Mit6.824 Lab1-MapReduce

前言

Mit6.824 是我在学习一些分布式系统方面的知识的时候偶然看到的，然后就开始尝试跟课。不得不说，国外的课程难度是真的大，一周的时间居然要学一门 Go 语言，然后还要读论文，进而做MapReduce 实验。
由于 MR（MapReduce） 框架需要建立在 DFS（Distributed File System）的基础上实现，所以本实验是通过使用多线程来模拟分布式环境。虽然难度上大大降低，但是通过该实验，还是会让我们对 MR 的核心原理有一个较为深刻的认识。
做实验之前我们需要先把经典的 MapReduce 论文给看了，窝比较建议直接看英文原文，但如果时间不充裕的话，可以直接在网上找中文的翻译版。
刚开始做这个实验的时候真的是一头雾水，完全不知道如何下手。后来发现这个工程有一个自动化测试文件（test_test.go）,每部分实验都会使用这个测试文件里的函数对代码进行测试。我们只要顺着这个测试函数逐步倒推，然后补全代码即可。

Part I: Map/Reduce input and output

第一部分是先实现一个顺序版（sequential）的MR，让我们对 MR 的流程有一个大体的认识,并且实现doMap() 和 doReduce() 两个函数。
其包含两个测试函数TestSequentialSingle() 和 TestSequentialMany()。

TestSequentialSingle()

每个map worker处理一个文件，所以map worker的数量就等于文件的数量。
测试单个map worker 和 reduce worker。

func TestSequentialSingle(t *testing.T) {
    mr := Sequential("test", makeInputs(1), 1, MapFunc, ReduceFunc)
    mr.Wait()
    check(t, mr.files)
    checkWorker(t, mr.stats)
    cleanup(mr)
}

TestSequentialMany（）

此测试函数测试多个 map worker 和多个 reduce worker。
其运行逻辑和TestSequentialSingle类似。

func TestSequentialMany(t *testing.T) {
    mr := Sequential("test", makeInputs(5), 3, MapFunc, ReduceFunc)
    mr.Wait()
    check(t, mr.files)
    checkWorker(t, mr.stats)
    cleanup(mr)
}

Sequential()

测试函数将工作名称，测试文件，reduce 的数量，用户定义的 map 函数，reduce 函数五个实参传递给Sequential()

// Sequential runs map and reduce tasks sequentially, waiting for each task to
// complete before running the next.
func Sequential(jobName string, files []string, nreduce int,
    mapF func(string, string) []KeyValue,
    reduceF func(string, []string) string,
) (mr *Master) {
    mr = newMaster("master")
    go mr.run(jobName, files, nreduce, func(phase jobPhase) {
        switch phase {
        case mapPhase:
            for i, f := range mr.files {
                doMap(mr.jobName, i, f, mr.nReduce, mapF)
            }
        case reducePhase:
            for i := 0; i < mr.nReduce; i++ {
                doReduce(mr.jobName, i, mergeName(mr.jobName, i), len(mr.files), reduceF)
            }
        }
    }, func() {
        mr.stats = []int{len(files) + nreduce}
    })
    return
}

Sequential()首先获取一个Master 对象的指针，然后利用函数闭包运行Master.run()。

Master.run()

// run executes a mapreduce job on the given number of mappers and reducers.
//
// First, it divides up the input file among the given number of mappers, and
// schedules each task on workers as they become available. Each map task bins
// its output in a number of bins equal to the given number of reduce tasks.
// Once all the mappers have finished, workers are assigned reduce tasks.
//
// When all tasks have been completed, the reducer outputs are merged,
// statistics are collected, and the master is shut down.
//
// Note that this implementation assumes a shared file system.
func (mr *Master) run(jobName string, files []string, nreduce int,
    schedule func(phase jobPhase),
    finish func(),
) {
    mr.jobName = jobName
    mr.files = files
    mr.nReduce = nreduce

    fmt.Printf("%s: Starting Map/Reduce task %s\n", mr.address, mr.jobName)

    schedule(mapPhase)
    schedule(reducePhase)
    finish()
    mr.merge()

    fmt.Printf("%s: Map/Reduce task completed\n", mr.address)

    mr.doneChannel <- true
}

doMap()

doMap() 和 doReduce()是需要我们去实现的函数。
doMap()的实现主要是将用户定义的MapFunc()切割的文本，通过 hash 分到 'nReduce'个切片中去。

func doMap(
    jobName string, // the name of the MapReduce job
    mapTaskNumber int, // which map task this is
    inFile string,
    nReduce int, // the number of reduce task that will be run ("R" in the paper)
    mapF func(file string, contents string) []KeyValue,
) {
    // read contents from 'infile'
    dat,err := ioutil.ReadFile(inFile)
    if err != nil {
        log.Fatal("doMap: readFile ", err)
    }

    //transfer data into ‘kvSlice’ according to the mapF()
    kvSlice := mapF(inFile, string(dat))

    //divide the ‘kvSlice’ into 'reduceKv' according to the ihash()
    var reduceKv [][]KeyValue // temporary variable which will be written into reduce files
    for i:=0;i<nReduce;i++ {
        s1 := make([]KeyValue,0)
        reduceKv = append(reduceKv, s1)
    }
    for _,kv := range kvSlice{
        hash := ihash(kv.Key) % nReduce
        reduceKv[hash] = append(reduceKv[hash],kv)
    }

    //write 'reduceKv' into ‘nReduce’ JSON files
    for i := 0;i<nReduce;i++ {
        file,err := os.Create(reduceName(jobName,mapTaskNumber,i))
        if err != nil {
            log.Fatal("doMap: create ", err)
        }

        enc := json.NewEncoder(file)
        for _, kv := range reduceKv[i]{
            err := enc.Encode(&kv)
            if err != nil {
                log.Fatal("doMap: json encodem ", err)
            }
        }

        file.Close()

    }
}

doReduce()

doReduce()主要是将 key 值相同的 value 打包发送给用户定义的 ReduceFunc()，获得一个新的 kv对，key 值不变，而value值则是ReduceFunc()的返回值，排序，最后将新的 kv对 切片写入文件。

type ByKey []KeyValue
func (a ByKey) Len() int { return len(a) }
func (a ByKey) Swap(i, j int) { a[i],a[j] = a[j],a[i] }
func (a ByKey) Less(i, j int) bool { return a[i].Key < a[j].Key }

func doReduce(
    jobName string, // the name of the whole MapReduce job
    reduceTaskNumber int, // which reduce task this is
    outFile string, // write the output here
    nMap int, // the number of map tasks that were run ("M" in the paper)
    reduceF func(key string, values []string) string,
) {
    //read kv slice from the json file
    var kvSlice []KeyValue
    for i := 0;i<nMap;i++{
        //file, _ := os.OpenFile(reduceName(jobName,i,reduceTaskNumber), os.O_RDONLY, 0666)
        file,err := os.Open(reduceName(jobName,i,reduceTaskNumber))
        if err != nil {
            log.Fatal("doReduce: open ", err)
        }
        var kv KeyValue
        dec := json.NewDecoder(file)
        for{
            err := dec.Decode(&kv)
            kvSlice = append(kvSlice,kv)
            if err == io.EOF {
                break
            }
        }
        file.Close()
        /********/
        //此处如果用 defer，可能会造成文件开启过多，造成程序崩溃
        /********/
    }

    //sort the intermediate kv slices by key
    sort.Sort(ByKey(kvSlice))

    //process kv slices in the reduceF()
    var reduceFValue []string
    var outputKv []KeyValue
    var preKey string = kvSlice[0].Key
    for i,kv := range kvSlice{
        if i == (len(kvSlice) - 1) {
            reduceFValue = append(reduceFValue, kv.Value)
            outputKv = append(outputKv, KeyValue{preKey, reduceF(preKey, reduceFValue)})
        } else {
                if kv.Key != preKey {
                    outputKv = append(outputKv, KeyValue{preKey, reduceF(preKey, reduceFValue)})
                    reduceFValue = make([]string, 0)
                }
                reduceFValue = append(reduceFValue, kv.Value)
        }

        preKey = kv.Key
    }

    //write the reduce output as JSON encoded kv objects to the file named outFile
    file,err := os.Create(outFile)
    if err != nil {
        log.Fatal("doRuduce: create ", err)
    }
    defer file.Close()

    enc := json.NewEncoder(file)
    for _, kv := range outputKv{
        err := enc.Encode(&kv)
        if err != nil {
            log.Fatal("doRuduce: json encode ", err)
        }
    }
}

