I modified the code below to output words which occurred at least ten times. But it does not work -- the output file does not change at all. What do I have to do to make it work?
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.*;
import org.apache.hadoop.mapreduce.lib.output.*;
import org.apache.hadoop.util.*;
// ...
public class WordCount extends Configured implements Tool {
// ...
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);
}
}
}
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();
}
// where I modified, but not working, the output file didnt change
if(sum >= 10)
{
context.write(key, new IntWritable(sum));
}
}
}
public int run(String[] args) throws Exception {
Job job = new Job(getConf());
job.setJarByClass(WordCount.class);
job.setJobName("wordcount");
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setMapperClass(Map.class);
//job.setCombinerClass(Reduce.class);
job.setReducerClass(Reduce.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
boolean success = job.waitForCompletion(true);
return success ? 0 : 1;
}
public static void main(String[] args) throws Exception {
int ret = ToolRunner.run(new WordCount(), args);
System.exit(ret);
}
}
Code looks completely valid. I can suspect that your dataset is big enough, so words happens to appear more then 10 times?
Please laso make sure that you indeed looking into new results..
You can see the default Hadoop counters and have an idea of whats happening.
The code looks valid.
To be able to help you we need at least the command line you used to run this. It would also help if you could post the actual output if you feed it a file like this
one
two two
three three three
Etc up till 20
The code is definitely correct, Maybe you are reading the output generated before you modified the code. Or maybe you did not update the jar file which you previously used after modifying the code?
Related
import java.io.IOException;
import java.util.*;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class CommonFriends {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private IntWritable friend = new IntWritable();
private Text friends = new Text();
public void map(Object key, Text value, Context context ) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString(),"\n");
while (itr.hasMoreTokens()) {
String[] line = itr.nextToken().split(" ");
if(line.length > 2 ){
int person = Integer.parseInt(line[0]);
for(int i=1; i<line.length;i++){
int ifriend = Integer.parseInt(line[i]);
friends.set((person < ifriend ? person+"-"+ifriend : ifriend+"-"+person));
for(int j=1; j< line.length; j++ ){
if( i != j ){
friend.set(Integer.parseInt(line[j]));
context.write(friends, friend);
}
}
}
}
}
}
}
public static class IntSumReducer extends Reducer<Text,IntWritable,Text,Text> {
private Text result = new Text();
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
HashSet<IntWritable> duplicates = new HashSet();
ArrayList<Integer> tmp = new ArrayList();
for (IntWritable val : values) {
if(duplicates.contains(val))
tmp.add(val.get());
else
duplicates.add(val);
}
result.set(tmp.toString());
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "Common Friends");
job.setJarByClass(CommonFriends.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
Error: java.io.IOException: wrong value class: class org.apache.hadoop.io.Text is not class org.apache.hadoop.io.IntWritable
at org.apache.hadoop.mapred.IFile$Writer.append(IFile.java:194)
at org.apache.hadoop.mapred.Task$CombineOutputCollector.collect(Task.java:1350)
at org.apache.hadoop.mapred.Task$NewCombinerRunner$OutputConverter.write(Task.java:1667)
at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
at org.apache.hadoop.mapreduce.lib.reduce.WrappedReducer$Context.write(WrappedReducer.java:105)
at CommonFriends$IntSumReducer.reduce(CommonFriends.java:51)
at CommonFriends$IntSumReducer.reduce(CommonFriends.java:38)
at org.apache.hadoop.mapreduce.Reducer.run(Reducer.java:171)
at org.apache.hadoop.mapred.Task$NewCombinerRunner.combine(Task.java:1688)
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.sortAndSpill(MapTask.java:1637)
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.flush(MapTask.java:1489)
at org.apache.hadoop.mapred.MapTask$NewOutputCollector.close(MapTask.java:723)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:793)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
at org.apache.hadoop.mapred.YarnChild$2.run(YarnChild.java:164)
at java.security.AccessController.doPrivileged(Native Method)
at javax.security.auth.Subject.doAs(Subject.java:422)
at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
at org.apache.hadoop.mapred.YarnChild.main(YarnChild.java:158)
This is my code, The error message is the following.
Any idea??
I think the problem in the configuration of output classes of mapper and the reducer
the input files are a list of numbers in file.
Some more details will be provided if needed.
The program finds the common friend between friends
remove job.setCombinerClass(IntSumReducer.class); in your code could solve this problem
Just had a look into your code, it seems you are using reducer code as combiner code.
One thing you need to check.
Your combiner code will take input in form of <Text, IntWritable> and output of Combiner would be <Text, Text> format .
Then the input to your Reducer would be in format of < Text, Text> but you had specified the input to Reducer as < Text, IntWritable > , so it is throwing the error.
