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Posted to user@hbase.apache.org by Arun Allamsetty <ar...@gmail.com> on 2014/08/01 18:17:00 UTC
Re: Hbase Mapreduce API - Reduce to a file is not working properly.
Hi Parkirat,
I don't think that HBase is causing the problems. You might already know
this but need to add the reducer class to the job as you add the mapper.
Also, if you want to read from a HBase table in a MapReduce job, you need
to implement the TableMapper for the mapper and if you want to write to a
file on HDFS instead of a HBase table, you need to implement the generic
Reducer class for the reducer.
But again, if you can show everyone the code, people will be able to help
you better.
Cheers,
Arun
Sent from a mobile device. Please don't mind the typos.
On Jul 31, 2014 1:07 PM, "Nick Dimiduk" <nd...@gmail.com> wrote:
> Hi Parkirat,
>
> I don't follow the reducer problem you're having. Can you post your code
> that configures the job? I assume you're using TableMapReduceUtil
> someplace.
>
> Your reducer is removing duplicate values? Sounds like you need to update
> it's logic to only emit a value once. Pastebin-ing your reducer code may be
> helpful as well.
>
> -n
>
>
> On Thu, Jul 31, 2014 at 8:20 AM, Parkirat <pa...@gmail.com>
> wrote:
>
> > Hi All,
> >
> > I am using Mapreduce API to read Hbase Table, based on some scan
> operation
> > in mapper and putting the data to a file in reducer.
> > I am using Hbase Version "Version 0.94.5.23".
> >
> > *Problem:*
> > Now in my job, my mapper output a key as text and value as text, but my
> > reducer output key as text and value as nullwritable, but it seems *hbase
> > mapreduce api dont consider reducer*, and outputs both key and value as
> > text.
> >
> > Moreover if the same key comes twice, it goes to the file twice, even if
> my
> > reducer want to log it only once.
> >
> > Could anybody help me with this problem?
> >
> > Regards,
> > Parkirat Singh Bagga.
> >
> >
> >
> > --
> > View this message in context:
> >
> http://apache-hbase.679495.n3.nabble.com/Hbase-Mapreduce-API-Reduce-to-a-file-is-not-working-properly-tp4062141.html
> > Sent from the HBase User mailing list archive at Nabble.com.
> >
>
Re: Hbase Mapreduce API - Reduce to a file is not working properly.
Posted by Shahab Yunus <sh...@gmail.com>.
Parkirat,
This is a core Java concept which is mainly related to how Class
inheritance works in Java and how the @Override annotation is used, and is
not Hadoop specific. (It is also used while implementing interfaces since
JDK 6.)
You can read about it here:
http://tutorials.jenkov.com/java/annotations.html#override
http://www.javapractices.com/topic/TopicAction.do?Id=223
Regards,
Shahab
On Sat, Aug 2, 2014 at 2:56 AM, Arun Allamsetty <ar...@gmail.com>
wrote:
> Hi,
>
> The @Override annotation worked because, without it the reduce method in
> the superclass (Reducer) was being invoked, which basically writes the
> input from the mapper class to the context object. Try to look up the
> source code for the Reducer class online and you'll realize that.
>
> Hope that clears it up.
>
> Cheers,
> Arun
>
> Sent from a mobile device. Please don't mind the typos.
> Hi Shahab,
>
> Thanks for the response.
>
> I have added the @Override and somehow that worked. I have pasted the new
> Reducer code below.
>
> Though, I did not understood the difference here, as if what I have done
> differently. I might be a very silly reason though.
>
> =========================================================
> package com.test.hadoop;
>
> import java.io.IOException;
>
> import org.apache.hadoop.io.IntWritable;
> import org.apache.hadoop.io.Text;
> import org.apache.hadoop.mapreduce.Reducer;
>
> public class WordCountReducer extends Reducer<Text, IntWritable, Text,
> IntWritable> {
>
> @Override
> protected 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));
> }
> }
> =========================================================
>
> Regards,
> Parkirat Bagga.