Part II: Single-worker word count

第二部分是实现mapF() 和 reduceF()函数，来实现通过顺序 MR统计词频的功能。
比较简单，就直接放代码了。

func mapF(filename string, contents string) []mapreduce.KeyValue {
    f := func(c rune) bool {
        return !unicode.IsLetter(c)
    }
    var strSlice []string = strings.FieldsFunc(contents,f)
    var kvSlice []mapreduce.KeyValue
    for _,str := range strSlice {
        kvSlice = append(kvSlice, mapreduce.KeyValue{str, "1"})
    }

    return kvSlice
}

func reduceF(key string, values []string) string {
    var cnt int64
    for _,str := range values{
        temp,err := strconv.ParseInt(str,10,64)
        if(err != nil){
            fmt.Println("wc :parseint ",err)
        }
        cnt += temp
    }
    return strconv.FormatInt(cnt,10)
}

Part III: Distributing MapReduce tasks && Part IV: Handling worker failures

第三部分和第四部分可以一起来做，主要是完成schedule()，实现一个通过线程并发执行 map worker 和 reduce worker 的 MR 框架。框架通过 RPC 来模拟分布式计算，并要带有 worker 的容灾功能。

TestBasic()

测试函数启动两个线程运行RUnWoker()。

func TestBasic(t *testing.T) {
    mr := setup()
    for i := 0; i < 2; i++ {
        go RunWorker(mr.address, port("worker"+strconv.Itoa(i)),
            MapFunc, ReduceFunc, -1)
    }
    mr.Wait()
    check(t, mr.files)
    checkWorker(t, mr.stats)
    cleanup(mr)
}

setup() && Distributed()

func setup() *Master {
    files := makeInputs(nMap)
    master := port("master")
    mr := Distributed("test", files, nReduce, master)
    return mr
}

通过mr.startRPCServer() 启动 master 的 RPC 服务器，然后通过 mr.run()进行 worker 的调度。

// Distributed schedules map and reduce tasks on workers that register with the
// master over RPC.
func Distributed(jobName string, files []string, nreduce int, master string) (mr *Master) {
    mr = newMaster(master)
    mr.startRPCServer()
    go mr.run(jobName, files, nreduce,
        func(phase jobPhase) {
            ch := make(chan string)
            go mr.forwardRegistrations(ch)
            schedule(mr.jobName, mr.files, mr.nReduce, phase, ch)
        },
        func() {
            mr.stats = mr.killWorkers()
            mr.stopRPCServer()
        })
    return
}

Master.forwardRegistrations()

该函数通过worker 的数量来判断是否有新 worker 启动，一旦发现有新的 worker 启动，则使用管道（ch）通知schedule()。
理解该函数对实现后面的schedule()至关重要。

// helper function that sends information about all existing
// and newly registered workers to channel ch. schedule()
// reads ch to learn about workers.
func (mr *Master) forwardRegistrations(ch chan string) {
    i := 0
    for {
        mr.Lock()
        if len(mr.workers) > i {
            // there's a worker that we haven't told schedule() about.
            w := mr.workers[i]
            go func() { ch <- w }() // send without holding the lock.
            i = i + 1
        } else {
            // wait for Register() to add an entry to workers[]
            // in response to an RPC from a new worker.
            mr.newCond.Wait()
        }
        mr.Unlock()
    }
}

schedule()

shedule()虽然不长，但实现起来还是有点难度的。
waitGroup用来判断任务是否完成。
registerChan来监听是否有新的 worker 启动，如果有的话，就启动一个线程来运行该 worker。通过新开线程来运行新 worker的逻辑比较符合分布式 MR 的特点。
对于 宕掉的worker执行call()操作时，会返回false。
每开始执行一个任务，就让waitGroup减一，而执行失败（call()返回 false）则将waitGroup加一，代表会将该任务安排给其他 worker。

waitGroup.Wait()则会等到任务完全执行完返回。

func schedule(jobName string, mapFiles []string, nReduce int, phase jobPhase, registerChan chan string) {
    var ntasks int
    var n_other int // number of inputs (for reduce) or outputs (for map)
    switch phase {
    case mapPhase:
        ntasks = len(mapFiles)
        n_other = nReduce
    case reducePhase:
        ntasks = nReduce
        n_other = len(mapFiles)
    }

    fmt.Printf("Schedule: %v %v tasks (%d I/Os)\n", ntasks, phase, n_other)

    // All ntasks tasks have to be scheduled on workers, and only once all of
    // them have been completed successfully should the function return.
    // Remember that workers may fail, and that any given worker may finish
    // multiple tasks.

    waitGroup := sync.WaitGroup{}
    waitGroup.Add(ntasks)

    taskChan := make(chan int, ntasks)
    for i:=0;i<ntasks;i++  {
        taskChan <- i
    }

    go func() {
        for {
            ch := <- registerChan
            go func(c string) {
                for {
                    i := <- taskChan
                    if call(c,"Worker.DoTask", &DoTaskArgs{jobName,
                        mapFiles[i],phase,i,n_other},new(struct{})){
                        waitGroup.Done()
                    } else{
                        taskChan <- i
                    }
                }
            }(ch)
        }
    }()

    waitGroup.Wait()

    fmt.Printf("Schedule: %v phase done\n", phase)
}

RunWorker()

通过RunWorker() 来增加 worker。
nRPC来控制 worker 的寿命，每接收一次 rpc 请求就 -1s。如果初始值为 -1，则代表改 worker 是永生的。

// RunWorker sets up a connection with the master, registers its address, and
// waits for tasks to be scheduled.
func RunWorker(MasterAddress string, me string,
    MapFunc func(string, string) []KeyValue,
    ReduceFunc func(string, []string) string,
    nRPC int,
) {
    debug("RunWorker %s\n", me)
    wk := new(Worker)
    wk.name = me
    wk.Map = MapFunc
    wk.Reduce = ReduceFunc
    wk.nRPC = nRPC
    rpcs := rpc.NewServer()
    rpcs.Register(wk)
    os.Remove(me) // only needed for "unix"
    l, e := net.Listen("unix", me)
    if e != nil {
        log.Fatal("RunWorker: worker ", me, " error: ", e)
    }
    wk.l = l
    wk.register(MasterAddress)

    // DON'T MODIFY CODE BELOW
    for {
        wk.Lock()
        if wk.nRPC == 0 {
            wk.Unlock()
            break
        }
        wk.Unlock()
        conn, err := wk.l.Accept()
        if err == nil {
            wk.Lock()
            wk.nRPC--
            wk.Unlock()
            go rpcs.ServeConn(conn)
        } else {
            break
        }
    }
    wk.l.Close()
    debug("RunWorker %s exit\n", me)
}

Part V: Inverted index generation

第五部分是实现倒排索引。此处要求的倒排索引，就是在输出结果时，需要将出现过 key 值文件的文件名在 key 值后面输出。
功能是通过完成 mapF() 和 reduceF() 来实现的。

mapF()

将key 值所在文件的文件名赋给 kv对 的value。

func mapF(document string, value string) (res []mapreduce.KeyValue) {
    f := func(c rune) bool {
        return !unicode.IsLetter(c)
    }
    var strSlice []string = strings.FieldsFunc(value,f)
    var kvSlice []mapreduce.KeyValue
    for _,str := range strSlice {
        kvSlice = append(kvSlice, mapreduce.KeyValue{str, document})
    }

    return kvSlice
}

reduceF()

将相同 key 值的所有 value 打包并统计数量返回。

func reduceF(key string, values []string) string {
    var cnt int64
    var documents string
    set := make(map[string]bool)
    for _,str := range values{
        set[str] = true
    }
    var keys []string
    for key := range set{
        if set[key] == false{
            continue
        }
        keys = append(keys,key)
    }
    sort.Strings(keys)
    for _,key := range keys{
        cnt++
        if cnt >= 2{
            documents += ","
        }
        documents += key
    }
    //return strconv.FormatInt(cnt,10)
    return strconv.FormatInt(cnt,10) + " " + documents
}
 

后记

从刚开始的无从下手，到现在通过Lab1全部测试，MR 实验算是完全做完了，还是很有成就感的。
除了对 MR 有一个更深的理解之外，也深深感受到了优秀系统的魅力——功能强大，结构简洁。
同时又了解了一门新语言——GoLang，一门专门为高并发系统而设计的语言，用起来还是很舒服的。
但这毕竟是分布式系统的第一个实验，欠缺的知识还很多，继续努力。