Writing a value to file without moving to reducer - hadoop

I have an input of records like this,
a|1|Y,
b|0|N,
c|1|N,
d|2|Y,
e|1|Y
Now, in mapper, i has to check the value of third column. If it is 'Y' then that record has to write directly to output file without moving that record to reducer or else i.e, 'N' value records has to move to reducer for further processing..
So,
a|1|Y,
d|2|Y,
e|1|Y
should not go to reducer but
b|0|N,
c|1|N
should go to reducer and then to output file.
How can i do this??

What you can probably do is use MultipleOutputs - click here to separate out records of 'Y' and 'N' type to two different files from mappers.
Next, you run saparate jobs for the two newly generated 'Y' and 'N' type data sets.
For 'Y' types set number of reducers to 0, so that, Reducers aren't use. And, for 'N' types do it the way you want using reducers.
Hope this helps.

See if this works,
public class Xxxx {
public static class MyMapper extends
Mapper<LongWritable, Text, LongWritable, Text> {
public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
FileSystem fs = FileSystem.get(context.getConfiguration());
Random r = new Random();
FileSplit split = (FileSplit)context.getInputSplit();
String fileName = split.getPath().getName();
FSDataOutputStream out = fs.create(new Path(fileName + "-m-" + r.nextInt()));
String parts[];
String line = value.toString();
String[] splits = line.split(",");
for(String s : splits) {
parts = s.split("\\|");
if(parts[2].equals("Y")) {
out.writeBytes(line);
}else {
context.write(key, value);
}
}
out.close();
fs.close();
}
}
public static class MyReducer extends
Reducer<LongWritable, Text, LongWritable, Text> {
public void reduce(LongWritable key, Iterable<Text> values,
Context context) throws IOException, InterruptedException {
for(Text t : values) {
context.write(key, t);
}
}
}
/**
* #param args
* #throws IOException
* #throws InterruptedException
* #throws ClassNotFoundException
*/
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
// TODO Auto-generated method stub
Configuration conf = new Configuration();
conf.set("fs.default.name", "hdfs://localhost:9000");
conf.set("mapred.job.tracker", "localhost:9001");
Job job = new Job(conf, "Xxxx");
job.setJarByClass(Xxxx.class);
Path outPath = new Path("/output_path");
job.setMapperClass(MyMapper.class);
job.setReducerClass(MyReducer.class);
FileInputFormat.addInputPath(job, new Path("/input.txt"));
FileOutputFormat.setOutputPath(job, outPath);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}

In your map function, you will get input line by line. Split it according by using | as the delimiter. (by using the String.split() method to be exact)
It will look like this
String[] line = value.toString().split('|');
Access the third element of this array by line[2]
Then, using a simple if else statement, emit the output with N value for further processing.

Related

How to remove r-00000 extention from reducer output in mapreduce

I am able to rename my reducer output file correctly but r-00000 is still persisting .
I have used MultipleOutputs in my reducer class .
Here is details of the that .Not sure what am i missing or what extra i have to do?
public class MyReducer extends Reducer<NullWritable, Text, NullWritable, Text> {
private Logger logger = Logger.getLogger(MyReducer.class);
private MultipleOutputs<NullWritable, Text> multipleOutputs;
String strName = "";
public void setup(Context context) {
logger.info("Inside Reducer.");
multipleOutputs = new MultipleOutputs<NullWritable, Text>(context);
}
#Override
public void reduce(NullWritable Key, Iterable<Text> values, Context context)
throws IOException, InterruptedException {
for (Text value : values) {
final String valueStr = value.toString();
StringBuilder sb = new StringBuilder();
sb.append(strArrvalueStr[0] + "|!|");
multipleOutputs.write(NullWritable.get(), new Text(sb.toString()),strName);
}
}
public void cleanup(Context context) throws IOException,
InterruptedException {
multipleOutputs.close();
}
}
I was able to do it explicitly after my job finishes and thats ok for me.No delay in the job
if (b){
DateFormat dateFormat = new SimpleDateFormat("yyyy-MM-dd-HHmm");
Calendar cal = Calendar.getInstance();
String strDate=dateFormat.format(cal.getTime());
FileSystem hdfs = FileSystem.get(getConf());
FileStatus fs[] = hdfs.listStatus(new Path(args[1]));
if (fs != null){
for (FileStatus aFile : fs) {
if (!aFile.isDir()) {
hdfs.rename(aFile.getPath(), new Path(aFile.getPath().toString()+".txt"));
}
}
}
}
A more suitable approach to the problem would be changing the OutputFormat.
For eg :- If you are using TextOutputFormatClass, just get the source code of the TextOutputFormat class and modify the below method to get the proper filename (without r-00000). We need to then set the modified output format in the driver.
public synchronized static String getUniqueFile(TaskAttemptContext context, String name, String extension) {
/*TaskID taskId = context.getTaskAttemptID().getTaskID();
int partition = taskId.getId();*/
StringBuilder result = new StringBuilder();
result.append(name);
/*
* result.append('-');
* result.append(TaskID.getRepresentingCharacter(taskId.getTaskType()));
* result.append('-'); result.append(NUMBER_FORMAT.format(partition));
* result.append(extension);
*/
return result.toString();
}
So whatever name is passed through the multiple outputs, filename will be created according to it.

