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POJoin with Hadoop in Java
 

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  1. POJoin with Hadoop in Java
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    <p>I'm working since short time with Hadoop and trying to implement a join in Java. It doesn't matter if Map-Side or Reduce-Side. I took Reduce-Side join since it was supposed to be easier to implement. I know that Java is not the best choice for joins, aggregations etc. and should better pick Hive or Pig which I have done already. However I'm working on a research project and I have to use all of those 3 languages in order to deliver a comparison. </p> <p>Anyway, I have two input files with different structure. One is key|value and the other one is key|value1;value2;value3;value4. One record from each input file looks like following:</p> <ul> <li>Input1: <code>1;2010-01-10T00:00:01</code></li> <li>Input2: <code>1;23;Blue;2010-01-11T00:00:01;9999-12-31T23:59:59</code></li> </ul> <p>I followed the example in the Hadoop Definitve Guide book, but it didn't work for me. I'm posting my java files here, so you can see what's wrong.</p> <pre><code>public class LookupReducer extends Reducer&lt;TextPair,Text,Text,Text&gt; { private String result = ""; private String msisdn; private String attribute, product; private long trans_dt_long, start_dt_long, end_dt_long; private String trans_dt, start_dt, end_dt; @Override public void reduce(TextPair key, Iterable&lt;Text&gt; values, Context context) throws IOException, InterruptedException { context.progress(); //value without key to remember Iterator&lt;Text&gt; iter = values.iterator(); for (Text val : values) { Text recordNoKey = val; //new Text(iter.next()); String valSplitted[] = recordNoKey.toString().split(";"); //if the input is coming from CDR set corresponding values if(key.getSecond().toString().equals(CDR.CDR_TAG)) { trans_dt = recordNoKey.toString(); trans_dt_long = dateToLong(recordNoKey.toString()); } //if the input is coming from Attributes set corresponding values else if(key.getSecond().toString().equals(Attribute.ATT_TAG)) { attribute = valSplitted[0]; product = valSplitted[1]; start_dt = valSplitted[2]; start_dt_long = dateToLong(valSplitted[2]); end_dt = valSplitted[3]; end_dt_long = dateToLong(valSplitted[3]);; } Text record = val; //iter.next(); //System.out.println("RECORD: " + record); Text outValue = new Text(recordNoKey.toString() + ";" + record.toString()); if(start_dt_long &lt; trans_dt_long &amp;&amp; trans_dt_long &lt; end_dt_long) { //concat output columns result = attribute + ";" + product + ";" + trans_dt; context.write(key.getFirst(), new Text(result)); System.out.println("KEY: " + key); } } } private static long dateToLong(String date){ DateFormat formatter = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss"); Date parsedDate = null; try { parsedDate = formatter.parse(date); } catch (ParseException e) { // TODO Auto-generated catch block e.printStackTrace(); } long dateInLong = parsedDate.getTime(); return dateInLong; } public static class TextPair implements WritableComparable&lt;TextPair&gt; { private Text first; private Text second; public TextPair(){ set(new Text(), new Text()); } public TextPair(String first, String second){ set(new Text(first), new Text(second)); } public TextPair(Text first, Text second){ set(first, second); } public void set(Text first, Text second){ this.first = first; this.second = second; } public Text getFirst() { return first; } public void setFirst(Text first) { this.first = first; } public Text getSecond() { return second; } public void setSecond(Text second) { this.second = second; } @Override public void readFields(DataInput in) throws IOException { // TODO Auto-generated method stub first.readFields(in); second.readFields(in); } @Override public void write(DataOutput out) throws IOException { // TODO Auto-generated method stub first.write(out); second.write(out); } @Override public int hashCode(){ return first.hashCode() * 163 + second.hashCode(); } @Override public boolean equals(Object o){ if(o instanceof TextPair) { TextPair tp = (TextPair) o; return first.equals(tp.first) &amp;&amp; second.equals(tp.second); } return false; } @Override public String toString(){ return first + ";" + second; } @Override public int compareTo(TextPair tp) { // TODO Auto-generated method stub int cmp = first.compareTo(tp.first); if(cmp != 0) return cmp; return second.compareTo(tp.second); } public static class FirstComparator extends WritableComparator { protected FirstComparator(){ super(TextPair.class, true); } @Override public int compare(WritableComparable comp1, WritableComparable comp2){ TextPair pair1 = (TextPair) comp1; TextPair pair2 = (TextPair) comp2; int cmp = pair1.getFirst().compareTo(pair2.getFirst()); if(cmp != 0) return cmp; return -pair1.getSecond().compareTo(pair2.getSecond()); } } public static class GroupComparator extends WritableComparator { protected GroupComparator() { super(TextPair.class, true); } @Override public int compare(WritableComparable comp1, WritableComparable comp2) { TextPair pair1 = (TextPair) comp1; TextPair pair2 = (TextPair) comp2; return pair1.compareTo(pair2); } } } } </code></pre> <hr> <pre><code>public