Our goal with Apache Spark is very simple: provide the best platform for computation on big data. We do this through both a powerful core engine and rich libraries for useful analytics tasks. Today, we are excited to announce the release of Spark 0.9.0. This major release extends Spark’s libraries and further improves its performance and usability. Spark 0.9.0 is the largest release to date, with work from 83 contributors, who submitted over 300 patches.
Apache® Spark™ News
We are often asked how does Apache Spark fits in the Hadoop ecosystem, and how one can run Spark in a existing Hadoop cluster. This blog aims to answer these questions.
Hadoop integration has always been a key goal of Spark and YARN users have long been able to run Spark on YARN. However, up to now, it has been relatively hard to run Apache Spark on Hadoop MapReduce v1 clusters, i.e. clusters that do not have YARN installed. Typically, users would have to get permission to install Spark/Scala on some subset of the machines, a process that could be time consuming. Enter SIMR (Spark In MapReduce), which has been released in conjunction with Spark 0.8.1.
We are happy to announce the release of Apache Spark 0.8.1. In addition to performance and stability improvements, this release adds three new features. First, Spark now supports for the newest versions of YARN (2.2+). Second, the standalone cluster manager supports a high-availability mode in which it can tolerate master failures. Third, shuffles have been optimized to create fewer files, improving shuffle performance drastically in some settings.
Apache Hadoop has revolutionized big data processing, enabling users to store and process huge amounts of data at very low costs. MapReduce has proven to be an ideal platform to implement complex batch applications as diverse as sifting through system logs, running ETL, computing web indexes, and powering personal recommendation systems. However, its reliance on persistent storage to provide fault tolerance and its one-pass computation model make MapReduce a poor fit for low-latency applications and iterative computations, such as machine learning and graph algorithms.