Hadoop

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Introduction To Hadoop Kenneth Heafield Google Inc January 14, 2008 Example code from Hadoop 0.13.1 used under the Apache License Version 2.0 and modified for presentation. Except as otherwise noted, the content of this presentation is licensed under the Creative Commons Attribution 2.5 License. Kenneth Heafield (Google Inc) Introduction To Hadoop January 14, 2008 1 / 12

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Outline 1 Word Count Code Mapper Reducer Main 2 How it Works Serialization Data Flow 3 Lab Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 2 / 12

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Word Count Code Mapper Mapper < “wikipedia.org”, “The Free” >→ < “The”, 1 >, < “Free”, 1 > public void map(WritableComparable key, Writable value, OutputCollector output, Reporter reporter) throws IOException { String line = ((Text)value).toString(); StringTokenizer itr = new StringTokenizer(line); Text word = new Text(); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); output.collect(word, new IntWritable(1)); } } Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 3 / 12

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Word Count Code Reducer Reducer < “The”, 1 >, < “The”, 1 >→ < “The”, 2 > public void reduce(WritableComparable key, Iterator values, OutputCollector output, Reporter reporter) throws IOException { int sum = 0; while (values.hasNext()) { sum += ((IntWritable) values.next()).get(); } output.collect(key, new IntWritable(sum)); } Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 4 / 12

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Word Count Code Main Main public static void main(String[] args) throws IOException { JobConf conf = new JobConf(WordCount.class); conf.setJobName("wordcount"); conf.setMapperClass(MapClass.class); conf.setCombinerClass(ReduceClass.class); conf.setReducerClass(ReduceClass.class); conf.setNumMapTasks(new Integer(40)); conf.setNumReduceTasks(new Integer(30)); conf.setInputPath(new Path("/shared/wikipedia_small")); conf.setOutputPath(new Path("/user/kheafield/word_count")); conf.setOutputKeyClass(Text.class); conf.setOutputValueClass(IntWritable.class); JobClient.runJob(conf); } Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 5 / 12

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How it Works Serialization Types Purpose Simple serialization for keys, values, and other data Interface Writable Read and write binary format Convert to String for text formats WritableComparable adds sorting order for keys Example Implementations ArrayWritable is only Writable BooleanWritable IntWritable sorts in increasing order Text holds a String Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 6 / 12

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How it Works Serialization A Writable public class IntPairWritable implements Writable { public int first; public int second; public void write(DataOutput out) throws IOException { out.writeInt(first); out.writeInt(second); } public void readFields(DataInput in) throws IOException { first = in.readInt(); second = in.readInt(); } public int hashCode() { return first + second; } public String toString() { return Integer.toString(first) + "," + Integer.toString(second); } Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 7 / 12

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How it Works Serialization WritableComparable Method public int compareTo(Object other) { IntPairWritable o = (IntPairWritable)other; if (first < o.first) return -1; if (first > o.first) return 1; if (second < o.second) return -1; if (second > o.second) return 1; return 0; } Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 8 / 12

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How it Works Data Flow Data Flow Default Flow 1 Mappers read from HDFS 2 Map output is partitioned by key and sent to Reducers 3 Reducers sort input by key 4 Reduce output is written to HDFS 1 HDFS 2 Mapper 3 Reducer 4 HDFS Input Map Sort Reduce Output Input Map Sort Reduce Output Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 9 / 12

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How it Works Data Flow Combiners Concept Add counts at Mapper before sending to Reducer. Word count is 6 minutes with combiners and 14 without. Implementation Mapper caches output and periodically calls Combiner Input to Combine may be from Map or Combine Combiner uses interface as Reducer Mapper Combine Sort Reduce Output Input Map Cache Sort Reduce Output Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 10 / 12

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Lab Exercises Recommended: Word Count Getword count running. Bigrams Count bigrams and unigrams eﬃciently. Capitalization With what probability is a word capitalized? Indexer In what documents does each word appear? Where in the documents? Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 11 / 12

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Lab Instructions 1 Login to the cluster successfully (and set your password). 2 Get Eclipse installed, so you can build Java code. 3 Install the Hadoop plugin for Eclipse so you can deploy jobs to the cluster. 4 Set up your Eclipse workspace from a template that we provide. 5 Run the word counter example over the Wikipedia data set. Kenneth Heaﬁeld (Google Inc) Introduction To Hadoop January 14, 2008 12 / 12

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