【Apache Hadoop】MapReuce 编程总结-多MapReduce执行
学习hadoop，必不可少的就是写MapReduce程序，当然，对于简单的分析程序，我们只需一个MapReduce就能搞定，这里就不提单MapReuce的情况了，网上例子很多，大家可以百度Google一下。对于比较复杂的分析程序，我们可能需要多个Job或者多个Map或者Reduce进行分析计算。
多Job或者多MapReduce的编程形式有以下几种：
1、迭代式MapReduce
MapReduce迭代方式，通常是前一个MapReduce的输出作为下一个MapReduce的输入，最终可只保留最终结果，中间数据可以删除或保留，根据业务需要自己决定
示例代码如下：
Configuration conf = new Configuration();
//first Job
Job job1 = new Job(conf,"job1");
.....
FileInputFormat.addInputPath(job1,InputPaht1);
FileOutputFromat.setOutputPath(job1,Outpath1);
job1.waitForCompletion(true);
//second Mapreduce
Job job2 = new Job(conf1,"job1");
.....
FileInputFormat.addInputPath(job2,Outpath1);
FileOutputFromat.setOutputPath(job2,Outpath2);
job2.waitForCompletion(true);
//third Mapreduce
Job job3 = new Job(conf1,"job1");
.....
FileInputFormat.addInputPath(job3,Outpath2);
FileOutputFromat.setOutputPath(job3,Outpath3);
job3.waitForCompletion(true);
.....下面列举一个mahout怎样运用mapreduce迭代的，下面的代码快就是mahout中kmeans的算法的代码，在main函数中用一个while循环来做mapreduce的迭代，其中：runIteration()是一次mapreduce的过程。
但个人感觉现在的mapreduce迭代设计不太满意的地方。
1. 每次迭代，如果所有Job（task）重复创建，代价将非常高。
2.每次迭代，数据都写入本地和读取本地，I/O和网络传输的代价比较大。
好像Twister和Haloop的模型能过比较好的解决这些问题，但他们抽象度不够高，支持的计算有限。
期待着下个版本hadoop更好的支持迭代算法。
//main function
while (!converged && iteration <= maxIterations) {
log.info("K-Means Iteration {}", iteration);
// point the output to a new directory per iteration
Path clustersOut = new Path(output, AbstractCluster.CLUSTERS_DIR + iteration);
converged = runIteration(conf, input, clustersIn, clustersOut, measure.getClass().getName(), delta);
// now point the input to the old output directory
clustersIn = clustersOut;
iteration++;
}
private static boolean runIteration(Configuration conf,
Path input,
Path clustersIn,
Path clustersOut,
String measureClass,
String convergenceDelta)
throws IOException, InterruptedException, ClassNotFoundException {
conf.set(KMeansConfigKeys.CLUSTER_PATH_KEY, clustersIn.toString());
conf.set(KMeansConfigKeys.DISTANCE_MEASURE_KEY, measureClass);
conf.set(KMeansConfigKeys.CLUSTER_CONVERGENCE_KEY, convergenceDelta);
Job job = new Job(conf, "KMeans Driver running runIteration over clustersIn: " + clustersIn);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(ClusterObservations.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Cluster.class);
job.setInputFormatClass(SequenceFileInputFormat.class);
job.setOutputFormatClass(SequenceFileOutputFormat.class);
job.setMapperClass(KMeansMapper.class);
job.setCombinerClass(KMeansCombiner.class);
job.setReducerClass(KMeansReducer.class);
FileInputFormat.addInputPath(job, input);
FileOutputFormat.setOutputPath(job, clustersOut);
job.setJarByClass(KMeansDriver.class);
HadoopUtil.delete(conf, clustersOut);
if (!job.waitForCompletion(true)) {
throw new InterruptedException("K-Means Iteration failed processing " + clustersIn);
}
FileSystem fs = FileSystem.get(clustersOut.toUri(), conf);
return isConverged(clustersOut, conf, fs);
}2、依赖关系式MapReuce-JobControl
依赖关系式主要是由JobControl来实现，JobControl由两个类组成：Job和JobControl。其中，Job类封装了一个MapReduce作业及其对应的依赖关系，主要负责监控各个依赖作业的运行状态，以此更新自己的状态。
JobControl包含了一个线程用于周期性的监控和更新各个作业的运行状态，调度依赖作业运行完成的作业，提交处于READY状态的作业等，同事，还提供了一些API用于挂起、回复和暂停该线程。
示例代码如下：
Configuration job1conf = new Configuration();
Job job1 = new Job(job1conf,"Job1");
.........//job1 其他设置
Configuration job2conf = new Configuration();
Job job2 = new Job(job2conf,"Job2");
.........//job2 其他设置
Configuration job3conf = new Configuration();
Job job3 = new Job(job3conf,"Job3");
.........//job3 其他设置
job3.addDepending(job1);//设置job3和job1的依赖关系
job3.addDepending(job2);
JobControl JC = new JobControl("123");
JC.addJob(job1);//把三个job加入到jobcontorl中
JC.addJob(job2);
JC.addJob(job3);
JC.run();3、线性链式MapReduce-ChainMapper/ChainReduce
ChainMapper/ChainReduce主要为了解决线性链式Mapper提出的。在Map或者Reduce阶段存在多个Mapper，这些Mapper像Linux管道一样，前一个Mapper的输出结果直接重定向到下一个Mapper的输入，行程流水线。
需要注意的是，对于任意一个MapReduce作业，Map和Reduce阶段可以有无线个Mapper，但是Reduce只能有一个。所以包含多个Reduce的作业，不能使用ChainMapper/ChainReduce来完成。
代码如下：
...
conf.setJobName("chain");
conf.setInputFormat(TextInputFormat.class);
conf.setOutputFormat(TextOutputFormat.class);
JobConf mapper1Conf=new JobConf(false);
JobConf mapper2Conf=new JobConf(false);
JobConf redduce1Conf=new JobConf(false);
JobConf mappe3Conf=new JobConf(false);
...