Hadoop not all values get assembled for one key

I have some data that I would like to aggregate by key using Mapper code and then perform something on all values that belong to a key using Reducer code. For example if I have:
key = 1, val = 1,
key = 1, val = 2,
key = 1, val = 3
I would like to get key=1, val=[1,2,3] in my Reducer.
The thing is, I get something like
key = 1, val=[1,2]
key = 1, val=[3]
Why is that so?
I thought that all the values for one specific key will be assembled in one reducer, but now it seems that there can be more key, val [ ] pairs, since there can be multiple reducers, is that so?
Should I set number of reducers to be 1?
I'm new to Hadoop so this confuses me.
Here's the code
public class SomeJob {
public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException
{
Job job = new Job();
job.setJarByClass(SomeJob.class);
FileInputFormat.addInputPath(job, new Path("/home/pera/data/input/some.csv"));
FileOutputFormat.setOutputPath(job, new Path("/home/pera/data/output"));
job.setMapperClass(SomeMapper.class);
job.setReducerClass(SomeReducer.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
job.waitForCompletion(true);
}
}
public class SomeMapper extends Mapper<LongWritable, Text, Text, Text>{
#Override
protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
String line = value.toString();
String parts[] = line.split(";");
context.write(new Text(parts[0]), new Text(parts[4]));
}
}
public class SomeReducer extends Reducer<Text, Text, Text, Text>{
#Override
protected void reduce(Text key, Iterable<Text> values, Context context) throws IOException, InterruptedException {
String properties = "";
for(Text value : values)
{
properties += value + " ";
}
context.write(key, new Text(properties));
}
}

Hadoop Mapreduce: Custom Input Format

I have a file with data having text and "^" in between:
SOME TEXT^GOES HERE^
AND A FEW^MORE
GOES HERE
I am writing a custom input format to delimit the rows using "^" character. i.e The output of the mapper should be like:
SOME TEXT
GOES HERE
AND A FEW
MORE GOES HERE
I have written a written a custom input format which extends FileInputFormat and also written a custom record reader that extends RecordReader. Code for my custom record reader is given below. I dont know how to proceed with this code. Having trouble with the nextKeyValue() method in the WHILE loop part. How should I read the data from a split and generate my custom key-value? I am using all new mapreduce package instead of the old mapred package.
public class MyRecordReader extends RecordReader<LongWritable, Text>
{
long start, current, end;
Text value;
LongWritable key;
LineReader reader;
FileSplit split;
Path path;
FileSystem fs;
FSDataInputStream in;
Configuration conf;
#Override
public void initialize(InputSplit inputSplit, TaskAttemptContext cont) throws IOException, InterruptedException
{
conf = cont.getConfiguration();
split = (FileSplit)inputSplit;
path = split.getPath();
fs = path.getFileSystem(conf);
in = fs.open(path);
reader = new LineReader(in, conf);
start = split.getStart();
current = start;
end = split.getLength() + start;
}
#Override
public boolean nextKeyValue() throws IOException
{
if(key==null)
key = new LongWritable();
key.set(current);
if(value==null)
value = new Text();
long readSize = 0;
while(current<end)
{
Text tmpText = new Text();
readSize = read //here how should i read data from the split, and generate key-value?
if(readSize==0)
break;
current+=readSize;
}
if(readSize==0)
{
key = null;
value = null;
return false;
}
return true;
}
#Override
public float getProgress() throws IOException
{
}
#Override
public LongWritable getCurrentKey() throws IOException
{
}
#Override
public Text getCurrentValue() throws IOException
{
}
#Override
public void close() throws IOException
{
}
}
There is no need to implement that yourself. You can simply set the configuration value textinputformat.record.delimiter to be the circumflex character.
conf.set("textinputformat.record.delimiter", "^");
This should work fine with the normal TextInputFormat.