>
>
>
> --
> View this message in context:
>
> http://apache-hbase.679495.n3.nabble.com/Hbase-Mapreduce-API-Reduce-to-a-file-is-not-working-properly-tp4062141p4062240.html
> Sent from the HBase User mailing list archive at Nabble.com.
>
Re: Hbase Mapreduce API - Reduce to a file is not working properly.
Posted by Arun Allamsetty <ar...@gmail.com>.
Hi,
The @Override annotation worked because, without it the reduce method in
the superclass (Reducer) was being invoked, which basically writes the
input from the mapper class to the context object. Try to look up the
source code for the Reducer class online and you'll realize that.
Hope that clears it up.
Cheers,
Arun
Sent from a mobile device. Please don't mind the typos.
Hi Shahab,
Thanks for the response.
I have added the @Override and somehow that worked. I have pasted the new
Reducer code below.
Though, I did not understood the difference here, as if what I have done
differently. I might be a very silly reason though.
=========================================================
package com.test.hadoop;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordCountReducer extends Reducer<Text, IntWritable, Text,
IntWritable> {
@Override
protected 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));
}
}
=========================================================
Regards,
Parkirat Bagga.
--
View this message in context:
http://apache-hbase.679495.n3.nabble.com/Hbase-Mapreduce-API-Reduce-to-a-file-is-not-working-properly-tp4062141p4062240.html
Sent from the HBase User mailing list archive at Nabble.com.
Re: Hbase Mapreduce API - Reduce to a file is not working properly.
Posted by Parkirat <pa...@gmail.com>.
Hi Shahab,
Thanks for the response.
I have added the @Override and somehow that worked. I have pasted the new
Reducer code below.
Though, I did not understood the difference here, as if what I have done
differently. I might be a very silly reason though.
=========================================================
package com.test.hadoop;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordCountReducer extends Reducer<Text, IntWritable, Text,
IntWritable> {
@Override
protected 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));
}
}
=========================================================
Regards,
Parkirat Bagga.
--
View this message in context: http://apache-hbase.679495.n3.nabble.com/Hbase-Mapreduce-API-Reduce-to-a-file-is-not-working-properly-tp4062141p4062240.html
Sent from the HBase User mailing list archive at Nabble.com.
Re: Hbase Mapreduce API - Reduce to a file is not working properly.
Posted by Shahab Yunus <sh...@gmail.com>.
Add @Override notation at top of the 'reduce' method and then try (just
like you are doing for the 'map' method):
public class WordCountReducer extends Reducer<Text, IntWritable, Text,
IntWritable> {
* @Override*
protected void reduce(Text key, Iterable<IntWritable> values,
org.apache.hadoop.mapreduce.Reducer.Context context)
throws IOException, InterruptedException {
...
Regards,
Shahab
On Fri, Aug 1, 2014 at 5:05 PM, Parkirat <pa...@gmail.com> wrote:
> Thanks All for replying to my thread.
>
> I have further investigated the issue and found that hadoop is not
> running/respecting any reduce for my jobs ir-respective of if, it is normal
> mapreduce or hbase api of mapreduce.
