[infinispan-dev] Hadoop and ISPN first and next steps
gustavonalle at gmail.com
Mon Jun 23 05:04:08 EDT 2014
Last week Pedro, myself and Mircea met at London to start prototyping the integration between Hadoop and ISPN.
We discussed several scenarios where Hadoop and ISPN would be able to work together, and decided to start with ISPN server as the source and/or sink for a Hadoop Map Reduce job
After creating an InputFormat and OutputFormat for ISPN , we generated some data  and run a sample job  using Hadoop v1.x, both in docker  and on a 4 node physical cluster (installed with the help of puppet )
We also run the same job in the same cluster with the same data, but using HDFS as data source and sink, so that we could verify correctness.
In this setup, each Hadoop slave runs the TaskTracker, Data node and ISPN server, and the idea was to generate a split  based on segments and redirect the map task to be executed on the nodes associated with those segments. This routing and filtering the data is still work in progress, carried on by Pedro.
- For sure optimise the current Input/OutputFormat so that it can efficiently read/write data. This will allow ISPN to become part of the Hadoop ecosystem and easier to integrate it with tools like Apache Hive  or Pig .
- Investigate closer integration for Map Reduce, potentially usable in library mode. As you might know, YARN (the overhaul of Hadoop architecture) is not only about Map Reduce, and it offers more extensions points than Hadoop Map Reduce v1
- I read with great interest the Spark paper . Spark provides a DSL with functional language constructs like map, flatMap and filter to process distributed data in memory. In this scenario, Map Reduce is just a special case achieved by chaining functions . As Spark is much more than Map Reduce, and can run many machine learning algorithms efficiently, I was wondering if we should shift attention to Spark rather than focusing too much on Map Reduce. Thoughts?
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