Cloudera launches in-memory analyzer for Hadoop
- 04 February, 2014 20:10
Hadoop distributor Cloudera has released a commercial edition of the Apache Spark program, which analyzes data in real time from within Cloudera's Hadoop environments.
The release has the potential to expand Hadoop's use for stream processing and faster machine learning.
"Data scientists love Spark," said Matt Brandwein, Cloudera director of product marketing.
Spark does a good job at machine learning, which requires multiple iterations over the same data set, Brandwein said.
"Historically, you'd do that stuff with MapReduce, if you're using Hadoop. But MapReduce is really slow," Brandwein said, referring to how the MapReduce framework requires many multiple reads and writes to disk to carry out machine learning duties. Spark can do this task while the data is still in working memory. Maintainers of the software claim that Spark can run programs up to 100 times faster than Hadoop itself, thanks to its in-memory design model.
Spark is also good at stream processing, in which it can monitor a constant flow of data and carry out certain functions if certain conditions are met.
Stream processing, for instance, could be applied to fraud management and security event management. "In those cases, you're analyzing real-time data off the wire to detect any anomalies and take action," Brandwein said. The data can also be off-loaded to the Hadoop file system for further interactive and deeper batch-processing analysis.
First developed at University of California at Berkeley, Apache Spark provides a way to load streaming data into the working memory of a cluster of servers, where it can be queried in real-time. It has no upper limit of how many servers, or how much memory, it can use.
It relies on the latest version of Hadoop data-processing network, which uses YaRN (Yet another Research Negotiator). Spark does not require the MapReduce framework though, which operates in batch mode. It has APIs (application programming Interfaces) for Java, Scala and Python. It can natively read data from the HDFS (Hadoop File System), the HBase Hadoop database and the Cassandra data store.
The Apache Spark project has over 120 developers who have contributed to the project, and the technology has been used by Yahoo, Intel, as well as a number of other, smaller, companies. DataBricks, which offers its own commercial version Spark, offers support for Spark on behalf of Cloudera users.
The idea of applying Hadoop-style analysis to streaming data is not a new one. Twitter maintains Storm, a set of open source software it uses for analyzing messages.
In addition to Spark, Cloudera also announced that it has repackaged its commercial Hadoop offering into three separate packages: the Basic edition, the Flex edition and the Enterprise Hub Edition. The Enterprise Hub addition bundles all of the additional tools that Cloudera has integrated with Hadoop, including HBase, Spark, backup capabilities, and the Impala SQL analytic edition. The Flex edition allows the user to pick one additional tool in addition to core Hadoop.
Cloudera has also renamed its Cloudera Standard edition to Cloudera Express.