hadoop pipes编程示例

hadoop
pipes是hadoop的c++正式接口，通过socket与Map/Reduce框架通信，具体原理这里不在详述，下面通过一个单词统计的示例来说明用法。

1.代码

#include "hadoop/Pipes.hh"

#include "hadoop/TemplateFactory.hh"

#include "hadoop/StringUtils.hh"

const std::string WORDCOUNT = "WORDCOUNT";

const std::string INPUT_WORDS = "INPUT_WORDS";

const std::string OUTPUT_WORDS = "OUTPUT_WORDS";

class WordCountMap: public HadoopPipes::Mapper {

public:

HadoopPipes::TaskContext::Counter*
inputWords;




WordCountMap(HadoopPipes::TaskContext& context)
{

inputWords =
context.getCounter(WORDCOUNT, INPUT_WORDS);

}



void
map(HadoopPipes::MapContext& context) {


std::vector<std::string> words
=


HadoopUtils::splitString(context.getInputValue(), " ");

for(unsigned
int i=0; i < words.size(); ++i) {


context.emit(words[i], "1");

}


context.incrementCounter(inputWords, words.size());

}

};

class WordCountReduce: public HadoopPipes::Reducer {

public:

HadoopPipes::TaskContext::Counter*
outputWords;


WordCountReduce(HadoopPipes::TaskContext& context)
{

outputWords
= context.getCounter(WORDCOUNT, OUTPUT_WORDS);

}

void
reduce(HadoopPipes::ReduceContext& context) {

int sum =
0;

while
(context.nextValue()) {


sum += HadoopUtils::toInt(context.getInputValue());

}


context.emit(context.getInputKey(),
HadoopUtils::toString(sum));


context.incrementCounter(outputWords, 1);

}

};

int main(int argc, char *argv[]) {

return
HadoopPipes::runTask(HadoopPipes::TemplateFactory<WordCountMap,


WordCountReduce>());

}

2.编译

makefile如下：

CC = g++

HADOOP_INSTALL = /home/keke/hadoop-0.20.2-cdh3u4

PLATFORM = Linux-i386-32

CPPFLAGS = -m32 -I$(HADOOP_INSTALL)/c++/$(PLATFORM)/include

wordcount:wordcount.cpp


$(CC) $(CPPFLAGS) $< -Wall
-L$(HADOOP_INSTALL)/c++/$(PLATFORM)/lib -lhadooppipes -lhadooputils
-lpthread -lcrypto -g -O2 -o $@

3.运行

先将只执行文件复制到HDFS上面，例如放在HDFS的bin下

执行：

hadoop pipes -D hadoop.pipes.java.recordreader=true -D
hadoop.pipes.java.recordwrite=true -input /user/keke/input -output
output -program bin/wordcount