>
> I am pasting word count example that I have run and the input and output
> file below for the reference. Please if anybody can find any issue in my
> code:
>
> *Job Config Class:*
> ================================================
> package com.test.hadoop;
>
> import java.io.IOException;
>
> import org.apache.hadoop.conf.Configuration;
> import org.apache.hadoop.fs.Path;
> import org.apache.hadoop.io.IntWritable;
> import org.apache.hadoop.io.Text;
> import org.apache.hadoop.mapreduce.Job;
> import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
> import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
>
> public class WordCountJob {
>
> public static void main(String[] args) throws IOException,
> InterruptedException, ClassNotFoundException {
>
> if (args.length != 2) {
> System.out.println("usage: [inputdir]
> [outputdir]");
> System.exit(-1);
> }
>
> String inputdir = args[0].trim();
> String outputdir = args[1].trim();
>
> Configuration config = new Configuration();
>
> Job job = new Job(config, "Word Count");
> job.setJarByClass(WordCountMapper.class);
>
> FileInputFormat.setInputPaths(job, new Path(inputdir));
> FileOutputFormat.setOutputPath(job, new Path(outputdir));
>
> job.setMapperClass(WordCountMapper.class);
> job.setMapOutputKeyClass(Text.class);
> job.setMapOutputValueClass(IntWritable.class);
>
> job.setReducerClass(WordCountReducer.class);
> job.setOutputKeyClass(Text.class);
> job.setOutputValueClass(IntWritable.class);
>
> boolean b2 = job.waitForCompletion(true);
> if (!b2) {
> throw new IOException("error with job!");
> }
> }
>
> }
> ================================================
>
> *Mapper Class:*
> ================================================
> package com.test.hadoop;
>
> import java.io.IOException;
> import java.util.StringTokenizer;
>
> import org.apache.hadoop.io.IntWritable;
> import org.apache.hadoop.io.Text;
> import org.apache.hadoop.mapreduce.Mapper;
>
> public class WordCountMapper extends Mapper<Object, Text, Text,
> IntWritable>
> {
>
> private final static IntWritable one = new IntWritable(1);
> private Text word = new Text();
>
> @Override
> protected void map(Object key, Text value,
> org.apache.hadoop.mapreduce.Mapper.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);
> }
> }
> }
> ================================================
>
> *Reducer Class:*
> ================================================
> package com.test.hadoop;
>
> import java.io.IOException;
>
> import org.apache.hadoop.io.IntWritable;
> import org.apache.hadoop.io.Text;
> import org.apache.hadoop.mapreduce.Reducer;
>
> public class WordCountReducer extends Reducer<Text, IntWritable, Text,
> IntWritable> {
>
> protected void reduce(Text key, Iterable<IntWritable> values,
> org.apache.hadoop.mapreduce.Reducer.Context
> context)
> throws IOException, InterruptedException {
>
> int sum = 0;
> for (IntWritable val : values) {
> sum += val.get();
> }
> context.write(key, new IntWritable(sum));
> }
> }
> ================================================
>
> *Input File:*
> ================================================
> -bash-4.1$ cat /tmp/testfile.txt
> This is an example to test Hadoop so as to test if this example works fine
> or not.
> ================================================
>
> *Mapreduce Console Output:*
> ================================================
> -bash-4.1$ hadoop jar /tmp/WordCount.jar com.test.hadoop.WordCountJob
> /tmp/wc/input /tmp/wc/output
> 14/08/01 20:52:19 WARN mapred.JobClient: Use GenericOptionsParser for
> parsing the arguments. Applications should implement Tool for the same.