Two things can be done :-
1) You might consider changing the output type of Reducer .
2) You might consider writing a separate Combiner code.
I'm trying to make it so that when a user enters a word the program will go through the txt file and count all the instances of that word.
I'm using MapReduce and i'm new at it.
I know there is a really simple way to do this and i've been trying to figure that out for a while.
In this code I'm trying to make it so that it would ask for the user input and the program would go through the file and find instances.
I've seen some codes on stack overflow and someone mentioned that setting the configuration to conf.set("userinput","Data") would help somehow.
Also there is some updated way to have the user input.
The if statement in my program is an example of when the user word is entered it only finds that word.
import java.util.StringTokenizer;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class WordCount {
public static class TokenizerMapper
extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
//So I've seen that this is the correct way of setting it up.
// However I've heard that there mroe efficeint ways of setting it up as well.
/*
public void setup(Context context) {
Configuration config=context.getConfiguration();
String wordstring=config.get("mapper.word");
word.setAccessibleHelp(wordstring);
}
*/
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString());
while (itr.hasMoreTokens()) {
if(word=="userinput") {
word.set(itr.nextToken());
context.write(word, one);
}
}
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable();
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0;
for (IntWritable val : values) {
sum += val.get();
}
result.set(sum);
context.write(key, result);
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = Job.getInstance(conf, "word count");
job.setJarByClass(WordCount.class);
job.setMapperClass(TokenizerMapper.class);
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
I'm not sure about the setup method, but you pass the input at the command line as an argument.
conf.set("mapper.word",args[0]);
Job job =...
// Notice you now need 3 arguments to run this
FileInputFormat.addInputPath(job, new Path(args[1]));
FileOutputFormat.setOutputPath(job, new Path(args[2]));
In the mapper or reducer, you can get the string
Configuration config=context.getConfiguration();
String wordstring=config.get("mapper.word");
And you need to get the string from the tokenizer before you can compare it. You also need to compare strings, not a string to a text object
String wordstring=config.get("mapper.word");
while (itr.hasMoreTokens()) {
String token = itr.nextToken();
if(wordstring.equals(token)) {
word.set(token);
context.write(word, one);
}
I have written a Map Reduce Program in Hadoop for hashing all the records of the file, and appending the hased value as an additional attribute to each record and then output to Hadoop file system
This is the code i have written
public class HashByMapReduce
{
public static class LineMapper extends Mapper<Text, Text, Text, Text>
{
private Text word = new Text();
public void map(Text key, Text value, Context context) throws IOException, InterruptedException
{
key.set("single")
String line = value.toString();
word.set(line);
context.write(key, line);
}
}
public static class LineReducer
extends Reducer<Text,Text,Text,Text>
{
private Text result = new Text();
public void reduce(Text key, Iterable<Text> values,
Context context
) throws IOException, InterruptedException
{
String translations = "";
for (Text val : values)
{
translations = val.toString()+","+String.valueOf(hash64(val.toString())); //Point of Error
result.set(translations);
context.write(key, result);
}
}
}
public static void main(String[] args) throws Exception
{
Configuration conf = new Configuration();
Job job = new Job(conf, "Hashing");
job.setJarByClass(HashByMapReduce.class);
job.setMapperClass(LineMapper.class);
job.setReducerClass(LineReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(KeyValueTextInputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
I have written this code with the logic that Each line is read by the Map method which assigns all value to a single key which then passes to same Reducer method. which the passes each values to hash64() function.
But i see its passing a null value(empty value) to hash function. I am not unable to figure it out why? Thanks in advance
The cause of the problem is most probably due to the use of KeyValueTextInputFormat. From Yahoo tutorial :
InputFormat: Description: Key: Value:
TextInputFormat Default format; The byte offset The line contents
reads lines of of the line
text files
KeyValueInputFormat Parses lines Everything up to the The remainder of
into key, first tab character the line
val pairs
It's breaking your input lines wrt tab character. I suppose there is no tab in your lines. As a result the key in the LineMapper is a whole line while nothing is being passed as value ( not sure null or empty ).
From your code I think you should better use TextInputFormat class as your inputformat which produces line offset as key and the complete line as value. This should solve your problem.
EDIT : I run your code with following changes, and it seems to work fine:
Changed inputformat to TextInputFormat and accordingly change declaration of the Mapper
Added proper setMapOutputKeyClass & setMapOutputValueClass to the job. These are not mandatory but often creates problem on running.
Removed your ket.set("single") and added a private outkey to the Mapper.
Since you provided no details of hash64 method, I used String.toUpperCase for testing.
If the issue persists, then I'm sure that your hash method hasn't handle null well.
