MapReduce实现推荐系统(4)

import java.io.IOException;
import java.util.Map;

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.mapred.FileInputFormat;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.RunningJob;
import org.apache.hadoop.mapred.TextInputFormat;
import org.apache.hadoop.mapred.TextOutputFormat;

public class Step3 {

public static class Step31_UserVectorSplitterMapper extends MapReduceBase implements Mapper<LongWritable, Text, IntWritable, Text> {
        private final static IntWritable k = new IntWritable();
        private final static Text v = new Text();

        public void map(LongWritable key, Text values, OutputCollector<IntWritable, Text> output, Reporter reporter) throws IOException {
            String[] tokens = Recommend.DELIMITER.split(values.toString());
            for (int i = 1; i < tokens.length; i++) {
                String[] vector = tokens[i].split(":");
                int itemID = Integer.parseInt(vector[0]);
                String pref = vector[1];

k.set(itemID);
                v.set(tokens[0] + ":" + pref);
                output.collect(k, v);
            }
        }
    }

public static void run1(Map<String, String> path) throws IOException {
        JobConf conf = Recommend.config();

String input = path.get("Step3Input1");
        String output = path.get("Step3Output1");

HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
        hdfs.rmr(output);

conf.setOutputKeyClass(IntWritable.class);
        conf.setOutputValueClass(Text.class);

conf.setMapperClass(Step31_UserVectorSplitterMapper.class);

conf.setInputFormat(TextInputFormat.class);
        conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(input));
        FileOutputFormat.setOutputPath(conf, new Path(output));

RunningJob job = JobClient.runJob(conf);
        while (!job.isComplete()) {
            job.waitForCompletion();
        }
    }

public static class Step32_CooccurrenceColumnWrapperMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {
        private final static Text k = new Text();
        private final static IntWritable v = new IntWritable();

        public void map(LongWritable key, Text values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {
            String[] tokens = Recommend.DELIMITER.split(values.toString());
            k.set(tokens[0]);
            v.set(Integer.parseInt(tokens[1]));
            output.collect(k, v);
        }
    }

public static void run2(Map<String, String> path) throws IOException {
        JobConf conf = Recommend.config();

String input = path.get("Step3Input2");
        String output = path.get("Step3Output2");

HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);
        hdfs.rmr(output);

conf.setOutputKeyClass(Text.class);
        conf.setOutputValueClass(IntWritable.class);

conf.setMapperClass(Step32_CooccurrenceColumnWrapperMapper.class);

conf.setInputFormat(TextInputFormat.class);
        conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(input));
        FileOutputFormat.setOutputPath(conf, new Path(output));

RunningJob job = JobClient.runJob(conf);
        while (!job.isComplete()) {
            job.waitForCompletion();
        }
    }

}

Step3运行结果：