Why Hadoop Need scala (Updated)

A slightly updated version of my "Why Hadoop Needs Scala" talk, mostly high-level and, as I mention often, mostly about Scoobi.

This version was presented on June 5th 2012 at XebiCon, the first Xebia open knowledge exchange.

Transcript

1. @agemooij A Look at Scoobi and Scalding Scala DSLs for Hadoop Why Needs Scala Scoobi Scalding
2. Obligatory “About Me” Slide
3. Rocks!
4. Sucks! But programming kinda
5. Hello World Word Count using Hadoop MapReduce
6. For each word, sum the 1s to get the total Split lines into words Group by word (?) Turn each word into a Pair(word, 1)
7. Low level glue code Lots of small unintuitive Mapper and Reducer Classes Lots of Hadoop intrusiveness (Context, Writables, Exceptions, etc.) 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 Already a lot better, but anything more complex gets hard pretty fast. Handy for quick exploration of data! Pig is hard to customize/extend Nice! And the same goes for Hive
11. package cascadingtutorial.wordcount; /** * Wordcount example in Cascading */ public class Main { public static void main( String[] args ) { String inputPath = args[0]; String outputPath = args[1]; Scheme inputScheme = new TextLine(new Fields("offset", "line")); Scheme outputScheme = new TextLine(); Tap sourceTap = inputPath.matches( "^[^:]+://.*") ? new Hfs(inputScheme, inputPath) : new Lfs(inputScheme, inputPath); Tap sinkTap = outputPath.matches("^[^:]+://.*") ? new Hfs(outputScheme, outputPath) : new Lfs(outputScheme, outputPath); Pipe wcPipe = new Each("wordcount", 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(); } } Pipes & Filters Not very intuitive Lots of boilerplate code Very powerful! Record Model Strange new abstraction Joins & CoGroups
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 Scalding 5% Scoobi 95% NOTE: My relative familiarity with either platform:
17. http://github.com/nicta/scoobi A Scala library that implements a higher level programming model for Hadoop MapReduce
18. Counting Words using Scoobi For each word, sum the 1s to get the total Split lines into words Group by word Turn each word into a Pair(word, 1) 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. • Can read/write text files, Sequence files and Avro files • Can influence sorting (raw, secondary) IO 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 http://nicta.github.com/scoobi/ scoobi-dev@googlegroups.com scoobi-users@googlegroups.com Version 0.4 released today (!) • Avro, Sequence Files • Materialized DObjects • DList reduction methods (product, min, etc.) • Vastly improved testing support • Less overhead • Much more
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 @scalding cascading-user@googlegroups.com http://blog.echen.me/2012/02/09/movie-recommendations-and-more- via-mapreduce-and-scalding/ https://github.com/twitter/scalding/wiki Current version: 0.5.4 http://github.com/twitter/scalding
30. How do they compare? Different approaches, similar power Small feature differences, which will even out over time Scoobi gets a little closer to idiomatic Scala Twitter is definitely a bigger fish than 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?