by Svetlana Sicular | November 26, 2012 | Comments Off on What Is True In-Memory?
Database benchmarks early this millennium were secretly prophetic. Everybody who could read beyond a first paragraph of any benchmark’s write-up was chuckling : “They ran it in memory,” as if database vendors were cheating. And maybe, they were. All of them. So when everybody is cheating – it is not cheating, it is a game by new rules. Now, a decade or so later, in-memory computing goes beyond the steroid benchmarks and becomes available to the masses. A debate over dead bodies [of work] of those who were not cheating is morphing to a question “What is true in-memory?”
This question becomes contagious and affects not only database vendors but other humans too. Life-loggers, who are using technology instead of their own memory, are cheating also: they log everything about their lives, they remember things differently or, rather, do not have to remember much but just know how to search. I’ve been thinking about whether it makes sense to record everything versus simply live here and now. Is life-logging memory a luggage or a gear? How much does it help to move forward rather than get stuck in a recursive look backward? I don’t know. I falter where I firmly trod, — Tennyson, ‘In Memoriam,’ the poem, most known for the lines ‘Tis better to have loved and lost / Than never to have loved at all.
People who lost their loved ones, experience a temporary memory loss. My theory is that it happens, because there is no reason to remember when there is no one to share your memory with. Perhaps, in-memory databases are for those who want to live and share, and non in-memory – for those who lost. And with the memory loss being temporary, non in-memory databases soon will become in-memory if they want to survive in the future and keep living. As for life-logging, what is it philosophically – the future or the loss? Is it in-memory or on disk?
Read Complimentary Relevant Research
Organizing for Big Data Through Better Process and Governance
With big data past the Peak of Inflated Expectations on the Hype Cycle, organizations are addressing next-level challenges and asking,...
View Relevant Webinars
Hadoop and Spark: Understanding Open Source Opportunities and Risks
As companies build foundational data and analytics infrastructure with Spark and Hadoop, the market continues to shift and evolve in...
Comments or opinions expressed on this blog are those of the individual contributors only, and do not necessarily represent the views of Gartner, Inc. or its management. Readers may copy and redistribute blog postings on other blogs, or otherwise for private, non-commercial or journalistic purposes, with attribution to Gartner. This content may not be used for any other purposes in any other formats or media. The content on this blog is provided on an "as-is" basis. Gartner shall not be liable for any damages whatsoever arising out of the content or use of this blog.