> 14/08/01 20:52:19 INFO input.FileInputFormat: Total input paths to process
> :
> 1
> 14/08/01 20:52:19 INFO lzo.GPLNativeCodeLoader: Loaded native gpl library
> 14/08/01 20:52:19 INFO lzo.LzoCodec: Successfully loaded & initialized
> native-lzo library [hadoop-lzo rev
> cf4e7cbf8ed0f0622504d008101c2729dc0c9ff3]
> 14/08/01 20:52:19 WARN snappy.LoadSnappy: Snappy native library is
> available
> 14/08/01 20:52:19 INFO util.NativeCodeLoader: Loaded the native-hadoop
> library
> 14/08/01 20:52:19 INFO snappy.LoadSnappy: Snappy native library loaded
> 14/08/01 20:52:41 INFO mapred.JobClient: Running job: job_201404021234_0090
> 14/08/01 20:52:42 INFO mapred.JobClient: map 0% reduce 0%
> 14/08/01 20:52:54 INFO mapred.JobClient: map 100% reduce 0%
> 14/08/01 20:53:02 INFO mapred.JobClient: map 100% reduce 33%
> 14/08/01 20:53:04 INFO mapred.JobClient: map 100% reduce 100%
> 14/08/01 20:53:05 INFO mapred.JobClient: Job complete:
> job_201404021234_0090
> 14/08/01 20:53:05 INFO mapred.JobClient: Counters: 29
> 14/08/01 20:53:05 INFO mapred.JobClient: Job Counters
> 14/08/01 20:53:05 INFO mapred.JobClient: Launched reduce tasks=1
> 14/08/01 20:53:05 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=9171
> 14/08/01 20:53:05 INFO mapred.JobClient: Total time spent by all
> reduces
> waiting after reserving slots (ms)=0
> 14/08/01 20:53:05 INFO mapred.JobClient: Total time spent by all maps
> waiting after reserving slots (ms)=0
> 14/08/01 20:53:05 INFO mapred.JobClient: Launched map tasks=1
> 14/08/01 20:53:05 INFO mapred.JobClient: Data-local map tasks=1
> 14/08/01 20:53:05 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=9719
> 14/08/01 20:53:05 INFO mapred.JobClient: File Output Format Counters
> 14/08/01 20:53:05 INFO mapred.JobClient: Bytes Written=119
> 14/08/01 20:53:05 INFO mapred.JobClient: FileSystemCounters
> 14/08/01 20:53:05 INFO mapred.JobClient: FILE_BYTES_READ=197
> 14/08/01 20:53:05 INFO mapred.JobClient: HDFS_BYTES_READ=214
> 14/08/01 20:53:05 INFO mapred.JobClient: FILE_BYTES_WRITTEN=112948
> 14/08/01 20:53:05 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=119
> 14/08/01 20:53:05 INFO mapred.JobClient: File Input Format Counters
> 14/08/01 20:53:05 INFO mapred.JobClient: Bytes Read=83
> 14/08/01 20:53:05 INFO mapred.JobClient: Map-Reduce Framework
> 14/08/01 20:53:05 INFO mapred.JobClient: Map output materialized
> bytes=197
> 14/08/01 20:53:05 INFO mapred.JobClient: Map input records=1
> 14/08/01 20:53:05 INFO mapred.JobClient: Reduce shuffle bytes=197
> 14/08/01 20:53:05 INFO mapred.JobClient: Spilled Records=36
> 14/08/01 20:53:05 INFO mapred.JobClient: Map output bytes=155
> 14/08/01 20:53:05 INFO mapred.JobClient: CPU time spent (ms)=2770
> 14/08/01 20:53:05 INFO mapred.JobClient: Total committed heap usage
> (bytes)=398393344
> 14/08/01 20:53:05 INFO mapred.JobClient: Combine input records=0
> 14/08/01 20:53:05 INFO mapred.JobClient: SPLIT_RAW_BYTES=131
> 14/08/01 20:53:05 INFO mapred.JobClient: Reduce input records=18
> 14/08/01 20:53:05 INFO mapred.JobClient: Reduce input groups=15
> 14/08/01 20:53:05 INFO mapred.JobClient: Combine output records=0
> 14/08/01 20:53:05 INFO mapred.JobClient: Physical memory (bytes)
> snapshot=385605632
> 14/08/01 20:53:05 INFO mapred.JobClient: Reduce output records=18
> 14/08/01 20:53:05 INFO mapred.JobClient: Virtual memory (bytes)
> snapshot=2707595264
> 14/08/01 20:53:05 INFO mapred.JobClient: Map output records=18
> ================================================
>
> *Generated Output File:*
> ================================================
> -bash-4.1$ hadoop fs -tail /tmp/wc/output/part-r-00000
> Hadoop 1
> This 1
> an 1
> as 1
> example 1
> example 1
> fine 1
> if 1
> is 1
> not. 1
> or 1
> so 1
> test 1
> test 1
> this 1
> to 1
> to 1
> works 1
> ================================================
>
> Regards,
> Parkirat Bagga
>
>
>
>
> --
> View this message in context:
> http://apache-hbase.679495.n3.nabble.com/Hbase-Mapreduce-API-Reduce-to-a-file-is-not-working-properly-tp4062141p4062222.html
> Sent from the HBase User mailing list archive at Nabble.com.
