Multiple Inputs with MRJob

If you're in need of processing your raw data against another (or same row_i, row_j) data set, you can either:

1) Create an S3 bucket to store a copy of your data. Pass the location of this copy to your task class, e.g. self.options.bucket and self.options.my_datafile_copy_location in the code below. Caveat: Unfortunately, it seems that the whole file must get "downloaded" to the task machines before getting processed. If the connections falters or takes too long to load, this job may fail. Here is some Python/MRJob code to do this.

Put this in your mapper function:

d1 = line1.split('\t', 1)
v1, col1 = d1[0], d1[1]
conn = boto.connect_s3(aws_access_key_id=<AWS_ACCESS_KEY_ID>, aws_secret_access_key=<AWS_SECRET_ACCESS_KEY>)
bucket = conn.get_bucket(self.options.bucket)  # bucket = conn.get_bucket(MY_UNIQUE_BUCKET_NAME_AS_STRING)
data_copy = bucket.get_key(self.options.my_datafile_copy_location).get_contents_as_string().rstrip()
### CAVEAT: Needs to get the whole file before processing the rest.
for line2 in data_copy.split('\n'):
    d2 = line2.split('\t', 1)
    v2, col2 = d2[0], d2[1]
    ## Now, insert code to do any operations between v1 and v2 (or c1 and c2) here:
    yield <your output key, value pairs>
conn.close()

2) Create a SimpleDB domain, and store all of your data in there. Read here on boto and SimpleDB: http://code.google.com/p/boto/wiki/SimpleDbIntro

Your mapper code would look like this:

dline = dline.strip()
d0 = dline.split('\t', 1)
v1, c1 = d0[0], d0[1]
sdb = boto.connect_sdb(aws_access_key_id=<AWS_ACCESS_KEY>, aws_secret_access_key=<AWS_SECRET_ACCESS_KEY>)
domain = sdb.get_domain(MY_DOMAIN_STRING_NAME)
for item in domain:
    v2, c2 = item.name, item['column']
    ## Now, insert code to do any operations between v1 and v2 (or c1 and c2) here:
    yield <your output key, value pairs>
sdb.close()

This second option may perform better if you have very large amounts of data, since it can make the requests for each row of data rather than the whole amount at once. Keep in mind that SimpleDB values can only be a maximum of 1024 characters long, so you may need to compress/decompress via some method if your data values are longer than that.

The actual answer to your question is that mrjob does not quite yet support the hadoop streaming join pattern, which is to read the map_input_file environment variable (which exposes the map.input.file property) to determine which type of file you are dealing with based on its path and/or name.

You might still be able to pull it off, if you can easily detect from just reading the data itself which type it belongs to, as is displayed in this article:

http://allthingshadoop.com/2011/12/16/simple-hadoop-streaming-tutorial-using-joins-and-keys-with-python/

However that's not always possible...

Otherwise myjob looks fantastic and I wish they could add support for this in the future. Until then this is pretty much a deal breaker for me.

This is how I use multiple inputs and based on filename make suitable changes in the mapper phase.

Runner Program :

from mrjob.hadoop import *


#Define all arguments

os.environ['HADOOP_HOME'] = '/opt/cloudera/parcels/CDH/lib/hadoop/'
print "HADOOP HOME is now set to : %s" % (str(os.environ.get('HADOOP_HOME')))
job_running_time = datetime.datetime.now().strftime('%Y-%m-%d_%H_%M_%S')
hadoop_bin = '/usr/bin/hadoop'
mode = 'hadoop'
hs = HadoopFilesystem([hadoop_bin])

input_file_names = ["hdfs:///app/input_file1/","hdfs:///app/input_file2/"]

aargs = ['-r',mode,'--jobconf','mapred.job.name=JobName','--jobconf','mapred.reduce.tasks=3','--no-output','--hadoop-bin',hadoop_bin]
aargs.extend(input_file_names)
aargs.extend(['-o',output_dir])
print aargs
status_file = True

mr_job = MRJob(args=aargs)
with mr_job.make_runner() as runner:
    runner.run()
os.environ['HADOOP_HOME'] = ''
print "HADOOP HOME is now set to : %s" % (str(os.environ.get('HADOOP_HOME')))

The MRJob Class :

class MR_Job(MRJob):
    DEFAULT_OUTPUT_PROTOCOL = 'repr_value'
    def mapper(self, _, line):
    """
    This function reads lines from file.
    """
    try:
        #Need to clean email.
        input_file_name = get_jobconf_value('map.input.file').split('/')[-2]
                """
                Mapper code
                """
    except Exception, e:
        print e

    def reducer(self, email_id,visitor_id__date_time):
    try:
        """
                Reducer Code
                """
    except:
        pass


if __name__ == '__main__':
    MRV_Email.run()