ivanprado

That's a great idea.

You are right that it should be used the proper tool for each particular problem. And Hadoop world is harder than single machine systems (like pandas). So, you shouldn't user Hadoop if you can do the job with simpler systems.

But I have something to add. Hadoop is not only introducing new techniques for distributed storage and computation. Hadoop is also proposing a methodological change in the way a data project is approached.

I'm not talking only about doing some analytic over the data, but building an entire data driven system. A good example would be the case of building a vertical search engine, for example for classified ads around the world. You can try to build the system just using a database and some workers dealing with the data. But soon you'll find a lot of problems for managing the system.

Hadoop provides you all the storage and processing power that you want (it is matter of money). Why if you build your system in a way where you recompute always everything from the raw input data? That can be seen as something stupid: Why doing that if you can run the system with less resources?

The answer is that with this approach you can:

- Being human fail-tolerant. If somebody introduces a bug in the system, you just have to fix the code, and relaunch the computation. That is not possible with stateful systems, like those based in doing updates over a database. - Being very agile in developing evolutions. Change the whole system is not traumatic, as you just have to change the code and relaunch the process with the new code without much impact in the system. That is not something simple in database backed systems.

The following page shows how a vertical search engine would be built using Hadoop and what would it be its advantages: http://www.datasalt.com/2011/10/scalable-vertical-search-eng...

Three things differentiate Splout SQL from using Sqoop for exporting to an existing SQL database: 1) Scalability: Relational databases rarely scales, or are too expensive for big volumes of data. They don't work well with Hadoop. 2) Update isolation: In Splout SQL, database updating never affects serving queries as it is performed in a Hadoop cluster. 3) Atomicity: Datasets are deployed atomically in Splout SQL. That avoids inconsistency problems that arises in RDMS when updating existing databases.

Just for clarify, split() java function is using regexp for the split as well. The code of String.split() is:

return Pattern.compile(regex).split(this, limit);

The benchmark seems fair to me.

Pangool is not an alternative for Cascading. For example, at this point, Pangool does not help you managing workflows. If you are starting a MapReduce application, it is probably the best option to start using higher level abstractions: Cascading, Hive, Pig, etc.

But if you are thinking about learning Hadoop using the standard Hadoop API, or if you need for some particular reason to use it for your project, we recommend you to use Pangool instead.

Or if you are considering to implement another abstraction on top of Hadoop, probably using Pangool for it would also be a good idea.

In fact, what we believe is that the default Hadoop API should look like Pangool.

Hi avibryant, According to our initial benchmark (http://pangool.net/benchmark.html), secondary sorting in Cascading is slow (http://bit.ly/wTKOxo), showing a 243% performance overhead compared to an efficient implementation in MapReduce. The implementation in MapReduce has a lot of lines (http://bit.ly/yYGnGe) whereas Pangool's implementation is quite simple (http://bit.ly/x9U7Yj). A common application of secondary sort is calculating moving averages, for instance.

Hi, I'm one of the developers of Pangool. The idea of Pangool is not to be yet another higher level API on top of Hadoop but rather to pose a replacement for the low-level Hadoop Java MapReduce API. Pangool has the same performance and flexibility than that of the Java MapReduce API although it makes several things a lot easier and convenient. There is no tradeoff, just advantages. There will be cases where you'd want to use Pig or Cascading. There will be some other cases where you'd want the flexibility and efficiency of MapReduce. For those cases we conceived Pangool. Nowadays only very advanced Hadoop users could write efficiently-performing MapReduce Jobs. Pangool hides all the advanced boilerplate code needed for writing highly efficient MapReduce jobs, making things like secondary sorting or reduce-side joins extremely easy.