How to insert , MapReduce output into HBASE using same program - hadoop

Question

I wrote a program which take pdf as an input and produces text output as a whole. I want to load this text in hbase using same program, is there any way to do it.Any help will be appreciable
//Driver Class
package com.tcs;
import java.io.IOException;
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.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class PdfInputDriver {
public static void main(String[] args) throws IOException,InterruptedException, ClassNotFoundException
{
Configuration conf = new Configuration();
GenericOptionsParser parser = new GenericOptionsParser(conf, args);
args = parser.getRemainingArgs();
#SuppressWarnings("deprecation")
Job job = new Job(conf, "Pdftext");
job.setJarByClass(PdfInputDriver.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(LongWritable.class);
job.setInputFormatClass(PdfInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(WordCountMapper.class);
job.setReducerClass(WordCountReducer.class);
System.out.println(job.waitForCompletion(true));
}
}
//InputFormatClass
package com.tcs;
import java.io.IOException;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
public class PdfInputFormat extends FileInputFormat<Object, Object> {
#SuppressWarnings({ "unchecked", "rawtypes" })
#Override
public RecordReader createRecordReader(
InputSplit split, TaskAttemptContext context) throws IOException,
InterruptedException {
return new PdfRecordReader();
}
}
//PDF Record Reader class
package com.tcs;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.pdfbox.pdmodel.PDDocument;
import org.apache.pdfbox.util.PDFTextStripper;
public class PdfRecordReader extends RecordReader<Object, Object> {
private String[] lines = null;
private LongWritable key = null;
private Text value = null;
#Override
public void initialize(InputSplit genericSplit, TaskAttemptContext context)
throws IOException, InterruptedException {
FileSplit split = (FileSplit) genericSplit;
Configuration job = context.getConfiguration();
final Path file = split.getPath();
/*
* The below code contains the logic for opening the file and seek to
* the start of the split. Here we are applying the Pdf Parsing logic
*/
FileSystem fs = file.getFileSystem(job);
FSDataInputStream fileIn = fs.open(split.getPath());
PDDocument pdf = null;
String parsedText = null;
PDFTextStripper stripper;
pdf = PDDocument.load(fileIn);
stripper = new PDFTextStripper();
parsedText = stripper.getText(pdf);
//String delims = "[ ]";
this.lines = parsedText.split("/n");
}
#Override
public boolean nextKeyValue() throws IOException, InterruptedException {
if (key == null) {
key = new LongWritable();
key.set(1);
value = new Text();
value.set(lines[0]);
} else
{
int temp = (int) key.get();
if (temp < (lines.length - 1)) {
int count = (int) key.get();
value = new Text();
value.set(lines[count]);
count = count + 1;
key = new LongWritable(count);
} else {
return false;
}
}
if (key == null || value == null) {
return false;
} else {
return true;
}
}
#Override
public LongWritable getCurrentKey() throws IOException,
InterruptedException {
return key;
}
#Override
public Text getCurrentValue() throws IOException, InterruptedException {
return value;
}
#Override
public float getProgress() throws IOException, InterruptedException {
return 0;
}
#Override
public void close() throws IOException {
}
}
//Mapper Class
package com.tcs;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
public class WordCountMapper extends Mapper<LongWritable, Text, Text, LongWritable>
{
protected void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
context.write(value, key);
}
}
//Reducer Class
package com.tcs;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
public class WordCountReducer extends Reducer<Object, Object, Object, Object> {
protected void reduce(Text key, Iterable<LongWritable> values,
Context context) throws IOException, InterruptedException {
context.write(key, new WordCountReducer());
}
}

Answer 1

i think you are making jar file of it. Just use the part-r-00000 file of what you have generated from mapreduce output.
create 'table'