This tutorial will walk you through the basic configurations and options of running Mrs in parallel. If you followed the installation instructions, then you should have already run the example WordCount program in serial mode and so have varified that Mrs is installed and running correctly on your system. Otherwise, you may want to do that before proceeding.
Start by opening two terminals, one for the master and one for the slave. In the slave terminal, ssh into the slave machine, and in both, navagate to the MRS_HOME/examples directory.
Before running anything let’s get familiar with the various options by running the help option.
> python wordcount.py --help
Running this command should display a list of the available options. Take note of the -I IMPLEMENTATION option where IMPLEMENTATION could be Serial, Master, Slave or Bypass. This will be followed by options specific to the default implimentation which is serial. To access the options for the other implimentations you will need to specify which, as in:
> python wordcount.py -I Master -h
Now, let’s run our friendly WordCount program in the most simple configuration and afterwords explain some of the other options that you might want to use. To begin, start the Master. You will need to specify a port number and of course the input file and an output directory. Note that if you don’t specify a port number, Mrs will automatically choose one, but you will need to run with the verbose option so it will print it out. You will probably want to run in verbose mode anyway.
> python wordcount.py -I Master -P 44555 --mrs-verbose mytxt.txt outDir
Next, start the Slave. The slave needs to know where to report to the master, which you can specify with the -M HOST:PORT option.
> python wordcount.py -I Slave -M [masterName]:44555 --mrs-verbose
And once again, if all went well, you should have the results in your outDir.
Now that you have run Mrs manually and are familiar with the Mrs interface you are ready to start with the clusterrun.py run-script. A run-script allows you to easily start the master and hundreds of slave machines all at once. We have included an example script in the MRS_HOME/examples directory. It is written in Python and uses the screen and pssh utilities. If you are not familiar with screen or pssh you might want to work through one of the many online tutorials or at least review their man pages before proceeding. You don’t need to be an expert but it could be very helpful to know the basic commands for manipulating a screen session such as creating, attaching/disattaching a screen session, and changing windows from within a session.
The example run-script (clusterrun.py) is pretty well documented and of course you can print out the run options with the help command.
> python clusterrun.py --help
To run the script you will need to modify the MRS_HOME/examples/slaves file. When the program is run the slaves file will be passed to the pssh program and should be a text file with the name of a slave machine on each line in the following format:
If the user name is left off, pssh will use the current user name, and for the port number, the ssh default will be used (port 22).
You will also need to set up passphraseless ssh between the master and slave machines before running the script. Instructions for how to do this are easly found online.
Finally once you have modified the slaves file and set up passphraseless ssh, you should be able to run it by passing it a host file, a mrs program and some input.
> python clusterrun.py --hosts slaves wordcount.py mytxt.txt
You do not need to specify an output folder as you did when running manually as all output is put in a folder named after the jobname. You can specify the job name with the - -jobname option, or if you don’t specify, Mrs will default to ‘newjob.’
The examples directory also includes a run-script called fulton.py that submits a job to a PBS scheduler. This script is customized for the Fulton Supercomputing Lab at BYU, so it will need to be modified for your particular use case. However, if you use a supercomputer with a batch scheduler, this example script should be a good starting point.
The WordCount program shows a Mrs program in it’s most simple form. In this tutorial, we will try to explain the basic format for a Mrs MapReduce program and some of the options for a more complex program.
The basic format for a Mrs MapReduce program looks something like this
import mrs class MrsProgram(mrs.MapReduce): def map(key, value): yield newkey, newvalue def reduce(key, values): yield newvalue if __name__ == '__main__': mrs.main(MrsProgram)
Here we create a class that extends the MapReduce class in the MRS_HOME/mrs/ mapreduce.py file, and overrides only the required map and reduce functions. Note that the mrs.MapReduce class provides a convenient way to write a program, but MapReduce programs are not required to inherit from it. The last section of code (the if __name__ == '__main__': section) just passes the program to Mrs to run.
In the map function in the WordCount program, we take in a chunk of text as the starting value, break it up into words and yield each word as a new key with a value of one (found one instance of that word). Then the reduce function sums all values associated with a particular key (word), and yields the result.
But what if, instead to reading in just one textfile and counting the words as we’ve been doing, you had thousands of files that you needed to process? This is a little more complicated, so you would probably want to override the input_data() function from mapreduce.py, by adding something like the following to your the WordCount program. (See wordcount2.py in the examples folder.)
def input_data(self, job): if len(self.args) < 2: print >>sys.stderr, "Requires input(s) and an output." return None inputs =  f = open(self.args) for line in f: inputs.append(line[:-1]) return job.file_data(inputs)
Now, you can just pass in a single file containing the path to each text file on a line of the input file, and it will read them all in.