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MapReduce and Spark

vision.cloudera.com

65 pointsrxin14 comments

I like Spark over Hadoop just from an interface point of view, particularly the ability to just start up a (Scala) shell and start playing around. Hadoop can be very effective, but even getting "hello world" to run requires an intimidating array of setup.

There is also a Python shell now :)

Have you tried Apache Hive? I believe it was meant to make Hadoop easier to use by way of SQL-like commands. Something like Qubole might be able to help too.

Pig is another option. It allows using SQL like commands on the grunt shell, making using Hadoop a lot easier.

Going from Hive/Pig to Spark enables substantial improvement in developers' productivity (for non-reporting/BI workloads). You can properly unit test your program, use a debugger, and have all your code in the same place in the same language (rather than in the case of Pig, write UDFs in Java and then use a pseudo-scripting language for workflow specification).

All of these are just productivity gains; not to mention the performance gains you get when you go from MapReduce to Spark.

There is Shark which is Hive implemented on top of Spark instead of Hadoop.

I have, I should've mentioned that as an option. But I find it much easier to think in Scala than in an SQL-like language.

I often read that spark avoids the costly synchronization required in mapreduce, since it uses DAG's. Can someone explain how is that achieved. If the application so demands that you can launch jobs together, that can be done even with hadoop/mapreduce. If one job requires the output of another, then the job has to wait for synchronization whether its mapreduce or DAG.

I believe it is because Spark stores data in objects called RDD (Resilient Distributed Dataset) and these are lazily evaluated. I could be wrong though.

http://spark.incubator.apache.org/docs/latest/quick-start.ht...

Spark's major benefit comes from storing the intermediate results in-memory instead of storing it in HDFS as Hadoop does. Let's say a certain query needs to run 3 mapreduce jobs A, B, C one after another. In Hadoop, there will be 3 hdfs reads and writes. With spark, there will be only 1 hdfs read (before launching A) and 1 write (after C is completed). In spark, the output of A gets stored in RAM which is read by B and so on until the final write.

The DAG used by spark represents how one job/partition of data depends on another job/partition and what methods (e.g. filter) need to be applied on the parent data to get the child data. This is useful when a node goes down and that portion of data has to be recomputed. Note that users can choose to persist some intermediate results to hdfs to avoid recomputation in case of failure.

Some interesting use cases are also described on Cloudera's developer blog, at http://blog.cloudera.com/blog/2013/11/putting-spark-to-use-f....

Although spark is nice, I'm also looking forward to mpi/orted integration with hadoop...

"Performance: Launches ~1000x faster, runs ~10x faster"

"Launch scaling: Hadoop (~N), MR+ (~logN)"

"Wireup: Hadoop (~N2), MR+ (~logN)"

http://slurm.schedmd.com/slurm_ug_2012/MapRedSLURM.pdf

What I would love to know is if Mahout works out of the box with Spark or if there's a third party library that bridges the two.

No. See MLbase http://mlbase.org

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