Although it abstracts and simplifies it all quite a bit, Dumbo still forces you to think in MapReduce, which might not be ideal if you want to implement complex data flows in a limited amount of time. Personally, I think that Dumbo still occupies a useful space within the Hadoop ecosystem, but in some cases it makes sense to work at an even higher level and use something like Pig or Hive. In fact, sometimes it makes sense to combine the two and do some parts of your data flow in Dumbo and others in Pig. To make this possible, I recently wrote a Pig loader function for sequence files that contain TypedBytesWritables, which is the file format Dumbo uses by default to store all its output on Hadoop. Here’s an example of a Pig script that reads Dumbo output:
register pigtail.jar; -- http://github.com/klbostee/pigtail
a = load '/hdfs/path/to/dumbo/output'
using fm.last.pigtail.storage.TypedBytesSequenceFileLoader()
as (artist:int, val:(listeners:int, listens:int));
b = foreach a generate artist, val.listeners as listeners;
c = order b by listeners;
d = limit c 100;
dump d;
You basically just have to specify names and types for the components of the key/value pairs and you’re good to go.
A possibly useful side-effect of writing this loader is the ability it creates of reading all sorts of file formats with Pig. Everything that Dumbo can read can also be consumed by Pig scripts now, all you have to do is write a simple Dumbo script that converts it to typed bytes sequence files:
from dumbo import run
from dumbo.lib import identitymapper
if __name__ == "__main__":
run(identitymapper)
The proper solution is of course to write custom Pig loaders, but this gets the job done too and doesn’t slow things down that much.
