Wednesday Jun 27, 2012

Yammer, Berkeley DB, and the 3rd Platform

If you read the news, you know that the latest high-profile social media acquisition was just confirmed. Microsoft has agreed to acquire Yammer for 1.2 billion. Personally, I believe that Yammer’s amazing success can be mainly attributed to their wise decision to use Berkeley DB Java Edition as their backend data store. :-)

I’m only kidding, of course. However, as Ryan Kennedy points out in the video I recently blogged about, BDB JE did provide the right feature set that allowed them to reliably grow their business. Which in turn allowed them to focus on their core value add. As it turns out, their ‘add’ is quite valuable!

This actually makes sense to me, a lot more sense than certain other recent social acquisitions, and here’s why. Last year, IDC declared that we are entering a new computing era, the era of the “3rd Platform.” In case you’re curious, the first 2 were terminal computing and client/server computing, IIRC. Anyway, this 3rd one is more complicated. This year, IDC refined the concept further. It now involves 4 distinct buzzwords: cloud, social, mobile, and big data.

Yammer is a social media platform that runs in the cloud, designed to be used from mobile devices. Their approach, using Berkeley DB Java Edition with High Availability, qualifies as big data. This means that Yammer is sitting right smack in the center if IDC’s new computing era. Another way to put it is: the folks at Yammer were prescient enough to predict where things were headed, and get there first.

They chose Berkeley DB to handle their data. Maybe you should too!

Tuesday Nov 02, 2010

Berkeley DB Java Edition 4.1.6

Yesterday we released a new version of Berkeley DB Java Edition. This new release has some major enhancements for speed. BDB JE has always been as fast as the I/O + stable storage (disk) system for writes due to its write-once, append-only log-based architecture for fully durable commits (semi-durable, those which commit to operating system buffers rather than to the stable storage, operate at in-memory speeds). The issue until now was with random reads. Now, even with modest sized caches (512MB), you can experience predictable latency for random out-of-cache reads even for multi-TB databases.

This is a first in the pure-Java world. BDB JE is the only solution when you need large scale, predictable ACID storage for non-relational data. Imagine configuring your heap to 2GB and BDB JE's cache to 512MB then accessing TBs of data on disk knowing that your application will have 1.5GB of memory in the JVM to use.

Memory management and GC have always been tricky to get right when building large scale Java systems. With this release of Berkeley DB Java Edition we help take you one step closer to a predictable database in pure-Java.

Read more on Charlie Lamb's blog.

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