+1 on what Brane said. Considering that the Accelerator code is already in the open and licensed under ASL2.0 I don't see any issues with just adding it to the existing Ignite code base. As far as the PPMC is Ok with handling the additional dev. load.
With my Apache Bigtop hat on, I'd say it'd be even better as we'll be able to use the Apache Incubator project instead of the current github source. Cos On Tue, Dec 30, 2014 at 10:56AM, Dmitriy Setrakyan wrote: > Hi, > > I want to open up a discussion about adding GridGain Hadoop Accelerator as > a feature of Apache Ignite. > > As some of you may know, Hadoop Accelerator is now offered as a part of > GridGain open source edition. It is built on top of Ignite In-Memory Data > Fabric technology and provides plug-n-play acceleration for Hadoop. It also > recently has been integrated with Apache BigTop. > > The acceleration is achieved by providing the following Hadoop components > in memory: > > - IgniteFS, in-memory Hadoop-compliant file system, which natively plugs > into Hadoop, and is built on top of Ignite data grid. > - Ignite MapReduce, very fast Hadoop MapReduce implementation, which is > built on top of Ignite computation framework. > > I anticipate that some questions will arise around how Ignite Hadoop > Accelerator is different from Apache Spark. The reality is that they are > very different. > > One of the main differences is that Ignite Hadoop Accelerator will offer > acceleration of the existing Hadoop MapReduce computations that run > natively on Hadoop, while Spark essentially takes you off of Hadoop > MapReduce into its own DSL. Additionally, because of the fast MapReduce > implementation, Ignite Hadoop Accelerator will also accelerate native Hive > queries, while Spark provides its own SQL engine. > > More information about Hadoop Accelerator can be found here: > http://hadoop.gridgain.org/ > > Please let me know your thoughts.
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