not an I/O problem. It's a bandwidth problem, getting all that data in recovery mode.
Still wondering about the benefits of in-memory databases? Find some additional insights in these stories:
Learn how in-memory databases can extend the possibilities of analytics
See how an in-memory database helped this bank boost productivity
How to achieve the speed you need with in-memory data grids
In-memory data catches on in the big data world
IDC analyst explains why in-memory technology is beneficial
Oracle capitalizes on in-memory technology with database search feature
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