In this assignment you’ll build a MapReduce library as a way to learn the Go programming language and as a way to learn about fault tolerance in distributed systems. In the first part you will write a simple MapReduce program. In the second part you will write a Master that hands out jobs to workers, and handles failures of workers. The interface to the library and the approach to fault tolerance is similar to the one described in the original MapReduce paper.
Please refer to Assignment 0.
You’ll implement this assignment (and all the assignments) in Go 1.19.1. The Go web site contains lots of tutorial information which you may want to look at. We supply you with a non-distributed MapReduce implementation, and a partial implementation of a distributed implementation (just the boring bits).
It’s your responsibility to install Go in your development environment. We recommend using your distribution’s package manager.
On OS X with homebrew:
$ brew install go
On Ubuntu/Debian:
$ sudo apt-get install golang
On Arch:
$ sudo pacman -S go
There is an input file kjv12.txt in src/main, which was downloaded from here. Compile the initial software we provide you with and run it with the downloaded input file:
$ export GOPATH=$HOME/4113
$ cd ~/4113/src/main
$ go run wc.go master kjv12.txt sequential
# command-line-arguments
./wc.go:11: missing return at end of function
./wc.go:15: missing return at end of function
The compiler produces two errors, because the implementation of the Map and Reduce functions is incomplete.
Modify Map and Reduce so that wc.go reports the number of occurrences of each word in alphabetical order.
$ go run wc.go master kjv12.txt sequential
Split kjv12.txt
Split read 4834757
DoMap: read split mrtmp.kjv12.txt-0 966954
DoMap: read split mrtmp.kjv12.txt-1 966953
DoMap: read split mrtmp.kjv12.txt-2 966951
DoMap: read split mrtmp.kjv12.txt-3 966955
DoMap: read split mrtmp.kjv12.txt-4 966944
DoReduce: read mrtmp.kjv12.txt-0-0
DoReduce: read mrtmp.kjv12.txt-1-0
DoReduce: read mrtmp.kjv12.txt-2-0
DoReduce: read mrtmp.kjv12.txt-3-0
DoReduce: read mrtmp.kjv12.txt-4-0
DoReduce: read mrtmp.kjv12.txt-0-1
DoReduce: read mrtmp.kjv12.txt-1-1
DoReduce: read mrtmp.kjv12.txt-2-1
DoReduce: read mrtmp.kjv12.txt-3-1
DoReduce: read mrtmp.kjv12.txt-4-1
DoReduce: read mrtmp.kjv12.txt-0-2
DoReduce: read mrtmp.kjv12.txt-1-2
DoReduce: read mrtmp.kjv12.txt-2-2
DoReduce: read mrtmp.kjv12.txt-3-2
DoReduce: read mrtmp.kjv12.txt-4-2
Merge phaseMerge: read mrtmp.kjv12.txt-res-0
Merge: read mrtmp.kjv12.txt-res-1
Merge: read mrtmp.kjv12.txt-res-2
The output will be in the file “mrtmp.kjv12.txt”. Your implementation is correct if the following command produces the following top 10 words:
$ sort -n -k2 mrtmp.kjv12.txt | tail -10
unto: 8940
he: 9666
shall: 9760
in: 12334
that: 12577
And: 12846
to: 13384
of: 34434
and: 38850
the: 62075
To make testing easy for you, run:
$ ./test-wc.sh
and it will report if your solution is correct or not.
Before you start coding reread Section 2 of the MapReduce paper and our code for MapReduce, which is in mapreduce.go in package mapreduce. In particular, you want to read the code of the function RunSingle and the functions it calls. This will help you understand what MapReduce does and learn Go by example.
Once you understand this code, implement Map and Reduce in wc.go.
Hint: you can use strings.FieldsFunc to split a string into components.
Hint: for the purposes of this exercise, you can consider a word to be any contiguous sequence of letters, as determined by unicode.IsLetter. A good read on what strings are in Go is the Go Blog on strings.
Hint: the strconv package (http://golang.org/pkg/strconv/) is handy to convert strings to integers, etc.
