hadoop debug script

A Hadoop job may consist of many map tasks and reduce tasks. Therefore, debugging a
Hadoop job is often a complicated process. It is a good practice to first test a Hadoop job
using unit tests by running it with a subset of the data.
However, sometimes it is necessary to debug a Hadoop job in a distributed mode. To support
such cases, Hadoop provides a mechanism called debug scripts. This recipe explains how to
use debug scripts.

A debug script is a shell script, and Hadoop executes the script whenever a task encounters
an error. The script will have access to the $script, $stdout, $stderr, $syslog, and
$jobconfproperties, as environment variables populated by Hadoop. You can find a
sample script from resources/chapter3/debugscript. We can use the debug scripts
to copy all the logfiles to a single location, e-mail them to a single e-mail account, or perform
some analysis.
LOG_FILE=HADOOP_HOME/error.log
echo "Run the script" >> $LOG_FILE
echo $script >> $LOG_FILE
echo $stdout>> $LOG_FILE
echo $stderr>> $LOG_FILE
echo $syslog >> $LOG_FILE
echo $jobconf>> $LOG_FILE

when you execute this, you should pay attention to the execute path, or else it will not found debug script.

package chapter3;
 
import java.net.URI;
 
import org.apache.hadoop.filecache.DistributedCache;
import org.apache.hadoop.fs.FileStatus;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 
public class WordcountWithDebugScript {
    private static final String scriptFileLocation = "resources/chapter3/debugscript";
    private static final String HDFS_ROOT = "/debug";
 
    public static void setupFailedTaskScript(JobConf conf) throws Exception {
 
        // create a directory on HDFS where we'll upload the fail scripts
        FileSystem fs = FileSystem.get(conf);
        // Path debugDir = new Path("/debug");
        Path debugDir = new Path(HDFS_ROOT);
 
        // who knows what's already in this directory; let's just clear it.
        if (fs.exists(debugDir)) {
            fs.delete(debugDir, true);
        }
 
        // ...and then make sure it exists again
        fs.mkdirs(debugDir);
 
        // upload the local scripts into HDFS
        fs.copyFromLocalFile(new Path(scriptFileLocation), new Path(HDFS_ROOT
                + "/fail-script"));
 
        FileStatus[] list = fs.listStatus(new Path(HDFS_ROOT));
        if (list == null || list.length == 0) {
            System.out.println("No File found");
        } else {
            for (FileStatus f : list) {
                System.out.println("File found " + f.getPath());
            }
        }
 
        conf.setMapDebugScript("./fail-script");
        conf.setReduceDebugScript("./fail-script");
        // this create a simlink from the job directory to cache directory of
        // the mapper node
        DistributedCache.createSymlink(conf);
 
        URI fsUri = fs.getUri();
 
        String mapUriStr = fsUri.toString() + HDFS_ROOT
                + "/fail-script#fail-script";
        System.out.println("added " + mapUriStr + "to distributed cache 1");
        URI mapUri = new URI(mapUriStr);
        // Following copy the map uri to the cache directory of the job node
        DistributedCache.addCacheFile(mapUri, conf);
    }
 
    public static void main(String[] args) throws Exception {
        JobConf conf = new JobConf();
        setupFailedTaskScript(conf);
        Job job = new Job(conf, "word count");
 
        job.setJarByClass(FaultyWordCount.class);
        job.setMapperClass(FaultyWordCount.TokenizerMapper.class);
        job.setReducerClass(FaultyWordCount.IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        FileSystem.get(conf).delete(new Path(args[1]), true);
        FileInputFormat.addInputPath(job, new Path(args[0]));
        FileOutputFormat.setOutputPath(job, new Path(args[1]));
        job.waitForCompletion(true);
    }
 
}

digest from mapreduce cookbook