does anyone find Cascading for Hadoop Map Reduce useful?

Question

I\'ve been trying Cascading, but I cannot see any advantage over the classic map reduce approach for writing jobs.

Map Reduce jobs gives me more freedom and Cascading se

Answer 1

Keeping in mind I'm the author of Cascading...

My suggestion is to use Pig or Hive if they make sense for your problem, Pig especially.

But if you are in the business of data, and not just poking around your data for insights, you will find the Cascading approach makes much more sense for most problems than raw MapReduce.

Your first obstacle with raw MapReduce will be thinking in MapReduce. Trivial problems are simple in MapReduce, but its much easier to develop complex applications if you can work with a model that more easily maps to your problem domain (filter this, parse that, sort those, join the rest, etc).

Next you will realize that a normal unit of work in Hadoop consists of multiple MapReduce jobs. Chaining jobs together is a solvable problem but it should not leak into your application domain level code, it should be hidden and transparent.

Further, you will find refactoring and creating re-usable code much harder if you have to continually move functions between mappers and reducers. or from mappers to the previous reducer to get an optimization. Which leads to the issue of brittleness.

Cascading believes in failing fast as possible. The planner attempts to resolve and satisfy dependencies between all those field names before the Hadoop cluster is even engaged in work. This means 90%+ of all issues will be found before waiting hours for your job to find it during execution.

You can alleviate this in raw MapReduce code by creating domain objects like Person or Document, but many applications don't need all the fields down stream. Consider if you needed the average age of all males. You do not want to pay the IO penalty of passing a whole Person around the network when all you need is a binary gender and numeric age.

With fail fast semantics and lazy binding of sinks and sources, it becomes very easy to build frameworks on Cascading that themselves create Cascading flows (which become many Hadoop MapReduce jobs). A project I'm currently involved with ends up with 100's of MapReduce jobs per run, many created on the fly mid run based on feedback from the data being processed. Search for Cascalog to see an example of a Clojure based framework for simply creating complex processes. Or Bixo for a web mining toolkit and framework that's far easier to customize than Nutch.

Finally Hadoop is never used alone, that means your data is always pulled from some external source and pushed to another after processing. The dirty secret about Hadoop is that it is a very effective ETL framework (so its silly to hear ETL vendors talk about using their tools to push/pull data onto/from Hadoop). Cascading eases this pain somewhat by allowing you to write your operations, applications, and unit tests independent of the integration end-points. Cascading is used in production to load systems like Membase, Memcached, Aster Data, Elastic Search, HBase, Hypertable, Cassandra, etc. (Unfortunately not all the adapters have been released by their authors.)

If you will, please send me a list of the issues your are experiencing with the interface. I am constantly looking for better ways to improve the API and documentation, and the user community is always around to help.