class Joiner extends Configured implements Tool { public static final String DATA_SEPERATOR =";"; //Define the symbol for seperating the output data public static final String NUMBER_OF_REDUCER = "1"; //Define the number of the used reducer jobs public static final String COMPRESS_MAP_OUTPUT = "true"; //if the output from the mapping process should be compressed, set COMPRESS_MAP_OUTPUT = "true" (if not set it to "false") public static final String USED_COMPRESSION_CODEC = "org.apache.hadoop.io.compress.SnappyCodec"; //set the used codec for the data compression public static final boolean JOB_RUNNING_LOCAL = true; //if you run the Hadoop job on your local machine, you have to set JOB_RUNNING_LOCAL = true //if you run the Hadoop job on the Telefonica Cloud, you have to set JOB_RUNNING_LOCAL = false public static final String OUTPUT_PATH = "/home/hduser"; //set the folder, where the output is saved. Only needed, if JOB_RUNNING_LOCAL = false public static class KeyPartitioner extends Partitioner&lt;TextPair, Text&gt; { @Override public int getPartition(/*[*/TextPair key/*]*/, Text value, int numPartitions) { System.out.println("numPartitions: " + numPartitions); return (/*[*/key.getFirst().hashCode()/*]*/ &amp; Integer.MAX_VALUE) % numPartitions; } } private static Configuration hadoopconfig() { Configuration conf = new Configuration(); conf.set("mapred.textoutputformat.separator", DATA_SEPERATOR); conf.set("mapred.compress.map.output", COMPRESS_MAP_OUTPUT); //conf.set("mapred.map.output.compression.codec", USED_COMPRESSION_CODEC); conf.set("mapred.reduce.tasks", NUMBER_OF_REDUCER); return conf; } @Override public int run(String[] args) throws Exception { // TODO Auto-generated method stub if ((args.length != 3) &amp;&amp; (JOB_RUNNING_LOCAL)) { System.err.println("Usage: Lookup &lt;CDR-inputPath&gt; &lt;Attribute-inputPath&gt; &lt;outputPath&gt;"); System.exit(2); } //starting the Hadoop job Configuration conf = hadoopconfig(); Job job = new Job(conf, "Join cdrs and attributes"); job.setJarByClass(Joiner.class); MultipleInputs.addInputPath(job, new Path(args[0]), TextInputFormat.class, CDRMapper.class); MultipleInputs.addInputPath(job, new Path(args[1]), TextInputFormat.class, AttributeMapper.class); //FileInputFormat.addInputPath(job, new Path(otherArgs[0])); //expecting a folder instead of a file if(JOB_RUNNING_LOCAL) FileOutputFormat.setOutputPath(job, new Path(args[2])); else FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH)); job.setPartitionerClass(KeyPartitioner.class); job.setGroupingComparatorClass(TextPair.FirstComparator.class); job.setReducerClass(LookupReducer.class); job.setMapOutputKeyClass(TextPair.class); job.setMapOutputValueClass(Text.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); return job.waitForCompletion(true) ? 0 : 1; } public static void main(String[] args) throws Exception { int exitCode = ToolRunner.run(new Joiner(), args); System.exit(exitCode); } } </code></pre> <hr> <pre><code>public class Attribute { public static final String ATT_TAG = "1"; public static class AttributeMapper extends Mapper&lt;LongWritable, Text, TextPair, Text&gt;{ private static Text values = new Text(); //private Object output = new Text(); @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { //partition the input line by the separator semicolon String[] attributes = value.toString().split(";"); String valuesInString = ""; if(attributes.length != 5) System.err.println("Input column number not correct. Expected 5, provided " + attributes.length + "\n" + "Check the input file"); if(attributes.length == 5) { //setting the values with the input values read above valuesInString = attributes[1] + ";" + attributes[2] + ";" + attributes[3] + ";" + attributes[4]; values.set(valuesInString); //writing out the key and value pair context.write( new TextPair(new Text(String.valueOf(attributes[0])), new Text(ATT_TAG)), values); } } } } public class CDR { public static final String CDR_TAG = "0"; public static class CDRMapper extends Mapper&lt;LongWritable, Text, TextPair, Text&gt;{ private static Text values = new Text(); private Object output = new Text(); @Override public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { //partition the input line by the separator semicolon String[] cdr = value.toString().split(";"); //setting the values with the input values read above values.set(cdr[1]); //output = CDR_TAG + cdr[1]; //writing out the key and value pair context.write( new TextPair(new Text(String.valueOf(cdr[0])), new Text(CDR_TAG)), values); } } } </code></pre> <p>I would be glad if anyone could at least post a link for a tutorial or a simple example where such a join functionality is implemented. I searched a lot, but either the code was not complete or there was not enough explanation.</p>
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    1. PTQuestion
    1. USruakh
    1. USHans
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    1. COGiven one record in each of the above formats, what do you want to produce? What's the format?
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      1. POJoin with Hadoop in Java
      1. USChris Gerken
 

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