MIT 6.824 Day2

w1

MapReduce

Lab 1

Part 1: Map/Reduce input and output

1. 完成sequential implementation
    1.1 function that divides up the output of a map task（doMap() function in common_map.go）
    1.2 function that gathers all the inputs for a reduce task（doReduce() function in common_reduce.go）

2. 测试方法
    2.1 set debugEnabled = true in common.go
    2.2 cd 6.824/src/mapreduce; go test -v -run Sequential

Part 2: Single-worker word count

Input Files: pg-*.txt
Map/Reduce Files: mrtmp.wcseq-<MapTaskNumber>-<ReduceTaskNumber>
Merge Files: mrtmp.wcseq-res-<ReduceTaskNumber>


1. 完成 main/wc.go 中的 mapF() 和 reduceF() 函数
    1.1 mapF, Input包含文件名(filename)，以及该文件的内容(contents)；Output是key为单词，value为1的结构的切片
        func mapF(filename string, contents string) (kvResult []mapreduce.KeyValue)
        
    1.2 ReduceF，Input是key(单词)，以及该单词的出现情况的切片，比如["1", "1",...]，Output是该单词出现的总数
        func reduceF(key string, values []string) string 


2. 测试方法
    cd 6.824/src/main
    go run wc.go master sequential pg-*.txt

Part 3: Distributing MapReduce tasks

1. complete a version of MapReduce that splits the work over a set of worker threads that run in parallel on multiple cores.
    1.1 implement schedule() in mapreduce/schedule.go

2. 测试方法
    go test -run TestParallel
    go test -race -run TestParallel