Full code :
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class HashByMapReduce {
public static class LineMapper extends
Mapper<LongWritable, Text, Text, Text> {
private Text word = new Text();
private Text outKey = new Text("single");
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
word.set(line);
context.write(outKey, word);
}
}
public static class LineReducer extends Reducer<Text, Text, Text, Text> {
private Text result = new Text();
public void reduce(Text key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
String translations = "";
for (Text val : values) {
translations = val.toString() + ","
+ val.toString().toUpperCase(); // Point of Error
result.set(translations);
context.write(key, result);
}
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
Job job = new Job(conf, "Hashing");
job.setJarByClass(HashByMapReduce.class);
job.setMapperClass(LineMapper.class);
job.setReducerClass(LineReducer.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.setInputFormatClass(TextInputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
I was trying a mapreduce wordcount code in hadoop, but the reducer class is never called and the program terminates after running the mapper class.
import java.io.IOException;
import java.util.*;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.conf.*;
import org.apache.hadoop.io.*;
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 Map extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private Text word = new Text();
#Override
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);
}
}
}
public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {
#Override
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();
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);
}
}
I have even Overridden the classes as required.
IDE : Eclipse Luna
Hadoop : version 2.5
A Job object forms the specification of the job and gives you control over how the job is run. When we run this job on a Hadoop cluster, we will package the code into a JAR file (which Hadoop will distribute around the cluster).
Rather than explicitly specify the name of the JAR file, we can pass a class in the Job's setJarByClass() method, which Hadoop will use to locate the relevant JAR file by looking for the JAR file containing this class.
I do not see the statement in the main method. Hence, include this and then compile and run the code.
job.setJarByClass(WordCount.class);
I get the following error when I execute my alphabet count program.
java.io.IOException: Type mismatch in key from map: expected org.apache.hadoop.io.Text, recieved org.apache.hadoop.io.LongWritable
at org.apache.hadoop.mapred.MapTask$MapOutputBuffer.collect(MapTask.java:1014)
at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:691)
at org.apache.hadoop.mapreduce.TaskInputOutputContext.write(TaskInputOutputContext.java:80)
at com.example.AlphabetCount$Map.map(AlphabetCount.java:40)
Command used to run: ./bin/hadoop jar /home/ubuntu/Documents/AlphabetCount.jar input output
I have browsed and checked the first eight links when I google using the error message. I have implemented their advice and yet the error message appears. Can you help me out, please?
Code:
package com.example;
import java.io.IOException;
import java.util.Iterator;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
public class AlphabetCount {
public static class Map1 extends
Mapper<LongWritable, Text, Text, IntWritable> {
private Text alphabet = new Text();
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
String line = value.toString();
byte[] byteArray = line.getBytes();
int sum = 0;
alphabet.set("a");
for (int i = 0; i < byteArray.length; i++) {
if ((byteArray[i] == 'a') || (byteArray[i] == 'A')) {
sum += 1;
}
}
context.write(alphabet, new IntWritable(sum));
}
}
public static class Reduce1 extends
Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> value,
Context context) throws IOException, InterruptedException {
final Text alphabet = new Text();
alphabet.set(key);
int sum = 0;
while (value.hasNext()) {
sum = sum + value.next().get();
}
context.write(alphabet, new IntWritable(sum));
}
}
public static void main(String[] args) throws IOException,
InterruptedException, ClassNotFoundException {
Configuration conf = new Configuration();
Job job = new Job(conf);
job.setJarByClass(AlphabetCount.class);
job.setMapperClass(Map1.class);
job.setCombinerClass(Reduce1.class);
job.setReducerClass(Reduce1.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.waitForCompletion(true);
}
}
Update: (Solution) the above code works! I was getting the error because the jar I was executing was different from the jar I was updating with the above code! I had initially exported the jar (with erroneous code) from eclipse to location x and subsequently I was updating the code in location y but still executing the jar from location x! damn!
Try specifying your input and output formats classes in your main method and also the input key format of your Mapper. You should have something similar to this :
public class AlphabetCount {
public static class Map1 extends
Mapper<Text, Text, Text, IntWritable> {
...
public void map(Text key, Text value, Context context)
throws IOException, InterruptedException {
...
}
}
public static class Reduce1 extends
Reducer<Text, IntWritable, Text, IntWritable> {
public void reduce(Text key, Iterator<IntWritable> value,
Context context) throws IOException, InterruptedException {
...
}
}
public static void main(String[] args) throws IOException,
InterruptedException, ClassNotFoundException {
Configuration conf = new Configuration();
Job job = new Job(conf);
...
job.setInputFormatClass(KeyValueTextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
...
}
}