>
Re: Hbase Mapreduce API - Reduce to a file is not working properly.
Posted by Parkirat <pa...@gmail.com>.
Thanks All for replying to my thread.
I have further investigated the issue and found that hadoop is not
running/respecting any reduce for my jobs ir-respective of if, it is normal
mapreduce or hbase api of mapreduce.
I am pasting word count example that I have run and the input and output
file below for the reference. Please if anybody can find any issue in my
code:
*Job Config Class:*
================================================
package com.test.hadoop;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCountJob {
public static void main(String[] args) throws IOException,
InterruptedException, ClassNotFoundException {
if (args.length != 2) {
System.out.println("usage: [inputdir] [outputdir]");
System.exit(-1);
}
String inputdir = args[0].trim();
String outputdir = args[1].trim();
Configuration config = new Configuration();
Job job = new Job(config, "Word Count");
job.setJarByClass(WordCountMapper.class);
FileInputFormat.setInputPaths(job, new Path(inputdir));
FileOutputFormat.setOutputPath(job, new Path(outputdir));
job.setMapperClass(WordCountMapper.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setReducerClass(WordCountReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
boolean b2 = job.waitForCompletion(true);
if (!b2) {
throw new IOException("error with job!");
}
}
}
================================================
*Mapper Class:*
================================================
package com.test.hadoop;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WordCountMapper extends Mapper<Object, Text, Text, IntWritable>
{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
@Override
protected void map(Object key, Text value,
org.apache.hadoop.mapreduce.Mapper.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);
}
}
}
================================================
*Reducer Class:*
================================================
package com.test.hadoop;
import java.io.IOException;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordCountReducer extends Reducer<Text, IntWritable, Text,
IntWritable> {
protected void reduce(Text key, Iterable<IntWritable> values,
org.apache.hadoop.mapreduce.Reducer.Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
context.write(key, new IntWritable(sum));
}
}
================================================
*Input File:*
================================================
-bash-4.1$ cat /tmp/testfile.txt
This is an example to test Hadoop so as to test if this example works fine
or not.
================================================
*Mapreduce Console Output:*
================================================
-bash-4.1$ hadoop jar /tmp/WordCount.jar com.test.hadoop.WordCountJob
/tmp/wc/input /tmp/wc/output
14/08/01 20:52:19 WARN mapred.JobClient: Use GenericOptionsParser for
parsing the arguments. Applications should implement Tool for the same.
