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Presentation on functional data mining at the IGT Cloud meet up at eBay Netanya


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  • 1. Some tips for effective map reducing CHRISTOPHER SEVERS eBay eBay Netanya December 2nd, 2013
  • 3. THE AGENDA 1. Quick survey of the current landscape for Hadoop tools 2. A light comparison of the best functional tools. 3. General advice 4. Some code samples PRESENTATION TITLE GOES HERE 3
  • 4. THE ALTERNATIVES I promise this part will be quick
  • 5. VANILLA MAPREDUCE package org.myorg; import; import java.util.*; import org.apache.hadoop.fs.Path; import org.apache.hadoop.conf.*; import*; 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 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(); 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); } } } 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); } PRESENTATION TITLE GOES HERE 5
  • 6. 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 PRESENTATION TITLE GOES HERE 6
  • 7. APACHE HIVE •On previous slide: s/Pig/Hive/g PRESENTATION TITLE GOES HERE 7
  • 8. GENERAL ADVICE Do this, not that
  • 9. 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 PRESENTATION TITLE GOES HERE 9
  • 10. 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 PRESENTATION TITLE GOES HERE 10
  • 11. SCALDING AND SCOOBI This is what we use at eBay
  • 12. SOME SCALA CODE val myLines = getStuff val myWords = myLines.flatMap(w => w.split("s+")) val myWordsGrouped = myLines.groupBy(identity) val countedWords = myWordsGrouped. mapValues(x=>x.size) write(countedWords) PRESENTATION TITLE GOES HERE 12
  • 13. SOME SCALDING CODE val myLines = TextLine(path) val myWords= myLines.flatMap(w => w.split(" ")) .groupBy(identity) .size myWords.write(TypedTSV(output)) PRESENTATION TITLE GOES HERE 13
  • 14. 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 PRESENTATION TITLE GOES HERE 14
  • 15. 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 PRESENTATION TITLE GOES HERE 15
  • 16. 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] PRESENTATION TITLE GOES HERE 16
  • 17. 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 PRESENTATION TITLE GOES HERE 17
  • 18. 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) =>,r) } •Algebird handles tuples with ease •Very easy to define monoids for your own types PRESENTATION TITLE GOES HERE 18
  • 19. 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 PRESENTATION TITLE GOES HERE 19
  • 20. CONCLUSION We’re almost done!
  • 21. 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
  • 22. THANKS! •Questions/comments? PRESENTATION TITLE GOES HERE 22