Cloudera Engineering Blog · MapReduce Posts
The example comprised a 4×4 matrix of letters, which doesn’t come close to the number of relationships in a large graph. To calculate the number of possible combinations, we turned off the Bloom Filter with “
-D bloom=false“. This enters a brute-force mode where all possible combinations in the graph are traversed. In a 4×4 or 16-letter matrix, there are 11,686,456 combinations, and a 5×5 or 25-letter matrix has 9,810,468,798 combinations.
Graph theory is a growing part of Big Data. Using graph theory, we can find relationships in networks.
MapReduce is a great platform for traversing graphs. Therefore, one can leverage the power of an Apache Hadoop cluster to efficiently run an algorithm on the graph.
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.
Ed. Note: The post below pertains to CDH 4.x only. Read this post for updates concerning CDH 5.x.
As Apache Oozie, the workflow engine for Apache Hadoop, continues to receive wider adoption from our customers and the community, we’re seeing patterns with respect to the biggest challenges for users. One such point of difficulty is setting up and using Oozie’s ShareLib for allowing JARs to be shared by different workflows. This blog post is intended to help you with those tasks (for CDH 4.x only; read about changes in CDH 5.x here.
This is the first post in series that will get you going on how to write, compile, and run a simple MapReduce job on Apache Hadoop. The full code, along with tests, is available at http://github.com/cloudera/mapreduce-tutorial. The program will run on either MR1 or MR2.
We’ll assume that you have a running Hadoop installation, either locally or on a cluster, and your environment is set up correctly so that typing “hadoop” into your command line gives you some notes on usage. Detailed instructions for installing CDH, Cloudera’s open-source, enterprise-ready distro of Hadoop and related projects, are available here: https://ccp.cloudera.com/display/CDH4DOC/CDH4+Installation. We’ll also assume you have Maven installed on your system, as this will make compiling your code easier. Note that Maven is not a strict dependency; we could also compile using Java on the command line or with an IDE like Eclipse.
The Use Case
Hue is a web interface for Apache Hadoop that makes common Hadoop tasks such as running MapReduce jobs, browsing HDFS, and creating Apache Oozie workflows, easier. (To learn more about the integration of Oozie and Hue, see this blog post.) In this post, we’re going to focus on how one of the fundamental components in Hue, Useradmin, has matured.
New User and Permission Features
User and permission management in Hue has changed drastically over the past year. Oozie workflows, Apache Hive queries, and MapReduce jobs can be shared with other users or kept private. Permissions exist at the app level. Access to particular apps can be restricted, as well as certain sections of the apps. For instance, access to the shell app can be restricted, as well as access to the Apache HBase, Apache Pig, and Apache Flume shells themselves. Access privileges are defined for groups and users can be members of one or more groups.
Changes to Users, Groups, and Permissions
Announcing the Kiji Project: An Open Source Framework for Building Big Data Applications with Apache HBase
The following is a guest post from Aaron Kimball, who was Cloudera’s first engineer and the creator of the Apache Sqoop project. He is the Founder and CTO at WibiData, a San Francisco-based company building big data applications.
Our team at WibiData has been developing applications on Hadoop since 2010 and we’ve helped many organizations transform how they use data by deploying Hadoop. HBase in particular has allowed companies of all types to drive their business using scalable, high performance storage. Organizations have started to leverage these capabilities for various big data applications, including targeted content, personalized recommendations, enhanced customer experience and social network analysis.
Today we bring you a brief interview with Alex Holmes, author of the new book, Hadoop in Practice (Manning). You can learn more about the book and download a free sample chapter here.
There are a few good Hadoop books on the market right now. Why did you decide to write this book, and how is it complementary to them?
When I started working with Hadoop I leaned heavily on Tom White’s excellent book, Hadoop: The Definitive Guide (O’Reilly Media), to learn about MapReduce and how the internals of Hadoop worked. As my experience grew and I started working with Hadoop in production environments I had to figure out how to solve problems such as moving data in and out of Hadoop, using compression without destroying data locality, performing advanced joining techniques and so on. These items didn’t have a lot of coverage in existing Hadoop books, and that’s really the idea behind Hadoop in Practice – it’s a collection of real-world recipes that I learned the hard way over the years.
Earlier this month the Apache Hadoop PMC released Apache Hadoop 2.0.2-alpha, which fixes over 600 issues since the previous release in the 2.0 series, 2.0.1-alpha, back in July. This is a tremendous rate of development, of which all contributors to the project should feel proud.
Some of the more noteworthy changes in this release include:
With CDH4 onward, the Apache Hadoop component introduced two new terms for Hadoop users to wonder about: MR2 and YARN. Unfortunately, these terms are mixed up so much that many people are confused about them. Do they mean the same thing, or not?
This post aims to clarify these two terms.