ChainMapper.addMapper(conf,Mapper1.class,LongWritable.class,Text.class,Text.class,Text.class,true,mapper1Conf);
ChainMapper.addMapper(conf,Mapper2.class,Text.class,Text.class,LongWritable.class,Text.class,false,mapper2Conf);
ChainReducer.setReduce(conf,Reducer.class,LongWritable.class,Text.class,Text.class,Text.class,true,reduce1Conf);
ChainReducer.addMapper(conf,Mapper3.class,Text.class,Text.class,LongWritable.class,Text.class,true,mapper3Conf);
JobClient.runJob(conf);4、子Job式MapReduce
子Job式其实也是迭代式中的一种，我这里单独的提取出来了，说白了，就是一个父Job包含多个子Job。
在nutch中，Crawler是一个父Job，通过run方法中调用runTool工具进行子Job的调用，而runTool是通过反射来调用子Job执行。
下面来看下Nutch里面是如何实现的
....
private NutchTool currentTool = null;
....
private Map runTool(Class toolClass,
Map args) throws Exception {
currentTool = (NutchTool) ReflectionUtils.newInstance(toolClass,
getConf());
return currentTool.run(args);
}
...
@Override
public Map run(Map args) throws Exception {
results.clear();
status.clear();
String crawlId = (String) args.get(Nutch.ARG_CRAWL);
if (crawlId != null) {
getConf().set(Nutch.CRAWL_ID_KEY, crawlId);
}
String seedDir = null;
String seedList = (String) args.get(Nutch.ARG_SEEDLIST);
if (seedList != null) { // takes precedence
String[] seeds = seedList.split("\\s+");
// create tmp. dir
String tmpSeedDir = getConf().get("hadoop.tmp.dir") + "/seed-"
+ System.currentTimeMillis();
FileSystem fs = FileSystem.get(getConf());
Path p = new Path(tmpSeedDir);
fs.mkdirs(p);
Path seedOut = new Path(p, "urls");
OutputStream os = fs.create(seedOut);
for (String s : seeds) {
os.write(s.getBytes());
os.write('\n');
}
os.flush();
os.close();
cleanSeedDir = true;
seedDir = tmpSeedDir;
} else {
seedDir = (String) args.get(Nutch.ARG_SEEDDIR);
}
Integer depth = (Integer) args.get(Nutch.ARG_DEPTH);
if (depth == null)
depth = 1;
boolean parse = getConf().getBoolean(FetcherJob.PARSE_KEY, false);
String solrUrl = (String) args.get(Nutch.ARG_SOLR);
int onePhase = 3;
if (!parse)
onePhase++;
float totalPhases = depth * onePhase;
if (seedDir != null)
totalPhases++;
float phase = 0;
Map jobRes = null;
LinkedHashMap subTools = new LinkedHashMap();
status.put(Nutch.STAT_JOBS, subTools);
results.put(Nutch.STAT_JOBS, subTools);
// inject phase
if (seedDir != null) {
status.put(Nutch.STAT_PHASE, "inject");
jobRes = runTool(InjectorJob.class, args);
if (jobRes != null) {
subTools.put("inject", jobRes);
}
status.put(Nutch.STAT_PROGRESS, ++phase / totalPhases);
if (cleanSeedDir && tmpSeedDir != null) {
LOG.info(" - cleaning tmp seed list in " + tmpSeedDir);
FileSystem.get(getConf()).delete(new Path(tmpSeedDir), true);
}
}
if (shouldStop) {
return results;
}
// run "depth" cycles
for (int i = 0; i < depth; i++) {
status.put(Nutch.STAT_PHASE, "generate " + i);
jobRes = runTool(GeneratorJob.class, args);
if (jobRes != null) {
subTools.put("generate " + i, jobRes);
}
status.put(Nutch.STAT_PROGRESS, ++phase / totalPhases);
if (shouldStop) {
return results;
}
status.put(Nutch.STAT_PHASE, "fetch " + i);
jobRes = runTool(FetcherJob.class, args);
if (jobRes != null) {
subTools.put("fetch " + i, jobRes);
}
status.put(Nutch.STAT_PROGRESS, ++phase / totalPhases);
if (shouldStop) {
return results;
}
if (!parse) {
status.put(Nutch.STAT_PHASE, "parse " + i);
jobRes = runTool(ParserJob.class, args);
if (jobRes != null) {
subTools.put("parse " + i, jobRes);
}
status.put(Nutch.STAT_PROGRESS, ++phase / totalPhases);
if (shouldStop) {
return results;
}
}
status.put(Nutch.STAT_PHASE, "updatedb " + i);
jobRes = runTool(DbUpdaterJob.class, args);
if (jobRes != null) {
subTools.put("updatedb " + i, jobRes);
}
status.put(Nutch.STAT_PROGRESS, ++phase / totalPhases);
if (shouldStop) {
return results;
}
}
if (solrUrl != null) {
status.put(Nutch.STAT_PHASE, "index");
jobRes = runTool(SolrIndexerJob.class, args);
if (jobRes != null) {
subTools.put("index", jobRes);
}
}
return results;
}