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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.

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

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/phaseTryNumber1",
                    "-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 --create --alive --credentials ~/$USER-credentials.json --num-instances=1 --master-instance-type=m1.large --name phaseTry1  --json phase.json 

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 ~/ndsage-credentials.json --jobflow j-GA47B7VD991Q --list

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 ~/ndsage-credentials.json --jobflow j-GA47B7VD991Q --json phase.json 
Added jobflow steps

Get your results

Look in your S3 bucket for the results.

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