How to pass variable between two map reduce jobs

I have chained two Map reduce jobs. The Job1 will have only one reducer and I am computing a float value. I want to use this value in my reducer of Job2. This is my main method setup.
public static String GlobalVriable;
public static void main(String[] args) throws Exception {
int runs = 0;
for (; runs < 10; runs++) {
String inputPath = "part-r-000" + nf.format(runs);
String outputPath = "part-r-000" + nf.format(runs + 1);
MyProgram.MR1(inputPath);
MyProgram.MR2(inputPath, outputPath);
}
}
public static void MR1(String inputPath)
throws IOException, InterruptedException, ClassNotFoundException {
Configuration conf = new Configuration();
conf.set("var1","");
Job job = new Job(conf, "This is job1");
job.setJarByClass(MyProgram.class);
job.setMapperClass(MyMapper1.class);
job.setReducerClass(MyReduce1.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FloatWritable.class);
FileInputFormat.addInputPath(job, new Path(inputPath));
job.waitForCompletion(true);
GlobalVriable = conf.get("var1"); // I am getting NULL here
}
public static void MR2(String inputPath, String outputPath)
throws IOException, InterruptedException, ClassNotFoundException {
Configuration conf = new Configuration();
Job job = new Job(conf, "This is job2");
...
}
public static class MyReduce1 extends
Reducer<Text, FloatWritable, Text, FloatWritable> {
public void reduce(Text key, Iterable<FloatWritable> values, Context context)
throws IOException, InterruptedException {
float s = 0;
for (FloatWritable val : values) {
s += val.get();
}
String sum = Float.toString(s);
context.getConfiguration().set("var1", sum);
}
}
As you can see I need to iterate the entire program multiple times. My Job1 is computing a single number from the input. Since it is just a single number and a lot of iterations I dont want to write it to HDFS and read from it. Is there a way to share the value computed in Myreducer1 and use it in Myreducer2.
UPDATE: I have tried passing the value using conf.set & conf.get. The value is not being passed.
Here's how to pass back a float value via a counter ...
First, in the first reducer, transform the float value into a long by multiplying by 1000 (to maintain 3 digits of precision, for example) and putting the result into a counter:
public void cleanup(Context context) {
long result = (long) (floatValue * 1000);
context.getCounter("Result","Result").increment(result);
}
In the driver class, retrieve the long value and transform it back to a float:
public static void MR1(String inputPath)
throws IOException, InterruptedException, ClassNotFoundException {
Configuration conf = new Configuration();
Job job = new Job(conf, "This is job1");
job.setJarByClass(MyProgram.class);
job.setMapperClass(MyMapper1.class);
job.setReducerClass(MyReduce1.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(FloatWritable.class);
FileInputFormat.addInputPath(job, new Path(inputPath));
job.waitForCompletion(true);
long result = job.getCounters().findCounter("Result","Result").getValue();
float value = ((float)result) / 1000;
}
You could use ZooKeeper for this. It's great for any inter-job coordination or message passing like this.
Can't you just change the return type of MR1 to int (or whatever data type is appropriate) and return the number you computed:
int myNumber = MyProgram.MR1(inputPath);
Then add a parameter to MR2 and call it with your computed number:
MyProgram.MR2(inputPath, outputPath, myNumber);

