Lab 1: MapReduce

Important Dates and Other Stuff

Due ~~Tuesday, 09/28, 11:59pm.~~

Due Thursday, 09/30, 11:59pm.

Introduction

In this lab 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 tasks 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.

Software

You’ll implement this lab in Go. The Go web site contains lots of tutorial information which you may want to look at.

We supply you with parts of a MapReduce implementation that supports both distributed and non-distributed operation (just the boring bits). You can download the software skeleton from https://tddg.github.io/cs475-fall21/public/lab1.tar. To download the tar file from Zeus:

% cd $HOME
% wget https://tddg.github.io/cs475-fall21/public/lab1.tar
% tar -xvf lab1.tar
% ls
src/

Copy src/ into your CS475 lab directory:

% cp -r src $HOME/cs475-fall21/labs/
% cd $HOME/cs475-fall21/labs/
% ls
lab0/ src/

Now your local Git reporitory should contain two separate sub-directories, lab0/ and src/. src/ further contains two sub-directories, main/ and mapreduce/. You will be working on src/ for this lab assignment.

You can also view a snapshot of Lab 1’s src from https://git.gmu.edu/cs475-fall21/labs/-/tree/master/src.

You must make sure your code runs on x86 or x86_64 machines: that is, uname -a should mention i386 GNU/Linux or i686 GNU/Linux or x86_64 GNU/Linux. Autolab is running on an x86_64 cloud server, so you should test your submission on Zeus before submitting on Autolab.

Preamble: Getting familiar with the source

The MapReduce implementation we give you has support for two modes of operation, sequential and distributed. In the former, the map and reduce tasks are all executed serially: first, the first map task is executed to completion, then the second, then the third, etc. When all the map tasks have finished, the first reduce task is run, then the second, etc. This mode, while not very fast, can be very useful for debugging, since it removes much of the noise seen in a parallel execution. The distributed mode runs many worker threads that first execute map tasks in parallel, and then reduce tasks. This is much faster, but also harder to implement and debug.

The mapreduce package provides a simple MapReduce library with a sequential implementation. Applications should normally call Distributed() [located in master.go] to start a job, but may instead call Sequential() [also in master.go] to get a sequential execution for debugging purposes.

The flow of the mapreduce implementation is as follows:

The application provides a number of input files, a map function, a reduce function, and the number of reduce tasks (nReduce).
A master is created with this knowledge. It spins up an RPC server (see master_rpc.go), and waits for workers to register (using the RPC call Register() [defined in master.go]). As tasks become available (in steps 4 and 5), schedule() [schedule.go] decides how to assign those tasks to workers, and how to handle worker failures.
The master considers each input file one map task, and makes a call to doMap() [common_map.go] at least once for each task. It does so either directly (when using Sequential()) or by issuing the DoTask RPC on a worker [worker.go]. Each call to doMap() reads the appropriate file, calls the map function on that file’s contents, and writes the resulting key/value pairs to nReduce intermediate files. doMap() hashes each key to pick the intermediate file and thus the reduce task that will process the key. There will be nMap X nReduce files after all map tasks are done. Each file name contains a prefix, the map task number, and the reduce task number. If there are two map tasks and three reduce tasks, the map tasks will create these six intermediate files:
```
 mrtmp.xxx-0-0
 mrtmp.xxx-0-1
 mrtmp.xxx-0-2
 mrtmp.xxx-1-0
 mrtmp.xxx-1-1
 mrtmp.xxx-1-2
```
Each worker must be able to read files written by any other worker, as well as the input files. Real deployments use distributed storage systems such as GFS (for input) and transfer files (for map-to-reduce intermediate results) between workers to allow this access even though workers run on different machines. In this lab you’ll run all the workers on the same machine, and use the local file system.
The master next makes a call to doReduce() [common_reduce.go] at least once for each reduce task. The doReduce() for reduce task r collects the rth intermediate file from each map task, and calls the reduce function for each key that appears in those files. The reduce tasks produce nReduce result files.
The master calls mr.merge() [master_splitmerge.go], which merges all the nReduce files produced by the previous step into a single output.
The master sends a Shutdown RPC to each of its workers, and then shuts down its own RPC server.

NOTE: Over the course of the following exercises, you will have to write/modify doMap, doReduce, and schedule yourself. These are located in common_map.go, common_reduce.go, and schedule.go respectively. You will also have to write the map and reduce functions in ../main/wc.go.

You should not need to modify any other files, but reading them might be useful in order to understand how the other methods fit into the overall architecture of the system.

