Some tips for effective map reducing

CHRISTOPHER SEVERSeBay

eBay NetanyaDecember 2nd, 2013

THE AGENDA

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THE AGENDA

1. Quick survey of the current landscape for Hadoop tools

2. A light comparison of the best functional tools. 3. General advice4. Some code samples

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THE ALTERNATIVES

I promise this part will be quick

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VANILLA MAPREDUCE

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package org.myorg; import java.io.IOException;import java.util.*; import org.apache.hadoop.fs.Path;import org.apache.hadoop.conf.*;import org.apache.hadoop.io.*;import org.apache.hadoop.mapreduce.*;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; public class WordCount { public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer tokenizer = new StringTokenizer(line); while (tokenizer.hasMoreTokens()) { word.set(tokenizer.nextToken()); context.write(word, one); } } }

public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {

public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } context.write(key, new IntWritable(sum)); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf, "wordcount"); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setMapperClass(Map.class); job.setReducerClass(Reduce.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.waitForCompletion(true); }

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PIG

•Apache Pig is a really great tool for quick, ad-hoc data analysis

•While we can do amazing things with it, I’m not sure we should

•Anything complicated requires User Defined Functions (UDFs)

•UDFs require a separate code base•Now you have to maintain two separate languages for no good reason

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APACHE HIVE

•On previous slide: s/Pig/Hive/g

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GENERAL ADVICE

Do this, not that

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DO

•Use a higher level abstraction like distributed lists•Use objects instead of tuples•Use a good serialization format•Always check for data quality•Use flatMap for uncertain computations•Develop reusable reductions (monoids!)•Prefer map side operations when possible•Always check for data skew

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DON’T

•Never use nulls•Don’t use too many levels of nesting•Don’t use shared state•Don’t use iteration (too much)•Try not to start with a complicated approach

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SCALDING AND SCOOBIThis is what we use at eBay

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SOME SCALA CODE

val myLines = getStuffval myWords = myLines.flatMap(w => w.split("\\s+"))val myWordsGrouped = myLines.groupBy(identity)val countedWords = myWordsGrouped.mapValues(x=>x.size)write(countedWords)

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SOME SCALDING CODE

val myLines = TextLine(path)val myWords= myLines.flatMap(w => w.split(" ")) .groupBy(identity) .sizemyWords.write(TypedTSV(output))

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WHAT HAPPENED ON THE PREVIOUS SLIDE?

•flatMap()–Similar to map, but a one-to-many rather than one-to-one

mapping –Use when the desired result has some probability of

occurring –Can handle errors with the Option (Maybe) monad. A None

type will be discarded

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MORE EXPLANATION

•groupBy()–Takes a function that generates a key from the given value–Logically the result can be thought of as an associative

array: key -> List of values–In Scalding this doesn’t necessarily force a Hadoop reduce

phase, it depends on what comes after

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THE BEST PART

•size–This part is pure magic–size is actually sugar for .map( t => 1L).sum–sum has an implicit argument, mon: Monoid[T]

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MONOIDS: WHY YOU SHOULD CARE ABOUT MATH•From Wikipedia:

–a monoid is an algebraic structure with a single associative binary operation and an identity element.

•Almost everything you want to do is a monoid–Standard addition of numeric types is the most common–List/map/set/string concatenation–Top k elements–Bloom filter, count-min sketch, hyperloglog–stochastic gradient descent–histograms

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MORE MONOID STUFF

•If you are aggregating, you are probably using a monoid•Scalding has Algebird and monoid support baked in•Scoobi can use Algebird (or any other monoid library) with almost no work–combine { case (l,r) => monoid.plus(l,r) }

•Algebird handles tuples with ease•Very easy to define monoids for your own types

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ADVANTAGES

•Type checking–Find errors at compile time, not at job submission time (or

even worse, 5 hours after job submission time)•Single language

–Scala is a full programming language•Productivity

–Since the code you write looks like collections code you can use the Scala REPL to prototype

•Clarity–Write code as a series of operations and let the job planner

smash it all together

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CONCLUSION

We’re almost done!

THINGS TO TAKE AWAY

•Mapreduce is a functional problem, we should use functional tools

•You can increase productivity, safety, and maintainability all at once with no down side

•Thinking of data flows in a functional way opens up many new possibilities

•The community is awesome

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THANKS!

•Questions/comments?

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