Type mismatch in key from map, using SequenceFileInputFormat correctly

I am trying to run a recommender example from chapter6 (listing 6.1 ~ 6.4) in the ebook Mahout in Action. There are two mapper/reducer pairs. Here is the code:
Mapper - 1
public class WikipediaToItemPrefsMapper extends
Mapper<LongWritable,Text,VarLongWritable,VarLongWritable> {
private static final Pattern NUMBERS = Pattern.compile("(\d+)");
#Override
public void map(LongWritable key,
Text value,
Context context)
throws IOException, InterruptedException {
String line = value.toString();
Matcher m = NUMBERS.matcher(line);
m.find();
VarLongWritable userID = new VarLongWritable(Long.parseLong(m.group()));
VarLongWritable itemID = new VarLongWritable();
while (m.find()) {
itemID.set(Long.parseLong(m.group()));
context.write(userID, itemID);
}
}
}
Reducer - 1
public class WikipediaToUserVectorReducer extends
Reducer<VarLongWritable,VarLongWritable,VarLongWritable,VectorWritable> {
#Override
public void reduce(VarLongWritable userID,
Iterable<VarLongWritable> itemPrefs,
Context context)
throws IOException, InterruptedException {
Vector userVector = new RandomAccessSparseVector(
Integer.MAX_VALUE, 100);
for (VarLongWritable itemPref : itemPrefs) {
userVector.set((int)itemPref.get(), 1.0f);
}
//LongWritable userID_lw = new LongWritable(userID.get());
context.write(userID, new VectorWritable(userVector));
//context.write(userID_lw, new VectorWritable(userVector));
}
}
The reducer outputs a userID and a userVector and it looks like this: 98955 {590:1.0 22:1.0 9059:1.0 3:1.0 2:1.0 1:1.0} provided FileInputformat and TextInputFormat are used in the driver.
I want to use another pair of mapper-reducer to process this data further:
Mapper - 2
public class UserVectorToCooccurenceMapper extends
Mapper<VarLongWritable,VectorWritable,IntWritable,IntWritable> {
#Override
public void map(VarLongWritable userID,
VectorWritable userVector,
Context context)
throws IOException, InterruptedException {
Iterator<Vector.Element> it = userVector.get().iterateNonZero();
while (it.hasNext()) {
int index1 = it.next().index();
Iterator<Vector.Element> it2 = userVector.get().iterateNonZero();
while (it2.hasNext()) {
int index2 = it2.next().index();
context.write(new IntWritable(index1),
new IntWritable(index2));
}
}
}
}
Reducer - 2
public class UserVectorToCooccurenceReducer extends
Reducer {
#Override
public void reduce(IntWritable itemIndex1,
Iterable<IntWritable> itemIndex2s,
Context context)
throws IOException, InterruptedException {
Vector cooccurrenceRow = new RandomAccessSparseVector(Integer.MAX_VALUE, 100);
for (IntWritable intWritable : itemIndex2s) {
int itemIndex2 = intWritable.get();
cooccurrenceRow.set(itemIndex2, cooccurrenceRow.get(itemIndex2) + 1.0);
}
context.write(itemIndex1, new VectorWritable(cooccurrenceRow));
}
}
This is the driver I am using:
public final class RecommenderJob extends Configured implements Tool {
#Override
public int run(String[] args) throws Exception {
Job job_preferenceValues = new Job (getConf());
job_preferenceValues.setJarByClass(RecommenderJob.class);
job_preferenceValues.setJobName("job_preferenceValues");
job_preferenceValues.setInputFormatClass(TextInputFormat.class);
job_preferenceValues.setOutputFormatClass(SequenceFileOutputFormat.class);
FileInputFormat.setInputPaths(job_preferenceValues, new Path(args[0]));
SequenceFileOutputFormat.setOutputPath(job_preferenceValues, new Path(args[1]));
job_preferenceValues.setMapOutputKeyClass(VarLongWritable.class);
job_preferenceValues.setMapOutputValueClass(VarLongWritable.class);
job_preferenceValues.setOutputKeyClass(VarLongWritable.class);
job_preferenceValues.setOutputValueClass(VectorWritable.class);
job_preferenceValues.setMapperClass(WikipediaToItemPrefsMapper.class);
job_preferenceValues.setReducerClass(WikipediaToUserVectorReducer.class);
job_preferenceValues.waitForCompletion(true);
Job job_cooccurence = new Job (getConf());
job_cooccurence.setJarByClass(RecommenderJob.class);
job_cooccurence.setJobName("job_cooccurence");
job_cooccurence.setInputFormatClass(SequenceFileInputFormat.class);
job_cooccurence.setOutputFormatClass(TextOutputFormat.class);
SequenceFileInputFormat.setInputPaths(job_cooccurence, new Path(args[1]));
FileOutputFormat.setOutputPath(job_cooccurence, new Path(args[2]));
job_cooccurence.setMapOutputKeyClass(VarLongWritable.class);
job_cooccurence.setMapOutputValueClass(VectorWritable.class);
job_cooccurence.setOutputKeyClass(IntWritable.class);
job_cooccurence.setOutputValueClass(VectorWritable.class);
job_cooccurence.setMapperClass(UserVectorToCooccurenceMapper.class);
job_cooccurence.setReducerClass(UserVectorToCooccurenceReducer.class);
job_cooccurence.waitForCompletion(true);
return 0;
}
public static void main(String[] args) throws Exception {
ToolRunner.run(new Configuration(), new RecommenderJob(), args);
}
}
The error that I get is:
java.io.IOException: Type mismatch in key from map: expected org.apache.mahout.math.VarLongWritable, received org.apache.hadoop.io.IntWritable
In course of Googling for a fix, I found out that my issue is similar to this question. But the difference is that I am already using SequenceFileInputFormat and SequenceFileOutputFormat, I believe correctly. I also see that org.apache.mahout.cf.taste.hadoop.item.RecommenderJob does more or less something similar. In my understanding & Yahoo Tutorial
SequenceFileOutputFormat rapidly serializes arbitrary data types to the file; the corresponding SequenceFileInputFormat will deserialize the file into the same types and presents the data to the next Mapper in the same manner as it was emitted by the previous Reducer.
What am I doing wrong? Will really appreciate some pointers from someone.. I spent the day trying to fix this and got nowhere :(
Your second mapper has the following signature:
public class UserVectorToCooccurenceMapper extends
Mapper<VarLongWritable,VectorWritable,IntWritable,IntWritable>
But you define the following in your driver code:
job_cooccurence.setMapOutputKeyClass(VarLongWritable.class);
job_cooccurence.setMapOutputValueClass(VectorWritable.class);
The reducer is expecting <IntWritable, IntWritable> as input, so you should just amend your driver code to:
job_cooccurence.setMapOutputKeyClass(IntWritable.class);
job_cooccurence.setMapOutputValueClass(IntWritable.class);

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