Part I: MapReduce input and output

The MapReduce implementation you are given is missing some pieces. Before you can write your first MapReduce function pair, you will need to fix the sequential implementation. In particular, the code we give you is missing two crucial pieces: the function that divides up the output of a map task, and the function that gathers all the inputs for a reduce task. These tasks are carried out by the doMap() function in common_map.go, and the doReduce() function in common_reduce.go respectively. The comments in those files should point you in the right direction.

To help you determine if you have correctly implemented doMap() and doReduce(), we have provided you with a Go test suite that checks the correctness of your implementation. These tests are implemented in the file test_test.go. To run the tests for the sequential implementation that you have now fixed, run:

$ cd cs475-fall21/labs
$ export GOPATH="$PWD" # Must do this to tell go where the lab top-level is.
$ cd src/mapreduce
$ go test -run Sequential mapreduce/...
ok  	mapreduce	2.451s

If the output did not show ok next to the tests, your implementation has a bug in it. To give more verbose output, set debugEnabled = true in common.go, and add -v to the test command above. You will get much more output along the lines of:

$ go test -v -run Sequential mapreduce/... 
=== RUN   TestSequentialSingle
master: Starting Map/Reduce task test
Merge: read mrtmp.test-res-0
master: Map/Reduce task completed
--- PASS: TestSequentialSingle (1.54s)
=== RUN   TestSequentialMany
master: Starting Map/Reduce task test
Merge: read mrtmp.test-res-0
Merge: read mrtmp.test-res-1
Merge: read mrtmp.test-res-2
master: Map/Reduce task completed
--- PASS: TestSequentialMany (1.47s)
PASS
ok  	mapreduce	2.824s

Part II: Single-worker word count

Now that the map and reduce tasks are connected, we can start implementing some interesting MapReduce operations. For this lab, we will be implementing word count — a simple and classical MapReduce example. Specifically, your task is to modify mapF and reduceF so that wc.go reports the number of occurrences of each word. A word is any contiguous sequence of letters, as determined by unicode.IsLetter.

There are some input files with pathnames of the form pg-*.txt in src/main, downloaded from Project Gutenberg. Go ahead and try to compile the initial software we provide you and run it with the provided input files:

$ cd "$GOPATH/src/main"
$ go run wc.go master sequential pg-*.txt
# command-line-arguments
./wc.go:14: missing return at end of function
./wc.go:21: missing return at end of function

The compilation fails because we haven’t written a complete map function (mapF()) and reduce function (reduceF()) in wc.go yet. Before you start coding read Section 2 of the MapReduce paper. Your mapF() and reduceF() functions will differ a bit from those in the paper’s Section 2.1. Your mapF() will be passed the name of a file, as well as that file’s contents; it should split it into words, and return a Go slice of key/value pairs, of type mapreduce.KeyValue. Your reduceF() will be called once for each key, with a slice of all the values generated by mapF() for that key; it should return a single output value.

Hint: a good read on what strings are in Go is the Go Blog on strings.

Hint: you can use strings.FieldsFunc to split a string into components.

Hint: the strconv package (http://golang.org/pkg/strconv/) is handy to convert strings to integers etc.

You can test your solution using:

$ cd "$GOPATH/src/main"
$ time go run wc.go master sequential pg-*.txt
master: Starting Map/Reduce task wcseq
Merge: read mrtmp.wcseq-res-0
Merge: read mrtmp.wcseq-res-1
Merge: read mrtmp.wcseq-res-2
master: Map/Reduce task completed
14.59user 3.78system 0:14.81elapsed

The output will be in the file “mrtmp.wcseq”. You can remove the output file and all intermediate files with:

$ rm mrtmp.*

Your implementation is correct if the following command produces the following top 10 words:

$ sort -n -k2 mrtmp.wcseq | tail -10
he: 34077
was: 37044
that: 37495
I: 44502
in: 46092
a: 60558
to: 74357
of: 79727
and: 93990
the: 154024

To make testing easy for you, run:

$ sh ./test-wc.sh

and it will report if your solution is correct or not.

Part III: Distributing MapReduce tasks

Your current implementation runs the map and reduce tasks one at a time. One of MapReduce’s biggest selling points is that it can automatically parallelize ordinary sequential code without any extra work by the developer. In this part of the lab, you will complete a version of MapReduce that splits the work over a set of worker threads that run in parallel on multiple cores. While not distributed across multiple machines as in real MapReduce deployments, your implementation will use RPC to simulate distributed computation.

The code in mapreduce/master.go does most of the work of managing a MapReduce job. We also supply you with the complete code for a worker thread, in mapreduce/worker.go, as well as some code to deal with RPC in mapreduce/common_rpc.go.

Your job is to implement schedule() in mapreduce/schedule.go. The master calls schedule() twice during a MapReduce job, once for the Map phase, and once for the Reduce phase. schedule()’s job is to hand out tasks to the available workers. There will usually be more tasks than worker threads, so schedule() must give each worker a sequence of tasks, one at a time. schedule() should wait until all tasks have completed, and then return.

schedule() learns about the set of workers by reading its registerChan argument. That channel yields a string for each worker, containing the worker’s RPC address. Some workers may exist before schedule() is called, and some may start while schedule() is running; all will appear on registerChan. schedule() should use all the workers, including ones that appear after it starts.

schedule() tells a worker to execute a task by sending a Worker.DoTask RPC to the worker. This RPC’s arguments are defined by DoTaskArgs in mapreduce/common_rpc.go. The File element is only used by Map tasks, and is the name of the file to read; schedule() can find these file names in mapFiles.

Use the call() function in mapreduce/common_rpc.go to send an RPC to a worker. The first argument is the the worker’s address, as read from registerChan. The second argument should be "Worker.DoTask". The third argument should be the DoTaskArgs structure, and the last argument should be nil.

Your solution to Part III should only involve modifications to schedule.go. If you modify other files as part of debugging, please restore their original contents and then test before submitting.

To test your solution, you should use the same Go test suite as you did in Part I, except swapping out -run Sequential with -run TestBasic. This will execute the distributed test case without worker failures instead of the sequential ones we were running before:

$ go test -run TestBasic mapreduce/...

Hint: RPC package documents the Go RPC package.

Hint: schedule() should send RPCs to the workers in parallel so that the workers can work on tasks concurrently. You will find the go statement useful for this purpose; see Concurrency in Go.

Hint: schedule() must wait for a worker to finish before it can give it another task. You may find Go’s channels useful. You may find sync.WaitGroup useful.

Hint: The easiest way to track down bugs is to insert print state statements (perhaps calling debug() in common.go), collect the output in a file with go test -run TestBasic > out, and then think about whether the output matches your understanding of how your code should behave. The last step (thinking) is the most important.

Hint: To check if your code has race conditions, run Go’s race detector with your test: go test -race -run TestBasic > out.

NOTE: The code we give you runs the workers as threads within a single UNIX process, and can exploit multiple cores on a single machine. Some modifications would be needed in order to run the workers on multiple machines communicating over a network. The RPCs would have to use TCP rather than UNIX-domain sockets; there would need to be a way to start worker processes on all the machines; and all the machines would have to share storage through some kind of network file system.

Part IV: Handling worker failures

In this part you will make the master handle failed workers. MapReduce makes this relatively easy because workers don’t have persistent state. If a 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 task given to the failed worker to another worker.

An RPC failure doesn’t necessarily mean that the worker didn’t execute the task; the worker may have executed it but the reply was lost, or the worker may still be executing but the master’s RPC timed out. Thus, it may happen that two workers receive the same task, compute it, and generate output. Two invocations of a map or reduce function are required to generate the same output for a given input (i.e. the map and reduce functions are “functional”), so there won’t be inconsistencies if subsequent processing sometimes reads one output and sometimes the other. In addition, the MapReduce framework ensures that map and reduce function output appears atomically: the output file will either not exist, or will contain the entire output of a single execution of the map or reduce function (the lab code doesn’t actually implement this, but instead only fails workers at the end of a task, so there aren’t concurrent executions of a task).

NOTE: 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 would have to be recovered in order to resume operations after a master failure.

Your implementation must pass the two remaining test cases in test_test.go. The first case tests the failure of one worker, while 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 forward progress, but these workers fail after handling a few tasks. To run these tests:

$ go test -run Failure mapreduce/...

Part V: Inverted index generation (optional challenge)

Word count is a classical example of a MapReduce application, but it is not an application that many large consumers of MapReduce use. It is simply not very often you need to count the words in a really large dataset. For this challenge exercise, we will instead have you build Map and Reduce functions for generating an inverted index.

Inverted indices are widely used in computer science, and are particularly useful in document searching. Broadly speaking, an inverted index is a map from interesting facts about the underlying data, to the original location of that data. For example, in the context of search, it might be a map from keywords to documents that contain those words.

We have created a second binary in main/ii.go that is very similar to the wc.go you built earlier. You should modify mapF and reduceF in main/ii.go so that they together produce an inverted index. Running ii.go should output a list of tuples, one per line, in the following format:

$ go run ii.go master sequential pg-*.txt
$ head -n5 mrtmp.iiseq
A: 16 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-metamorphosis.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
ABC: 2 pg-les_miserables.txt,pg-war_and_peace.txt
ABOUT: 2 pg-moby_dick.txt,pg-tom_sawyer.txt
ABRAHAM: 1 pg-dracula.txt
ABSOLUTE: 1 pg-les_miserables.txt

If it is not clear from the listing above, the format is:

word: #documents documents,sorted,and,separated,by,commas

For this optional task, you must pass test-ii.sh, which runs:

$ sort -k1,1 mrtmp.iiseq | sort -snk2,2 mrtmp.iiseq | grep -v '16' | tail -10
women: 15 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-metamorphosis.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
won: 15 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-metamorphosis.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
wonderful: 15 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
words: 15 pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-metamorphosis.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
worked: 15 pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-metamorphosis.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
worse: 15 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
wounded: 15 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
yes: 15 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-metamorphosis.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
younger: 15 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt
yours: 15 pg-being_ernest.txt,pg-dorian_gray.txt,pg-dracula.txt,pg-emma.txt,pg-frankenstein.txt,pg-great_expectations.txt,pg-grimm.txt,pg-huckleberry_finn.txt,pg-les_miserables.txt,pg-moby_dick.txt,pg-sherlock_holmes.txt,pg-tale_of_two_cities.txt,pg-tom_sawyer.txt,pg-ulysses.txt,pg-war_and_peace.txt

Running all tests

You can run all the tests by running the script src/main/test-mr.sh. With a correct solution, your output should resemble:

$ sh ./test-mr.sh
==> Part I
ok  	mapreduce	3.234s

==> Part II
Passed test

==> Part III
ok  	mapreduce	2.026s

==> Part IV
ok  	mapreduce	10.462s

==> Part V (challenge)
Passed test

Note that your Zeus tests may show different execution times than what is shown above.

Please post questions on Ed.

Point distribution

Test	Points
Part I (Sequential)	5
Part II (./test-wc.sh)	5
Part III (TestBasic)	5
Part IV (Failure)	5

Your code will be tested on Autolab. No marks will be awarded if your code does not pass the test. You will receive full marks only if your code successfully passes the test.

What (and how) to hand in

Submitting on Autolab

You must turn in your lab assignment using Autolab. Read this document for instructions on how to sign-up for Autolab. If you did not receive a confirmation email from Autolab to set a password, enter your @gmu.edu (NOT masonlive) email, and click “Forgot your password” to get a new password. You may skip the Autolab signup step if you have done this in lab0.

Create a tar file of only the following Go source files, common_map.go, common_reduce.go, wc.go, and schedule.go. Please, .tar only, not .tgz, nor .7z/.zip. Name your tar file as lab1-handin.tar.

% tar -cvf lab1-handin.tar common_map.go common_reduce.go schedule.go wc.go

Please do not put any directory in your tar file as our autograder is scripted to directly fetch src files not directories. Use the following command to examine the content of your tar file:

% tar -tvf lab1-handin.tar
-rw-r--r--  0 yue    staff    5781 Aug 30 16:38 common_map.go
-rw-r--r--  0 yue    staff    2911 Aug 30 16:38 common_reduce.go
-rw-r--r--  0 yue    staff    2911 Aug 30 16:38 schedule.go
-rw-r--r--  0 yue    staff    2911 Aug 30 16:38 wc.go

When you upload your assignment, Autolab will automatically untar it and test it. You should verify that the result that Autolab generates is what you expect. Test your code on Zeus before submitting it to Autolab. Your code is tested in a cloud Linux VM. Assignments that do not compile or run will receive a maximum of 50%. Note that we have provided ample resources for you to verify that our view of your assignment is the same as your own: you will see the result of the test execution for your assignment when you submit it.

You can resubmit your assignment an unlimited number of times before the deadline. Note the late submission policy: assignments will be accepted up until 3 days past the deadline at a penalty of 10% per late day; after 3 days, no late assignments will be accepted, no exceptions.

You will need to share your private repository with our GTA Rui (his GitLab ID is the same as his Mason Email ID: ryang22). You may skip the above sharing step if you have done it already for lab0.

The final, important step: Don’t forget to commit all your changes:

% git commit -am 'the final awesome solution of lab1: [Your Name] and [Your GMU email]'

Acknowledgment

The lab assignment is adapted from MIT’s 6.824 course. Thanks to Frans Kaashoek, Robert Morris, and Nickolai Zeldovich for their support.

CS 475 Concurrent & Distributed Systems (Fall 2021)