You can remove the output file and all intermediate files with:
$ rm mrtmp.*
$ git commit -am "[you fill me in]"
$ git tag -a -m "i finished assignment 1 part 1" a1p1
$ git push origin master
$ git push origin a1p1
$
In this part you will design and implement a master who distributes jobs to a set of workers. We give you the code for the RPC messages (see common.go in the mapreduce package) and the code for a worker (see worker.go in the mapreduce package).
Your job is to complete master.go in the mapreduce package. In particular, the RunMaster() function in master.go should return only when all of the map and reduce tasks have been executed. This function will be invoked from the Run() function in mapreduce.go.
The code in mapreduce.go already implements the MapReduce.Register RPC function for you, and passes the new worker’s information to mr.registerChannel. You should process new worker registrations by reading from this channel.
Information about the MapReduce job is in the MapReduce struct, defined in mapreduce.go. Modify the MapReduce struct to keep track of any additional state (e.g., the set of available workers), and initialize this additional state in the InitMapReduce() function. The master does not need to know which Map or Reduce functions are being used for the job; the workers will take care of executing the right code for Map or Reduce.
In Part II, you don’t have to worry about the failures of the workers. You are done with Part II when your implementation passes the first test set in test_test.go in the mapreduce package.
test_test.go uses Go’s unit testing. From now on all exercises (including subsequent assignments) will use it, but you can always run the actual programs from the main directory. You run unit tests in a package directory as follows:
$ go test
The master should send RPCs to the workers in parallel so that the workers can work on jobs concurrently. You will find the go statement useful for this purpose, and so is the Go RPC documentation.
The master may have to wait for a worker to finish before it can hand out more jobs. You may find channels useful to synchronize the threads that are waiting for reply with the master once the reply arrives. Channels are explained in the document on Concurrency in Go.
We’ve given you code that sends RPCs via “UNIX-domain sockets”. This means that RPCs only work between processes on the same machine. It would be easy to convert the code to use TCP/IP-based RPC instead, so that it would communicate between machines; you’d have to change the first argument to calls to Listen() and Dial() to “tcp” instead of “unix”, and the second argument to a port number like “:5100”. You will need a shared distributed file system.
The easiest way to track down bugs is to insert log.Printf() statements, collect the output in a file with go test > out, and then think about whether the output matches your understanding of how your code should behave. The last step is most important.
Please let us know that you’ve gotten this far in the assignment, by pushing a tag to github.
$ git commit -am "[you fill me in]"
$ git tag -a -m "i finished assignment 1 part 2" a1p2
$ git push origin master
$ git push origin a1p2
In this part you will make the master handle worker failures. In MapReduce handling failures of workers is relatively straightforward, because the workers don’t have persistent state. If the worker fails, any RPCs that the master issued to that worker will fail (e.g., due to a timeout). Thus, if the master’s RPC to the worker fails, the master should re-assign the job given to the failed worker to another worker.
An RPC failure doesn’t necessarily mean that the worker failed; the worker may just be unreachable but still computing. Thus, it may happen that two workers receive the same job and compute it. However, because jobs are idempotent, it doesn’t matter if the same job is computed twice - both times it will generate the same output. So, you don’t have to do anything special for this case. (Our tests never fail workers in the middle of job, so you don’t even have to worry about several workers writing to the same output file.)
You don’t have to handle failures of the master; we will assume it won’t fail. Making the master fault tolerant is more difficult because it keeps persistent state that must be replicated in order to make the master fault tolerant. Keeping replicated state consistent in the presence of failures is challenging. Much of the later assignments is devoted to this challenge.
Your implementation must pass two remaining test cases in test_test.go. The first case tests the failure of one worker. The second test case tests handling of many failures of workers. Periodically, the test cases start new workers that the master can use to make progress, but these workers fail after handling a few jobs.
You hand in your assignment exactly as you’ve been letting us know your progress:
$ git commit -am "[you fill me in]"
$ git tag -a -m "i finished assignment 1" a1handin
$ git push origin master
$ git push origin a1handin
You should verify that you are able to see your final commit and your a1handin tag on the Github page in your repository for this assignment.
You will receive full credit if your software passes the test_test.go tests when we run your software on our machines. We will use the timestamp of your last a1handin tag for the purpose of calculating late days, and we will only grade that version of the code. (We’ll also know if you backdate the tag, don’t do that.)
go test -run "^${test_case}$" -timeout 2mtest_test.go, your code shouldn’t have a dependency on it.