1. Why Scalding
Needs Scala
A Look at Scoobi and Scalding
Scala DSLs for Hadoop
Scoobi
@agemooij

2. Obligatory “About Me” Slide

3. Rocks!

4. But programming
kinda
Sucks!

5. Hello World Word Count
using
Hadoop MapReduce

6. Split lines into words
Turn each word into a Pair(word, 1)
Group by word (?)
For each word, sum the 1s to get the total

7. Lots of small unintuitive
Mapper and Reducer
Classes
Lots of Hadoop intrusiveness
(Context, Writables, Exceptions, etc.)
Low level glue code
Actually runs the code on the cluster

8. This does not make me a
happy Hadoop developer!
Especially for things that are a little bit more complicated than counting words
• Unintuitive, invasive programming model
• Hard to compose/chain jobs into real, more
complicated programs
• Lots of low-level boilerplate code
• Branching, Joins, CoGroups, etc. hard to
implement

9. What Are the Alternatives?

10. Counting Words using Apache Pig
Nice!
Already a lot better, but anything more complex gets
hard pretty fast.
Pig is hard to customize/extend
Handy for quick exploration of data!
And the same goes for Hive

11. package cascadingtutorial.wordcount;
/**
Very powerful!
* Wordcount example in Cascading
*/ Record Model
public class Main
{
Pipes & Filters
public static void main( String[] args )
{
String inputPath = args[0];
Joins & CoGroups
String outputPath = args[1];
Scheme inputScheme = new TextLine(new Fields("offset", "line"));
Scheme outputScheme = new TextLine();
Tap sourceTap = inputPath.matches( "^[^:]+://.*") ?
new Hfs(inputScheme, inputPath) : Not very intuitive
new Lfs(inputScheme, inputPath);
Tap sinkTap = outputPath.matches("^[^:]+://.*") ?
new Hfs(outputScheme, outputPath) : Strange new abstraction
new Lfs(outputScheme, outputPath);
Pipe wcPipe = new Each("wordcount",
Lots of boilerplate code
new Fields("line"),
new RegexSplitGenerator(new Fields("word"), "s+"),
new Fields("word"));
wcPipe = new GroupBy(wcPipe, new Fields("word"));
wcPipe = new Every(wcPipe, new Count(), new Fields("count", "word"));
Properties properties = new Properties();
FlowConnector.setApplicationJarClass(properties, Main.class);
Flow parsedLogFlow = new FlowConnector(properties)
.connect(sourceTap, sinkTap, wcPipe);
parsedLogFlow.start();
parsedLogFlow.complete();
}
}

12. Meh... I’m lazy
I want more power with less work!

13. How would we
count words in
plain Scala?
(My current language of choice)

14. Nice!
Familiar, intuitive
What if...?

15. But that code doesn’t
scale to my cluster!
Or does it?
Meanwhile at Google...

16. Introducing
Scoobi & Scalding
Scala DSLs for Hadoop MapReduce
NOTE:
My relative familiarity
with either platform:
Scalding
5%
Scoobi
95%

17. http://github.com/nicta/scoobi
A Scala library that
implements a higher level
programming model for
Hadoop MapReduce

18. Counting Words using Scoobi
Split lines into words
Turn each word into a Pair(word, 1)
Group by word
For each word, sum the 1s to get the total
Actually runs the code on the cluster

19. Scoobi is...
• A distributed collections abstraction:
• Distributed collection objects abstract data in HDFS
• Methods on these objects abstract map/reduce
operations
• Programs manipulate distributed collections objects
• Scoobi turns these manipulations into MapReduce jobs
• Based on Google’s FlumeJava / Cascades
• A source code generator (it generates Java code!)
• A job plan optimizer
• Open sourced by NICTA
• Written in Scala (W00t!)

20. DList[T]
• Abstracts storage of data and files on HDFS
• Calling methods on DList objects to transform and
manipulate them abstracts the mapper, combiner,
sort-and-shuffle, and reducer phases of MapReduce
• Persisting a DList triggers compilation of the graph
into one or more MR jobs and their execution
• Very familiar: like standard Scala Lists
• Strongly typed
• Parameterized with rich types and Tuples
• Easy list manipulation using typical higher order
functions like map, flatMap, filter, etc.

21. DList[T]

22. IO
• Can read/write text files, Sequence files and Avro files
• Can influence sorting (raw, secondary)
Serialization
• Serialization of custom types through Scala type
classes and WireFormat[T]
• Scoobi implements WireFormat[T] for primitive types,
strings, tuples, Option[T], either[T], Iterable[T], etc.
• Out of the box support for serialization of Scala case
classes

23. IO/Serialization I

24. IO/Serialization II
For normal (i.e. non-case) classes

25. Further Info
Version 0.4 released today (!)
• Avro, Sequence Files
• Materialized DObjects
• DList reduction methods (product, min,
etc.)
• Vastly improved testing support
• Less overhead
• Much more
http://nicta.github.com/scoobi/
scoobi-dev@googlegroups.com
scoobi-users@googlegroups.com

26. Scalding!
http://github.com/twitter/scalding
A Scala library that
implements a higher level
programming model for
Hadoop MapReduce
Cascading

27. Counting Words using Scalding

28. Scalding is...
• A distributed collections abstraction
• A wrapper around Cascading (i.e. no source code
generation)
• Based on the same record model (i.e. named fields)
• Less strongly typed
• Uses Kryo Serialization
• Used by Twitter in production
• Written in Scala (W00t!)

29. Further Info
Current version: 0.5.4
http://github.com/twitter/scalding
https://github.com/twitter/scalding/wiki
@scalding
cascading-user@googlegroups.com
http://blog.echen.me/2012/02/09/movie-recommendations-and-more-
via-mapreduce-and-scalding/

30. How do they compare?
Small feature
Different approaches, differences, which will
similar power even out over time
Scoobi gets a little Twitter is definitely a
closer to idiomatic bigger fish than
Scala NICTA, so Scalding
gets all the attention
Both open sourced
(last year) Scoobi has better docs!

31. Which one should I use?
Ehm...
...I’m extremely prejudiced!

32. Questions?