14/08/01 20:52:19 INFO input.FileInputFormat: Total input paths to process :
1
14/08/01 20:52:19 INFO lzo.GPLNativeCodeLoader: Loaded native gpl library
14/08/01 20:52:19 INFO lzo.LzoCodec: Successfully loaded & initialized
native-lzo library [hadoop-lzo rev cf4e7cbf8ed0f0622504d008101c2729dc0c9ff3]
14/08/01 20:52:19 WARN snappy.LoadSnappy: Snappy native library is available
14/08/01 20:52:19 INFO util.NativeCodeLoader: Loaded the native-hadoop
library
14/08/01 20:52:19 INFO snappy.LoadSnappy: Snappy native library loaded
14/08/01 20:52:41 INFO mapred.JobClient: Running job: job_201404021234_0090
14/08/01 20:52:42 INFO mapred.JobClient: map 0% reduce 0%
14/08/01 20:52:54 INFO mapred.JobClient: map 100% reduce 0%
14/08/01 20:53:02 INFO mapred.JobClient: map 100% reduce 33%
14/08/01 20:53:04 INFO mapred.JobClient: map 100% reduce 100%
14/08/01 20:53:05 INFO mapred.JobClient: Job complete: job_201404021234_0090
14/08/01 20:53:05 INFO mapred.JobClient: Counters: 29
14/08/01 20:53:05 INFO mapred.JobClient: Job Counters
14/08/01 20:53:05 INFO mapred.JobClient: Launched reduce tasks=1
14/08/01 20:53:05 INFO mapred.JobClient: SLOTS_MILLIS_MAPS=9171
14/08/01 20:53:05 INFO mapred.JobClient: Total time spent by all reduces
waiting after reserving slots (ms)=0
14/08/01 20:53:05 INFO mapred.JobClient: Total time spent by all maps
waiting after reserving slots (ms)=0
14/08/01 20:53:05 INFO mapred.JobClient: Launched map tasks=1
14/08/01 20:53:05 INFO mapred.JobClient: Data-local map tasks=1
14/08/01 20:53:05 INFO mapred.JobClient: SLOTS_MILLIS_REDUCES=9719
14/08/01 20:53:05 INFO mapred.JobClient: File Output Format Counters
14/08/01 20:53:05 INFO mapred.JobClient: Bytes Written=119
14/08/01 20:53:05 INFO mapred.JobClient: FileSystemCounters
14/08/01 20:53:05 INFO mapred.JobClient: FILE_BYTES_READ=197
14/08/01 20:53:05 INFO mapred.JobClient: HDFS_BYTES_READ=214
14/08/01 20:53:05 INFO mapred.JobClient: FILE_BYTES_WRITTEN=112948
14/08/01 20:53:05 INFO mapred.JobClient: HDFS_BYTES_WRITTEN=119
14/08/01 20:53:05 INFO mapred.JobClient: File Input Format Counters
14/08/01 20:53:05 INFO mapred.JobClient: Bytes Read=83
14/08/01 20:53:05 INFO mapred.JobClient: Map-Reduce Framework
14/08/01 20:53:05 INFO mapred.JobClient: Map output materialized
bytes=197
14/08/01 20:53:05 INFO mapred.JobClient: Map input records=1
14/08/01 20:53:05 INFO mapred.JobClient: Reduce shuffle bytes=197
14/08/01 20:53:05 INFO mapred.JobClient: Spilled Records=36
14/08/01 20:53:05 INFO mapred.JobClient: Map output bytes=155
14/08/01 20:53:05 INFO mapred.JobClient: CPU time spent (ms)=2770
14/08/01 20:53:05 INFO mapred.JobClient: Total committed heap usage
(bytes)=398393344
14/08/01 20:53:05 INFO mapred.JobClient: Combine input records=0
14/08/01 20:53:05 INFO mapred.JobClient: SPLIT_RAW_BYTES=131
14/08/01 20:53:05 INFO mapred.JobClient: Reduce input records=18
14/08/01 20:53:05 INFO mapred.JobClient: Reduce input groups=15
14/08/01 20:53:05 INFO mapred.JobClient: Combine output records=0
14/08/01 20:53:05 INFO mapred.JobClient: Physical memory (bytes)
snapshot=385605632
14/08/01 20:53:05 INFO mapred.JobClient: Reduce output records=18
14/08/01 20:53:05 INFO mapred.JobClient: Virtual memory (bytes)
snapshot=2707595264
14/08/01 20:53:05 INFO mapred.JobClient: Map output records=18
================================================
*Generated Output File:*
================================================
-bash-4.1$ hadoop fs -tail /tmp/wc/output/part-r-00000
Hadoop 1
This 1
an 1
as 1
example 1
example 1
fine 1
if 1
is 1
not. 1
or 1
so 1
test 1
test 1
this 1
to 1
to 1
works 1
================================================
Regards,
Parkirat Bagga
--
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