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。
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