当前位置 博文首页 > golang如何实现mapreduce单进程版本详解
前言
MapReduce作为hadoop的编程框架,是工程师最常接触的部分,也是除去了网络环境和集群配 置之外对整个Job执行效率影响很大的部分,所以很有必要深入了解整个过程。元旦放假的第一天，在家没事干，用golang实现了一下mapreduce的单进程版本，github地址。处理对大文件统计最高频的10个单词，因为功能比较简单，所以设计没有解耦合。
本文先对mapreduce大体概念进行介绍，然后结合代码介绍一下，如果接下来几天有空，我会实现一下分布式高可用的mapreduce版本。下面话不多说了，来一起看看详细的介绍吧。
1. Mapreduce大体架构
上图是论文中mapreduce的大体架构。总的来说Mapreduce的思想就是分治思想：对数据进行分片，然后用mapper进行处理，以key-value形式输出中间文件；然后用reducer进行对mapper输出的中间文件进行合并：将key一致的合到一块，并输出结果文件；如果有需要，采用Combiner进行最后的合并。
归纳来说主要分为5部分：用户程序、Master、Mapper、Reducer、Combiner（上图未给出）。
总的来说，架构不复杂。组件间通信用啥都可以，比如RPC、HTTP或者私有协议等。
2. 实现代码介绍
该版本代码实现了单机单进程版本，Mapper、Reducer和Combiner的实现用协程goroutine实现，通信采用channel。代码写的比较随意，没有解耦合。
为了方便起见，Combiner对最高频的10个单词进行堆排序处理，按规范来说应该放在用户程序处理。
文件目录如下，其中bin文件夹下的big_input_file.txt为输入文件，可以调用generate下的main文件生成，caller文件为入口的用户程序，master目录下分别存放master、mapper、reducer、combiner代码：
. ├── README.md ├── bin │ └── file-store │ └── big_input_file.txt └── src ├── caller │ └── main.go ├── generate │ └── main.go └── master ├── combiner.go ├── mapper.go ├── master.go └── reducer.go 6 directories, 8 files
2.1 caller
用户程序，读入文件并按固定行数进行划分；然后调用master.Handle进行处理。
package main
import (
"os"
"path"
"path/filepath"
"bufio"
"strconv"
"master"
"github.com/vinllen/go-logger/logger"
)
const (
LIMIT int = 10000 // the limit line of every file
)
func main() {
curDir, err := filepath.Abs(filepath.Dir(os.Args[0]))
if err != nil {
logger.Error("Read path error: ", err.Error())
return
}
fileDir := path.Join(curDir, "file-store")
_ = os.Mkdir(fileDir, os.ModePerm)
// 1. read file
filename := "big_input_file.txt"
inputFile, err := os.Open(path.Join(fileDir, filename))
if err != nil {
logger.Error("Read inputFile error: ", err.Error())
return
}
defer inputFile.Close()
// 2. split inputFile into several pieces that every piece hold 100,000 lines
filePieceArr := []string{}
scanner := bufio.NewScanner(inputFile)
piece := 1
Outter:
for {
outputFilename := "input_piece_" + strconv.Itoa(piece)
outputFilePos := path.Join(fileDir, outputFilename)
filePieceArr = append(filePieceArr, outputFilePos)
outputFile, err := os.Create(outputFilePos)
if err != nil {
logger.Error("Split inputFile error: ", err.Error())
continue
}
defer outputFile.Close()
for cnt := 0; cnt < LIMIT; cnt++ {
if !scanner.Scan() {
break Outter
}
_, err := outputFile.WriteString(scanner.Text() + "\n")
if err != nil {
logger.Error("Split inputFile writting error: ", err.Error())
return
}
}
piece++
}
// 3. pass to master
res := master.Handle(filePieceArr, fileDir)
logger.Warn(res)
}
2.2 master
Master程序，依次生成Combiner、Reducer、Mapper，处理消息中转，输出最后结果。
package master
import (
"github.com/vinllen/go-logger/logger"
)
var (
MapChanIn chan MapInput // channel produced by master while consumed by mapper
MapChanOut chan string // channel produced by mapper while consumed by master
ReduceChanIn chan string // channel produced by master while consumed by reducer
ReduceChanOut chan string // channel produced by reducer while consumed by master
CombineChanIn chan string // channel produced by master while consumed by combiner
CombineChanOut chan []Item // channel produced by combiner while consumed by master
)
func Handle(inputArr []string, fileDir string) []Item {
logger.Info("handle called")
const(
mapperNumber int = 5
reducerNumber int = 2
)
MapChanIn = make(chan MapInput)
MapChanOut = make(chan string)
ReduceChanIn = make(chan string)
ReduceChanOut = make(chan string)
CombineChanIn = make(chan string)
CombineChanOut = make(chan []Item)
reduceJobNum := len(inputArr)
combineJobNum := reducerNumber
// start combiner
go combiner()
// start reducer
for i := 1; i <= reducerNumber; i++ {
go reducer(i, fileDir)
}
// start mapper
for i := 1; i <= mapperNumber; i++ {
go mapper(i, fileDir)
}
go func() {
for i, v := range(inputArr) {
MapChanIn <- MapInput{
Filename: v,
Nr: i + 1,
} // pass job to mapper
}
close(MapChanIn) // close map input channel when no more job
}()
var res []Item
outter:
for {
select {
case v := <- MapChanOut:
go func() {
ReduceChanIn <- v
reduceJobNum--
if reduceJobNum <= 0 {
close(ReduceChanIn)
}
}()
case v := <- ReduceChanOut:
go func() {
CombineChanIn <- v
combineJobNum--
if combineJobNum <= 0 {
close(CombineChanIn)
}
}()
case v := <- CombineChanOut:
res = v
break outter
}
}
close(MapChanOut)
close(ReduceChanOut)
close(CombineChanOut)
return res
}
2.3 mapper
Mapper程序，读入并按key-value格式生成中间文件，告知Master。
package master
import (
"fmt"
"path"
"os"
"bufio"
"strconv"
"github.com/vinllen/go-logger/logger"
)
type MapInput struct {
Filename string
Nr int
}
func mapper(nr int, fileDir string) {
for {
val, ok := <- MapChanIn // val: filename
if !ok { // channel close
break
}
inputFilename := val.Filename
nr := val.Nr
file, err := os.Open(inputFilename)
if err != nil {
errMsg := fmt.Sprintf("Read file(%s) error in mapper(%d)", inputFilename, nr)
logger.Error(errMsg)
MapChanOut <- ""
continue
}
mp := make(map[string]int)
scanner := bufio.NewScanner(file)
scanner.Split(bufio.ScanWords)
for scanner.Scan() {
str := scanner.Text()
//logger.Info(str)
mp[str]++
}
outputFilename := path.Join(fileDir, "mapper-output-" + strconv.Itoa(nr))
outputFileHandler, err := os.Create(outputFilename)
if err != nil {
errMsg := fmt.Sprintf("Write file(%s) error in mapper(%d)", outputFilename, nr)
logger.Error(errMsg)
} else {
for k, v := range mp {
str := fmt.Sprintf("%s %d\n", k, v)
outputFileHandler.WriteString(str)
}
outputFileHandler.Close()
}
MapChanOut <- outputFilename
}
}
2.4 reducer
Reducer程序，读入Master传递过来的中间文件并归并。
package master
import (
"fmt"
"bufio"
"os"
"strconv"
"path"
"strings"
"github.com/vinllen/go-logger/logger"
)
func reducer(nr int, fileDir string) {
mp := make(map[string]int) // store the frequence of words
// read file and do reduce
for {
val, ok := <- ReduceChanIn
if !ok {
break
}
logger.Debug("reducer called: ", nr)
file, err := os.Open(val)
if err != nil {
errMsg := fmt.Sprintf("Read file(%s) error in reducer", val)
logger.Error(errMsg)
continue
}
scanner := bufio.NewScanner(file)
for scanner.Scan() {
str := scanner.Text()
arr := strings.Split(str, " ")
if len(arr) != 2 {
errMsg := fmt.Sprintf("Read file(%s) error that len of line(%s) != 2(%d) in reducer", val, str, len(arr))
logger.Warn(errMsg)
continue
}
v, err := strconv.Atoi(arr[1])
if err != nil {
errMsg := fmt.Sprintf("Read file(%s) error that line(%s) parse error in reduer", val, str)
logger.Warn(errMsg)
continue
}
mp[arr[0]] += v
}
if err := scanner.Err(); err != nil {
logger.Error("reducer: reading standard input:", err)
}
file.Close()
}
outputFilename := path.Join(fileDir, "reduce-output-" + strconv.Itoa(nr))
outputFileHandler, err := os.Create(outputFilename)
if err != nil {
errMsg := fmt.Sprintf("Write file(%s) error in reducer(%d)", outputFilename, nr)
logger.Error(errMsg)
} else {
for k, v := range mp {
str := fmt.Sprintf("%s %d\n", k, v)
outputFileHandler.WriteString(str)
}
outputFileHandler.Close()
}
ReduceChanOut <- outputFilename
}
2.5 combiner
Combiner程序，读入Master传递过来的Reducer结果文件并归并成一个，然后堆排序输出最高频的10个词语。
package master
import (
"fmt"
"strings"
"bufio"
"os"
"container/heap"
"strconv"
"github.com/vinllen/go-logger/logger"
)
type Item struct {
key string
val int
}
type PriorityQueue []*Item
func (pq PriorityQueue) Len() int {
return len(pq)
}
func (pq PriorityQueue) Less(i, j int) bool {
return pq[i].val > pq[j].val
}
func (pq PriorityQueue) Swap(i, j int) {
pq[i], pq[j] = pq[j], pq[i]
}
func (pq *PriorityQueue) Push(x interface{}) {
item := x.(*Item)
*pq = append(*pq, item)
}
func (pq *PriorityQueue) Pop() interface{} {
old := *pq
n := len(old)
item := old[n - 1]
*pq = old[0 : n - 1]
return item
}
func combiner() {
mp := make(map[string]int) // store the frequence of words
// read file and do combine
for {
val, ok := <- CombineChanIn
if !ok {
break
}
logger.Debug("combiner called")
file, err := os.Open(val)
if err != nil {
errMsg := fmt.Sprintf("Read file(%s) error in combiner", val)
logger.Error(errMsg)
continue
}
scanner := bufio.NewScanner(file)
for scanner.Scan() {
str := scanner.Text()
arr := strings.Split(str, " ")
if len(arr) != 2 {
errMsg := fmt.Sprintf("Read file(%s) error that len of line != 2(%s) in combiner", val, str)
logger.Warn(errMsg)
continue
}
v, err := strconv.Atoi(arr[1])
if err != nil {
errMsg := fmt.Sprintf("Read file(%s) error that line(%s) parse error in combiner", val, str)
logger.Warn(errMsg)
continue
}
mp[arr[0]] += v
}
file.Close()
}
// heap sort
// pq := make(PriorityQueue, len(mp))
pq := make(PriorityQueue, 0)
heap.Init(&pq)
for k, v := range mp {
node := &Item {
key: k,
val: v,
}
// logger.Debug(k, v)
heap.Push(&pq, node)
}
res := []Item{}
for i := 0; i < 10 && pq.Len() > 0; i++ {
node := heap.Pop(&pq).(*Item)
res = append(res, *node)
}
CombineChanOut <- res
}
3. 总结
不足以及未实现之处：
接下来要是有空，我会实现分布式高可用的代码，模块间采用RPC通讯。
好了，以上就是这篇文章的全部内容了，希望本文的内容对大家的学习或者工作具有一定的参考学习价值，如果有疑问大家可以留言交流，谢谢大家对站长博客的支持。
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