MIT 6.824 lab1:mapreduce
這是 MIT 6.824 課程 lab1 的學習總結,記錄我在學習過程中的收獲和踩的坑。
我的實驗環境是 windows 10,所以對lab的code 做了一些環境上的修改,如果你僅僅對code 感興趣,請移步 : github/zouzhitao
mapreduce overview
先大致看一下 mapreduce 到底是什麽
我個人的簡單理解是這樣的: mapreduce 就是一種分布式處理用戶特定任務的系統。它大概是這樣處理的。
用戶提供兩個函數
mapFunc(k1,v1)-> list(k2,v2) reduceFunc(k2,list(v2)) -> ans of k2
這個 分布式系統 將用戶的任務做分布式處理,最終為每一個 k2
生成答案。下面我們就來描述一下,這個分布式系統是如何處理的。
首先,他有一個 master
來做任務調度。
master
- 先調度 worker 做 map 任務,設總的 map 任務的數目為 $M$ , 將result 存儲在 中間文件 m-i-j 中, $i \in {0,\dots ,M-1}, j \in {0,\dots,R-1}$
- 調度 worker 做 reduce 任務,設總的 reduce 任務數目為 $R$, 將答案儲存在 $r_j$
- 然後將所有的renduce 任務的ans merge起來作為答案放在一個文件中交給用戶。
detail 都在實驗中
detail
這部分講 實驗內容(觀看code), 不過不按照 lab 順序將。個人認為 做lab的目的,不是做lab 而是為了搞懂 mapreduce system
master
我們先來看看 master 這部分的代碼
// Master holds all the state that the master needs to keep track of. type Master struct { sync.Mutex address string doneChannel chan bool // protected by the mutex newCond *sync.Cond // signals when Register() adds to workers[] workers []string // each worker's UNIX-domain socket name -- its RPC address // Per-task information jobName string // Name of currently executing job files []string // Input files nReduce int // Number of reduce partitions shutdown chan struct{} l net.Listener stats []int }
master 維護了執行一個 job 需要的所有狀態
master.run
這部分是 master 具體做的事情
// 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) // worker 的地址
go mr.forwardRegistrations(ch)
schedule(mr.jobName, mr.files, mr.nReduce, phase, ch)
},
func() {
mr.stats = mr.killWorkers()
mr.stopRPCServer()
})
return
}
// 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
}
schedule
我們需要實現的其實是這個 schedule 也是最核心的, schedule
實現任務調度,註意這裏有 $M$ 個 map 任務,$R$ 個 reduce 任務,只有 $n$ 個 worker, 通常情況下,$M>n,R>n$ 這樣才能盡可能利用 worker 的性能,讓流水線充沛。
//
// schedule() starts and waits for all tasks in the given phase (mapPhase
// or reducePhase). the mapFiles argument holds the names of the files that
// are the inputs to the map phase, one per map task. nReduce is the
// number of reduce tasks. the registerChan argument yields a stream
// of registered workers; each item is the worker's RPC address,
// suitable for passing to call(). registerChan will yield all
// existing registered workers (if any) and new ones as they register.
//
func schedule(jobName string, mapFiles []string, nReduce int, phase jobPhase, registerChan chan string) {
var ntasks int
var nOther int // number of inputs (for reduce) or outputs (for map)
switch phase {
case mapPhase:
ntasks = len(mapFiles)
nOther = nReduce
case reducePhase:
ntasks = nReduce
nOther = len(mapFiles)
}
fmt.Printf("Schedule: %v %v tasks (%d I/Os)\n", ntasks, phase, nOther)
// All ntasks tasks have to be scheduled on workers. Once all tasks
// have completed successfully, schedule() should return.
//
// Your code here (Part III, Part IV).
