Tuesday Nov 29, 2011

Announcing Berkeley DB Java Edition Major Release

Berkeley DB Java Edition 5.0 was just released. There are a number of new features, enhancements, and options in there that our users have been asking for. Chief among them is a new class called DiskOrderedCursor, which greatly increases performance of systems using spinning platter magnetic hard drives. A number of users expressed interest in this feature, including Alex Feinberg of LinkedIn. Berkeley DB Java Edition is part of Project Voldemort, a distributed key/value database used by LinkedIn.

There have been many other improvements and optimizations. Concurrency is significantly improved, as is the performance of update and delete operations. New and interesting methods include Environment.preload, which allows multiple databases to be preloaded simultaneously. New Cursor methods enable for more effective searching through the database.

We continue to enhance Berkeley DB Java Edition’s High Availability as well. One new feature is the ability to open a replicated node read-only when the master is unavailable. This can allow critical systems to continue offering some functionality, even during a network or master node failure.

There’s a lot more in release 5.0. I encourage you to take a look at the extensive changelog yourself. As always, you can download the new release and try it out here:

http://www.oracle.com/technetwork/database/berkeleydb/downloads/index.html

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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