Cloudera Developer Blog · Whirr Posts
In this new installment of our “Meet the Project Founder” series, meet Tom White, founder of Apache Whirr, PMC Member for multiple other projects (Apache Hadoop, Apache Avro, Apache Bigtop, Apache Sqoop), and author of O’Reilly Media’s best-selling book, Hadoop: The Definitive Guide.
What led you to your project idea(s)?
This was post was originally published by U.C. Berkeley AMPLab developer (and former Clouderan) Matt Massie, on his personal blog. Matt has graciously permitted us to re-publish here for your convenience.
Note: The post below is valid for Impala version 0.6 only and is not being maintained for subsequent releases. To deploy Impala 0.7 and later using a much easier (and also free) method, use this how-to.
Cloudera Impala provides fast, interactive SQL queries directly on your Apache Hadoop data stored in HDFS or Apache HBase.
For several good reasons, 2013 is a Happy New Year for Apache Hadoop enthusiasts.
In 2012, we saw continued progress on developing the next generation of the MapReduce processing framework (MRv2), work that will bear fruit this year. HDFS experienced major progress toward becoming a lights-out, fully enterprise-ready distributed filesystem with the addition of high availability features and increased performance. And a hint of the future of the Hadoop platform was provided with the Beta release of Cloudera Impala, a real-time query engine for analytics across HDFS and Apache HBase data.
Let’s look at the highlights of the 2012 developments around projects supported by Cloudera.
Apache Hadoop Releases
Note (added July 8, 2013): The information below is deprecated; we suggest that you refer to this post for current instructions.
Today we bring you one user’s experience using Apache Whirr to spin up a CDH cluster in the cloud. This post was originally published here by George London (@rogueleaderr) based on his personal experiences; he has graciously allowed us to bring it to you here as well in a condensed form. (Note: the configuration described here is intended for learning/testing purposes only.)
I’m going to walk you through a (relatively) simple set of steps that will get you up and running MapReduce programs on a cloud-based, six-node distributed Apache Hadoop/Apache HBase cluster as fast as possible. This is all based on what I’ve picked up on my own, so if you know of better/faster methods, please let me know in comments!
We are happy to announce the general availability of CDH3 update 5. This update is a maintenance release of CDH3 platform and provides a considerable amount of bug-fixes and stability enhancements. Alongside these fixes, we have also included a few new features, most notable of which are the following:
Apache Whirr release 0.7.0 is now available. It includes changes covering over 50 issues, four of which were considered blockers. Whirr is a tool for quickly starting and managing clusters running on cloud services like Amazon EC2. This is the first Whirr release as a top level Apache project (previously releases were under the auspices of the Incubator). In addition to improving overall stability some of the highlights are described below: