6.824 Lab 1: MapReduce

Introduction

In this lab you'll build a MapReduce system. You'll implement a worker process that calls application Map and Reduce functions and handles reading and writing files, and a coordinator process that hands out tasks to workers and copes with failed workers. You'll be building something similar to the MapReduce paper. (Note: the lab uses "coordinator" instead of the paper's "master".)

Getting started

You need to setup Go to the labs.

You'll fetch the initial lab software with git (a version control system). To learn more about git, look at the Pro Git book or the git user's manual. To fetch the 6.824 lab software:

$ git clone git://g.csail.mit.edu/6.824-golabs-2021 6.824
$ cd 6.824
$ ls
Makefile src
$

We supply you with a simple sequential mapreduce implementation in src/main/mrsequential.go. It runs the maps and reduces one at a time, in a single process. We also provide you with a couple of MapReduce applications: word-count in mrapps/wc.go, and a text indexer in mrapps/indexer.go. You can run word count sequentially as follows:

$ cd ~/6.824
$ cd src/main
$ go build -race -buildmode=plugin ../mrapps/wc.go
$ rm mr-out*
$ go run -race mrsequential.go wc.so pg*.txt
$ more mr-out-0
A 509
ABOUT 2
ACT 8
...

mrsequential.go leaves its output in the file mr-out-0. The input is from the text files named pg-xxx.txt.

Feel free to borrow code from mrsequential.go. You should also have a look at mrapps/wc.go to see what MapReduce application code looks like.

Your Job

We have given you a little code to start you off. The "main" routines for the coordinator and worker are in main/mrcoordinator.go and main/mrworker.go; don't change these files. You should put your implementation in mr/coordinator.go, mr/worker.go, and mr/rpc.go.

Here's how to run your code on the word-count MapReduce application. First, make sure the word-count plugin is freshly built:

$ go build -race -buildmode=plugin ../mrapps/wc.go

$ rm mr-out*
$ go run -race mrcoordinator.go pg-*.txt

In one or more other windows, run some workers:

$ go run -race mrworker.go wc.so

$ cat mr-out-* | sort | more
A 509
ABOUT 2
ACT 8
...

We supply you with a test script in main/test-mr.sh. The tests check that the wc and indexer MapReduce applications produce the correct output when given the pg-xxx.txt files as input. The tests also check that your implementation runs the Map and Reduce tasks in parallel, and that your implementation recovers from workers that crash while running tasks.

If you run the test script now, it will hang because the coordinator never finishes:

$ cd ~/6.824/src/main
$ bash test-mr.sh
*** Starting wc test.

You can change ret := false to true in the Done function in mr/coordinator.go so that the coordinator exits immediately. Then:

$ bash test-mr.sh
*** Starting wc test.
sort: No such file or directory
cmp: EOF on mr-wc-all
--- wc output is not the same as mr-correct-wc.txt
--- wc test: FAIL
$

The test script expects to see output in files named mr-out-X, one for each reduce task. The empty implementations of mr/coordinator.go and mr/worker.go don't produce those files (or do much of anything else), so the test fails.

When you've finished, the test script output should look like this:

$ bash test-mr.sh
*** Starting wc test.
--- wc test: PASS
*** Starting indexer test.
--- indexer test: PASS
*** Starting map parallelism test.
--- map parallelism test: PASS
*** Starting reduce parallelism test.
--- reduce parallelism test: PASS
*** Starting crash test.
--- crash test: PASS
*** PASSED ALL TESTS
$

You'll also see some errors from the Go RPC package that look like

2019/12/16 13:27:09 rpc.Register: method "Done" has 1 input parameters; needs exactly three

A few rules:

• The map phase should divide the intermediate keys into buckets for nReduce reduce tasks, where nReduce is the argument that main/mrcoordinator.go passes to MakeCoordinator().
• The worker implementation should put the output of the X'th reduce task in the file mr-out-X.
• A mr-out-X file should contain one line per Reduce function output. The line should be generated with the Go "%v %v" format, called with the key and value. Have a look in main/mrsequential.go for the line commented "this is the correct format". The test script will fail if your implementation deviates too much from this format.
• You can modify mr/worker.go, mr/coordinator.go, and mr/rpc.go. You can temporarily modify other files for testing, but make sure your code works with the original versions; we'll test with the original versions.
• The worker should put intermediate Map output in files in the current directory, where your worker can later read them as input to Reduce tasks.
• main/mrcoordinator.go expects mr/coordinator.go to implement a Done() method that returns true when the MapReduce job is completely finished; at that point, mrcoordinator.go will exit.
• When the job is completely finished, the worker processes should exit. A simple way to implement this is to use the return value from call(): if the worker fails to contact the coordinator, it can assume that the coordinator has exited because the job is done, and so the worker can terminate too. Depending on your design, you might also find it helpful to have a "please exit" pseudo-task that the coordinator can give to workers.

Hints

• One way to get started is to modify mr/worker.go's Worker() to send an RPC to the coordinator asking for a task. Then modify the coordinator to respond with the file name of an as-yet-unstarted map task. Then modify the worker to read that file and call the application Map function, as in mrsequential.go.
• The application Map and Reduce functions are loaded at run-time using the Go plugin package, from files whose names end in .so.
• If you change anything in the mr/ directory, you will probably have to re-build any MapReduce plugins you use, with something like go build -race -buildmode=plugin ../mrapps/wc.go
• This lab relies on the workers sharing a file system. That's straightforward when all workers run on the same machine, but would require a global filesystem like GFS if the workers ran on different machines.
• A reasonable naming convention for intermediate files is mr-X-Y, where X is the Map task number, and Y is the reduce task number.
• The worker's map task code will need a way to store intermediate key/value pairs in files in a way that can be correctly read back during reduce tasks. One possibility is to use Go's encoding/json package. To write key/value pairs to a JSON file:

    enc := json.NewEncoder(file)
    for _, kv := ... {
      err := enc.Encode(&kv)
  

  and to read such a file back:

    dec := json.NewDecoder(file)
    for {
      var kv KeyValue
      if err := dec.Decode(&kv); err != nil {
        break
      }
      kva = append(kva, kv)
    }
  

• The map part of your worker can use the ihash(key) function (in worker.go) to pick the reduce task for a given key.
• You can steal some code from mrsequential.go for reading Map input files, for sorting intermedate key/value pairs between the Map and Reduce, and for storing Reduce output in files.
• The coordinator, as an RPC server, will be concurrent; don't forget to lock shared data.
• Use Go's race detector, with go build -race and go run -race. test-mr.sh by default runs the tests with the race detector.
• Workers will sometimes need to wait, e.g. reduces can't start until the last map has finished. One possibility is for workers to periodically ask the coordinator for work, sleeping with time.Sleep() between each request. Another possibility is for the relevant RPC handler in the coordinator to have a loop that waits, either with time.Sleep() or sync.Cond. Go runs the handler for each RPC in its own thread, so the fact that one handler is waiting won't prevent the coordinator from processing other RPCs.
• The coordinator can't reliably distinguish between crashed workers, workers that are alive but have stalled for some reason, and workers that are executing but too slowly to be useful. The best you can do is have the coordinator wait for some amount of time, and then give up and re-issue the task to a different worker. For this lab, have the coordinator wait for ten seconds; after that the coordinator should assume the worker has died (of course, it might not have).
• If you choose to implement Backup Tasks (Section 3.6), note that we test that your code doesn't schedule extraneous tasks when workers execute tasks without crashing. Backup tasks should only be scheduled after some relatively long period of time (e.g., 10s).
• To test crash recovery, you can use the mrapps/crash.go application plugin. It randomly exits in the Map and Reduce functions.
• To ensure that nobody observes partially written files in the presence of crashes, the MapReduce paper mentions the trick of using a temporary file and atomically renaming it once it is completely written. You can use ioutil.TempFile to create a temporary file and os.Rename to atomically rename it.
• test-mr.sh runs all the processes in the sub-directory mr-tmp, so if something goes wrong and you want to look at intermediate or output files, look there. You can modify test-mr.sh to exit after the failing test, so the script does not continue testing (and overwrite the output files).
• test-mr-many.sh provides a bare-bones script for running test-mr.sh with a timeout (which is how we'll test your code). It takes as an argument the number of times to run the tests. You should not run several test-mr.sh instances in parallel because the coordinator will reuse the same socket, causing conflicts.

  No-credit challenge exercises

  Implement your own MapReduce application (see examples in mrapps/*), e.g., Distributed Grep (Section 2.3 of the MapReduce paper).

  Get your MapReduce coordinator and workers to run on separate machines, as they would in practice. You will need to set up your RPCs to communicate over TCP/IP instead of than Unix sockets (see commented out line in Coordinator.server()), and read/write files using a shared file system. For example, you can ssh into multiple Athena cluster machines at MIT, which use AFS to share files; or you could rent a couple AWS instances and use S3 for storage.