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In this example, we are not using MapReduce to its full potential. We are only using it to run jobs in parallel, one job for each chromosome. The phase algorithm from UW writes its output to local files instead of stdout.

Mapper

~>cat phaseMapper.sh
#!/bin/sh

RESULT_BUCKET=s3://sagetest-YourUsername/results

while read S3_INPUT_FILE; do
    echo input to process ${S3_INPUT_FILE} 1>&2 

    # For debugging purposes, print out the files cached for us 
    ls -la 1>&2

    # Parse the s3 file path to get the file name
    LOCAL_INPUT_FILE=$(echo ${S3_INPUT_FILE} | perl -pe 'if (/^((s3[n]?):\/)?\/?([^:\/\s]+)((\/\w+)*\/)([\w\-\.]+[^#?\s]+)(.*)?(#[\w\-]+)?$/) {print "$6\n"};' | head -1)

    # Download the file from S3
    echo hadoop fs -get ${S3_INPUT_FILE} ${LOCAL_INPUT_FILE} 1>&2
    hadoop fs -get ${S3_INPUT_FILE} ${LOCAL_INPUT_FILE} 1>&2

    # Run phase processing
    ./phase ${LOCAL_INPUT_FILE} ${LOCAL_INPUT_FILE}_out 100 1 100

    # Upload the output files
    ls -la ${LOCAL_INPUT_FILE}*_out* 1>&2
    for f in ${LOCAL_INPUT_FILE}*_out*
    do
        echo hadoop fs -put $f ${RESULT_BUCKET}/$LOCAL_INPUT_FILE/$f 1>&2
        hadoop fs -put $f ${RESULT_BUCKET}/$LOCAL_INPUT_FILE/$f 1>&2
    done
    echo processed ${S3_INPUT_FILE} 1>&2
    echo 1>&2
    echo 1>&2
done

exit 0

Upload it to S3 via the AWS console or s3curl

/work/platform/bin/s3curl.pl --id $USER --put phaseMapper.sh https://s3.amazonaws.com/sagetest-$USER/scripts/phaseMapper.sh

Reducer

~>cat echoReducer.sh 
#!/bin/sh

while read LINE; do
    echo ${LINE} 1>&2 
    echo ${LINE}
done

exit 0

Upload it to S3 via the AWS console or s3curl

/work/platform/bin/s3curl.pl --id $USER --put echoReducer.sh https://s3.amazonaws.com/sagetest-$USER/scripts/echoReducer.sh

Input

~>cat phaseInput.txt 
s3://sagetest-YourUsername/input/ProSM_chrom_MT.phase.inp
... many more files, one per chromosome

Upload it to S3 via the AWS console or s3curl

/work/platform/bin/s3curl.pl --id $USER --put phaseInput.txt https://s3.amazonaws.com/sagetest-$USER/input/phaseInput.txt

Also upload all the data files referenced in phaseInput.txt to the location specified in that file.

Run the MapReduce Job

Job Configuration

~>cat phase.json 
[       
    {     
        "Name": "MapReduce Step 1: Run Phase",
        "ActionOnFailure": "CANCEL_AND_WAIT",
        "HadoopJarStep": {
            "Jar": "/home/hadoop/contrib/streaming/hadoop-streaming.jar",
                "Args": [
                    "-input",     "s3n://sagetest-YourUsername/input/phaseInput.txt",
                    "-output",    "s3n://sagetest-YourUsername/output/phaseTry1",
                    "-mapper",    "s3n://sagetest-YourUsername/scripts/phaseMapper.sh",
                    "-reducer",   "s3n://sagetest-YourUsername/scripts/echoReducer.sh",
                    "-cacheFile", "s3n://sagetest-YourUsername/scripts/phase#phase",
                    "-jobconf",   "mapred.map.tasks=1",
                    "-jobconf",   "mapred.reduce.tasks=1",
                ]
            }
    }
]

Start the MapReduce cluster

~>/work/platform/bin/elastic-mapreduce-cli/elastic-mapreduce --credentials
~/$USER-credentials.json --create --num-instances=1 --master-instance-type=m1.large  --json phase.json --name phaseTry1

Created job flow j-GA47B7VD991Q

Check on the job status

If something is misconfigured, it will fail in a minute or two. Check on the job status and make sure it is running.

~>/work/platform/bin/elastic-mapreduce-cli/elastic-mapreduce --credentials
~/$USER-credentials.json --list --jobflow j-GA47B7VD991Q

j-GA47B7VD991Q     RUNNING        ec2-174-129-134-200.compute-1.amazonaws.com       filesysTry1
   RUNNING        MapReduce Step 1: Run Phase

If there were any errors, make corrections and resubmit the job step

~>/work/platform/bin/elastic-mapreduce-cli/elastic-mapreduce --credentials ~/$USER-credentials.json --json phase.json --jobflow j-GA47B7VD991Q 
Added jobflow steps

Get your results

Look in your S3 bucket for the results.

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