//
//Part III
var wg sync.WaitGroup
wg.Add(ntasks)
for i := 0; i < ntasks; i++ {
go func(i int) {
defer wg.Done()
filename := ""
if i <= len(mapFiles) {
filename = mapFiles[i]
}
taskArgs := DoTaskArgs{
JobName: jobName,
File: filename,
Phase: phase,
TaskNumber: i,
NumOtherPhase: nOther,
}
taskFinished := false
for taskFinished == false {
workAddr := <-registerChan
taskFinished = call(workAddr, "Worker.DoTask", taskArgs, nil)
go func() { registerChan <- workAddr }()
}
}(i)
}
wg.Wait()
fmt.Printf("Schedule: %v done\n", phase)
}
schedule 要做的事情就是對於每一個任務,調用 call
函數去執行 一個rpc調用,讓 worker 執行 Worker.DoTask
這是 PART III/IV 的代碼。
這裏註意幾點細節
registerChan
用的是管道,傳輸可用worker
的地址,所以 執行完一個 task之後要將 worker 的地址重新放到registerChan
master
是串行調度的,也就是說他要等待所有map
任務做完,才會調度reduce
任務,所以在schedule 裏不能提前返回,要等待 說有task完成
接下來我們來看看這個 call
到底幹了什麽,其實它調用了 worker.DOTASK
, 所以我們簡單看看 worker.Dotask
幹了什麽就好
worker
// DoTask is called by the master when a new task is being scheduled on this
// worker.
func (wk *Worker) DoTask(arg *DoTaskArgs, _ *struct{}) error {
//...
switch arg.Phase {
case mapPhase:
doMap(arg.JobName, arg.TaskNumber, arg.File, arg.NumOtherPhase, wk.Map)
case reducePhase:
doReduce(arg.JobName, arg.TaskNumber, mergeName(arg.JobName, arg.TaskNumber), arg.NumOtherPhase, wk.Reduce)
}
//....
}
它核心就是調用了 doMap
和 doReduce
這也是 PART 1 的類容,我們來看看 doMap
和 doReduce
做了什麽
doMap
func doMap(
jobName string, // the name of the MapReduce job
mapTask 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(filename string, contents string) []KeyValue,
) {
//
// doMap manages one map task: it should read one of the input files
// (inFile), call the user-defined map function (mapF) for that file's
// contents, and partition mapF's output into nReduce intermediate files.
//
// There is one intermediate file per reduce task. The file name
// includes both the map task number and the reduce task number. Use
// the filename generated by reduceName(jobName, mapTask, r)
// as the intermediate file for reduce task r. Call ihash() (see
// below) on each key, mod nReduce, to pick r for a key/value pair.
//
// mapF() is the map function provided by the application. The first
// argument should be the input file name, though the map function
// typically ignores it. The second argument should be the entire
// input file contents. mapF() returns a slice containing the
// key/value pairs for reduce; see common.go for the definition of
// KeyValue.
//
// Look at Go's ioutil and os packages for functions to read
// and write files.
//
// Coming up with a scheme for how to format the key/value pairs on
// disk can be tricky, especially when taking into account that both
// keys and values could contain newlines, quotes, and any other
// character you can think of.
//
// One format often used for serializing data to a byte stream that the
// other end can correctly reconstruct is JSON. You are not required to
// use JSON, but as the output of the reduce tasks *must* be JSON,
// familiarizing yourself with it here may prove useful. You can write
// out a data structure as a JSON string to a file using the commented
// code below. The corresponding decoding functions can be found in
// common_reduce.go.
//
// enc := json.NewEncoder(file)
// for _, kv := ... {
// err := enc.Encode(&kv)
//
// Remember to close the file after you have written all the values!
//
// Your code here (Part I).
//
content := safeReadFile(inFile)
ans := mapF(inFile, string(content))
jsonEncoder := make([]*json.Encoder, nReduce)
for i := 0; i < nReduce; i++ {
f := safeCreaFile(reduceName(jobName, mapTask, i))
jsonEncoder[i] = json.NewEncoder(f)
defer f.Close()
}
for _, kv := range ans {
r := ihash(kv.Key) % nReduce
err := jsonEncoder[r].Encode(&kv)
if err != nil {
log.Fatal("jsonEncode err", err)
}
}
}
- 讀取文件內容
- 調用用戶的
mapF
生成一系列的key/val
將所有的key/val
list 以key
hash 到每個 reduce 文件中
也就是說,每個 map 任務產生 $nReduce$ 個中間文件,因此總共有MxR
個中間文件產生,同時 由於 是以key
hash 到reduce 任務的,可以保證同樣的key
一定到同一個 reduce
reduce
func doReduce(
jobName string, // the name of the whole MapReduce job
reduceTask 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,
) {
//
// doReduce manages one reduce task: it should read the intermediate
// files for the task, sort the intermediate key/value pairs by key,
// call the user-defined reduce function (reduceF) for each key, and
// write reduceF's output to disk.
//
// You'll need to read one intermediate file from each map task;
// reduceName(jobName, m, reduceTask) yields the file
// name from map task m.
//
// Your doMap() encoded the key/value pairs in the intermediate
// files, so you will need to decode them. If you used JSON, you can
// read and decode by creating a decoder and repeatedly calling
// .Decode(&kv) on it until it returns an error.
//
// You may find the first example in the golang sort package
// documentation useful.
//
// reduceF() is the application's reduce function. You should
// call it once per distinct key, with a slice of all the values
// for that key. reduceF() returns the reduced value for that key.
//
// You should write the reduce output as JSON encoded KeyValue
// objects to the file named outFile. We require you to use JSON
// because that is what the merger than combines the output
// from all the reduce tasks expects. There is nothing special about
// JSON -- it is just the marshalling format we chose to use. Your
// output code will look something like this:
//
// enc := json.NewEncoder(file)
// for key := ... {
// enc.Encode(KeyValue{key, reduceF(...)})
// }
// file.Close()
//
// Your code here (Part I).
//
kvs := make(map[string][]string)
for i := 0; i < nMap; i++ {
kv := jsonDecode(reduceName(jobName, i, reduceTask))
for _, v := range kv {
kvs[v.Key] = append(kvs[v.Key], v.Value)
}
}
f := safeCreaFile(outFile)
defer f.Close()
enc := json.NewEncoder(f)
for k, v := range kvs {
reduceAns := reduceF(k, v)
enc.Encode(KeyValue{k, reduceAns})
}
}
reduce 幹的事情也很簡單,它先讀取所有傳給它的任務。做成一個 list of key/val
然後調用用戶的 reduceF。將答案傳給用json 編碼到一個文件
PART I 完。
接下來是兩個實例
example
這裏的兩個例子是 word count 和倒排索引 invert index
word count
這個任務,是統計每個單詞出現的次數
//
// The map function is called once for each file of input. The first
// argument is the name of the input file, and the second is the
// file's complete contents. You should ignore the input file name,
// and look only at the contents argument. The return value is a slice
// of key/value pairs.
//
func mapF(filename string, contents string) []mapreduce.KeyValue {
// Your code here (Part II).
var ret []mapreduce.KeyValue
words := strings.FieldsFunc(contents, func(x rune) bool {
return unicode.IsLetter(x) == false
})
for _, w := range words {
kv := mapreduce.KeyValue{w, ""}
ret = append(ret, kv)
}
return ret
}
//
// The reduce function is called once for each key generated by the
// map tasks, with a list of all the values created for that key by
// any map task.
//
func reduceF(key string, values []string) string {
// Your code here (Part II).
return strconv.Itoa(len(values))
}
part II 完
這裏有一點要註意, test
用的是 diff,這個比對會將 \n
,\n\r
認成不一樣的,註意將ans 中的東西改成 \n
就好。
invert index
// The mapping function is called once for each piece of the input.
// In this framework, the key is the name of the file that is being processed,
// and the value is the file's contents. The return value should be a slice of
// key/value pairs, each represented by a mapreduce.KeyValue.
func mapF(document string, value string) (res []mapreduce.KeyValue) {
// Your code here (Part V).
words := strings.FieldsFunc(value, func(x rune) bool {
return unicode.IsLetter(x) == false
})
kvmap := make(map[string]string)
for _, w := range words {
kvmap[w] = document
}
for k, v := range kvmap {
res = append(res, mapreduce.KeyValue{k, v})
}
return
}
// The reduce function is called once for each key generated by Map, with a
// list of that key's string value (merged across all inputs). The return value
// should be a single output value for that key.
func reduceF(key string, values []string) string {
// Your code here (Part V).
numberOfDoc := len(values)
sort.Strings(values)
res := strconv.Itoa(numberOfDoc) + " " + strings.Join(values, ",")
return res
}
這個地方要註意將同一個文檔中的重復單詞去除掉,用一個 map
儲存一下就好
最後說一下環境的坑點
windows 環境註意事項
- lab 中註冊用的
unix
文件地址不能用,我將其改成了tcp
- 註意改成
tcp
後,worker在shutdown
的時候close
掉tcp鏈接
reference
- google mapreduce paper
- lab1
- github/zouzhitao code repo
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MIT 6.824 lab1:mapreduce