Hadoop Map Reduce Miscs

Word Count:
Spark:
val textFile = spark.textFile("hdfs://...")
val counts = textFile.flatMap(line => line.split(" "))
                 .map(word => (word, 1))
                 .reduceByKey(_ + _)
counts.saveAsTextFile("hdfs://...")
http://wiki.apache.org/hadoop/WordCount

Each mapper takes a line as input and breaks it into words. It then emits a key/value pair of the word and 1. Each reducer sums the counts for each word and emits a single key/value with the word and sum.

As an optimization, the reducer is also used as a combiner on the map outputs. This reduces the amount of data sent across the network by combining each word into a single record.

  17  public static class Map extends Mapper<LongWritable, Text, Text, IntWritable> {
  18     private final static IntWritable one = new IntWritable(1);
  19     private Text word = new Text();
  20         
  21     public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
  22         String line = value.toString();
  23         StringTokenizer tokenizer = new StringTokenizer(line);
  24         while (tokenizer.hasMoreTokens()) {
  25             word.set(tokenizer.nextToken());
  26             context.write(word, one);
  27         }
  28     }
  29  } 
  30         
  31  public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
  32 
  33     public void reduce(Text key, Iterable<IntWritable> values, Context context) 
  34       throws IOException, InterruptedException {
  35         int sum = 0;
  36         for (IntWritable val : values) {
  37             sum += val.get();
  38         }
  39         context.write(key, new IntWritable(sum));
  40     }
  41  }
  42         
  43  public static void main(String[] args) throws Exception {
  44     Configuration conf = new Configuration();
  45         
  46         Job job = new Job(conf, "wordcount");
  47     
  48     job.setOutputKeyClass(Text.class);
  49     job.setOutputValueClass(IntWritable.class);
  50         
  51     job.setMapperClass(Map.class);
  52     job.setReducerClass(Reduce.class);
  53         
  54     job.setInputFormatClass(TextInputFormat.class);
  55     job.setOutputFormatClass(TextOutputFormat.class);
  56         
  57     FileInputFormat.addInputPath(job, new Path(args[0]));
  58     FileOutputFormat.setOutputPath(job, new Path(args[1]));
  59         
  60     job.waitForCompletion(true);
  61  }

Line Count:

SparkConf sparkConf = new SparkConf().setAppName("File Copy");

03.JavaSparkContext sparkContext = new JavaSparkContext(sparkConf);

04. 

05.// Read the source file

06.JavaRDD<String> input = sparkContext.textFile(args[0]);

07. 

08.// Gets the number of entries in the RDD

09.long count = input.count();