Apache Hadoop adoption is accelerating among enterprises and advanced computing environments as the project, related projects, and ecosystem continue to expand. While there were valid reasons to avoid the 1.x versions, skeptics are reconsidering since Hadoop 2 (particularly the latest 2.2.0 version) provides a viable choice for a wider range of users and uses.
“The Hadoop 1.x generation was not easy to deploy or easy to manage,” said Juergen Urbanski, former chief technologist of T-Systems, the IT consulting division of Deutsche Telecom. “The many moving parts that make up a Hadoop cluster were difficult for users to configure. Fortunately, Hadoop 2 fills in many of the gaps. Manageability is a key expectation, particularly for the more critical business use cases.”
Hadoop 2.2.0 adds the YARN resource-management framework to the core set of Hadoop modules, which include the Hadoop Common set of utilities, the Hadoop Distributed File System (HDFS), and Hadoop MapReduce for parallel processing. Other improvements include enhancements to HDFS, binary compatibility for Map/Reduce applications built on Hadoop 1.x, and support for running Hadoop on Windows.
Meanwhile, Hadoop-related projects and commercial products are proliferating along with the ecosystem. Collectively, the new Hadoop capabilities provide a more palatable and workable solution, not only for enterprise developers, business analysts and IT, but also a larger community of data scientists.
“There are many technologies that are helping Hadoop realize its potential as being a more general-purpose platform for computing,” said Doug Cutting, co-creator of Hadoop. “We started out as a batch processing system. People used it to do computations on large data sets that they couldn’t do before, and they could do it affordably. Now there’s an ever-increasing amount of data processing that organizations can do using this one platform.”
YARN expands the possibilities
The limitations of Map/Reduce were the genesis of Apache Hadoop NextGen MapReduce (a.k.a. YARN), according to Arun Murthy, release manager for Hadoop 2.
“It was apparent as early as 2008 that Map/Reduce was going to become a limiting factor because it’s just one algorithm,” he said. “If you’re trying to do things like machine learning and modeling, Map/Reduce is not the right algorithm to do it.”
Rather than replacing Map/Reduce altogether, it was supplemented with YARN to provide things like resource management and fault tolerance as base primitives in the platform, while allowing end users to do different things as they process and track the data in different ways.