Hortonworks reignites Spark to boost Hadoop

Hortonworks puts its development expertise into bolstering Spark, Hadoop's in-memory processing system, to craft a next-generation tool

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Hortonworks bills itself as a pure open source play version of Hadoop, both in terms of what it distributes and how it invests resources back into Hadoop's core projects. In a blog post today, Hortonworks announced plans to plow its resources back into Spark, the Hadoop streamed-data, machine-learning, and in-memory data processing system.

In the post, Hortonworks says it has "outlined a set of initiatives to address some of the current challenges with the technology that will make it easier for users to consume as part of the completely open source Hortonworks Data Platform."

The task falls in two basic categories. First is more deeply integrating Spark with YARN, the technology created to replace MapReduce so that Hadoop applications can run more efficiently in parallel. Spark runs on YARN, but this "leads to a less than ideal utilization of cluster resources" in the current version of Spark, according to Hortonworks, "particularly when large datasets are involved."

Spark's big selling point is in-memory processing, so the planned work involves Spark's use of memory-handling features built into YARN. One such feature is node labels, which allows Spark applications to be tagged, so they can be automatically processed on nodes in the cluster where memory is in abundance.

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Spark provides in-memory and real-time processing for Hadoop. It works with the YARN framework, but its integration with YARN is rudimentary.

The other major category of improvement covers general Hadoop issues: security, governance, and operations. Security on Hadoop has been catch-as-catch-can for much of the product's lifetime, but a new Apache project that entered incubation earlier this year -- Apache Argus -- addresses it in a consistent manner.

Argus did not start as a community initiative; it's the open-sourced version of a commercial product, XA Secure, that Hortonworks acquired and transformed into an Apache-hosted project. The idea, as Hortonworks explained earlier this year, is to provide a centralized way to define and enforce security policy across Hadoop and all its components. This includes access controls down to the folder and file level in HDFS, and to the table and column level in Hive and HBase. But don't expect automatic Argus integration -- this project has a long road ahead for Hortonworks and everyone else contributing to the Hadoop ecosystem.

Other improvements include better debugging facilities for apps that use Spark, and integration with a YARN feature called ATS (Application Timeline Server). ATS metrics can be used for further debugging and performance improvements of Spark apps.

Spark is hardly the only in-memory processing solution offered for Hadoop, although much of the competition is proprietary. Pivotal, for instance, has an in-memory analytics system, Gemfire XD, that recently upgraded to a new version. It has some similarities with Spark, but it's aimed at those coming from a traditional database background, and it's a closed source offering. Cloudera has its own commercial distribution of Hadoop, albeit one that comes bundled with Spark instead of using a closed source substitute.

Copyright © 2014 IDG Communications, Inc.