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Data in Memory Important for Deep Data Analytics
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It still takes several orders of magnitude longer to read or write data to a magnetic disk when compared to accessing it directly in memory.
In-memory databases are becoming increasingly popular and an ever-more important factor in performance-critical activities such as stream processing and deep data analytics. In this DataBase Journal article, Julian Stuhler takes a look at the world of in-memory databases: a technology that’s both reassuringly familiar and intriguingly novel at the same time.
"The basic concept of data in memory is certainly nothing new. Early hard disks were slow by today’s standards, but even with the increased rotational speeds and bit density of today’s technology it still takes several orders of magnitude longer to read or write data to a magnetic disk when compared to accessing it directly in memory. 'I/O avoidance' is therefore a major preoccupation for anyone concerned about the overall performance of an IT system, and a great way of avoiding I/O is to keep data in memory once it has been read, so that next time it’s needed it can be accessed immediately."
Read the Full Story at DataBase Journal
This article was originally published on January 31, 2011
