前言：Hadoop当时我们弄时几乎没有什么中文文档。现在介绍的资料已经很多了，我就不再赘述。
业务描述：设定inputpath和ouputpath，根据访问日志分析某一个应用访问某一个API的总次数和总流量，统计后分别输出到两个文件中。注意：本案例我是改自阿里巴巴文初的那篇文章，他用的是0.17的版本，现在新的版本有很大的变动，我使用最新的0.20.2来改写了。
public class LogAnalysiser {
public static class MapClass
extends
org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, LongWritable>
{
public void map(LongWritable key, Text value,
OutputCollector<Text, LongWritable> output, Reporter reporter)
throws IOException
{
String line = value.toString();// 没有配置RecordReader，所以默认采用line的实现，key就是行号，value就是行内容
if (line == null || line.equals(""))
return;
String[] words = line.split(",");
if (words == null || words.length < 8)
return;
String appid = words[1];
String apiName = words[2];
LongWritable recbytes = new LongWritable(Long.parseLong(words[7]));
Text record = new Text();
record.set(new StringBuffer("flow::").append(appid)
.append("::").append(apiName).toString());
reporter.progress();
output.collect(record, recbytes);// 输出流量的统计结果，通过flow::作为前缀来标示。
record.clear();
record.set(new StringBuffer("count::").append(appid).append("::")
.append(apiName).toString());
output.collect(record, new LongWritable(1));// 输出次数的统计结果，通过count::作为前缀来标示
}
}
public static class PartitionerClass extends
org.apache.hadoop.mapreduce.Partitioner<Text, LongWritable>
{
public int getPartition(Text key, LongWritable value, int numPartitions)
{
if (numPartitions >= 2)// Reduce 个数，判断流量还是次数的统计分配到不同的Reduce
if (key.toString().startsWith("flow::"))
return 0;
else
return 1;
else
return 0;
}
/*public void configure(JobConf job) {
}*/
public void configure(Job job) {
}
}
public static class ReduceClass
extends
org.apache.hadoop.mapreduce.Reducer<Text, LongWritable, Text, LongWritable>
{
public void reduce(Text key, Iterator<LongWritable> values,
OutputCollector<Text, LongWritable> output, Reporter reporter)
throws IOException
{
Text newkey = new Text();
newkey.set(key.toString().substring(
key.toString().indexOf("::") + 2));
LongWritable result = new LongWritable();
long tmp = 0;
int counter = 0;
while (values.hasNext())// 累加同一个key的统计结果
{
tmp = tmp + values.next().get();
counter = counter + 1;// 担心处理太久，JobTracker长时间没有收到报告会认为TaskTracker已经失效，因此定时报告一下
if (counter == 1000)
{
counter = 0;
reporter.progress();
}
}
result.set(tmp);
output.collect(newkey, result);// 输出最后的汇总结果
}
}
public static class CombinerClass
extends
org.apache.hadoop.mapreduce.Reducer<Text, LongWritable, Text, LongWritable>
{
public void reduce(Text key, Iterator<LongWritable> values,
OutputCollector<Text, LongWritable> output, Reporter reporter)
throws IOException
{
LongWritable result = new LongWritable();
long tmp = 0;
while (values.hasNext())// 累加同一个key的统计结果
{
tmp = tmp + values.next().get();
}
result.set(tmp);
}
}
public static void main(String[] args)
{
try
{
run(args);
} catch (Exception e)
{
e.printStackTrace();
}
}
public static void run(String[] args) throws Exception
{
if (args == null || args.length < 2)
{
System.out.println("need inputpath and outputpath");
return;
}
String inputpath = args[0];
String outputpath = args[1];
String shortin = args[0];
String shortout = args[1];
if (shortin.indexOf(File.separator) >= 0)
shortin = shortin.substring(shortin.lastIndexOf(File.separator));
if (shortout.indexOf(File.separator) >= 0)
shortout = shortout.substring(shortout.lastIndexOf(File.separator));
SimpleDateFormat formater = new SimpleDateFormat("yyyy.MM.dd");
shortout = new StringBuffer(shortout).append("-")
.append(formater.format(new Date())).toString();
if (!shortin.startsWith("/"))
shortin = "/" + shortin;
if (!shortout.startsWith("/"))
shortout = "/" + shortout;
shortin = "/user/root" + shortin;
shortout = "/user/root" + shortout;
File inputdir = new File(inputpath);
File outputdir = new File(outputpath);
if (!inputdir.exists() || !inputdir.isDirectory())
{
System.out.println("inputpath not exist or isn't dir!");
return;
}
if (!outputdir.exists())
{
new File(outputpath).mkdirs();
}
Configuration conf = new Configuration();
Job job = new Job(conf, "analysis job");
job.setJarByClass(LogAnalysiser.class);
// JobConf conf = new JobConf(new Configuration(),
// LogAnalysiser.class);// 构建Config
FileSystem fileSys = FileSystem.get(conf);
fileSys.copyFromLocalFile(new Path(inputpath), new Path(shortin));// 将本地文件系统的文件拷贝到HDFS中
job.setJobName("analysisjob");
job.setOutputKeyClass(Text.class);// 输出的key类型，在OutputFormat会检查
job.setOutputValueClass(LongWritable.class); // 输出的value类型，在OutputFormat会检查
job.setMapperClass(MapClass.class);
job.setCombinerClass(CombinerClass.class);
job.setReducerClass(ReduceClass.class);
job.setPartitionerClass(PartitionerClass.class);
// job.set("mapred.reduce.tasks", "2");//老版本中的写法
// 强制需要有两个Reduce来分别处理流量和次数的统计,现在的版本中已经没有这个方法了
job.setNumReduceTasks(2);//新版本0.22.x中的方法
FileInputFormat.setInputPaths(job, shortin);// hdfs中的输入路径
FileOutputFormat.setOutputPath(job, new Path(shortout));// hdfs中输出路径
Date startTime = new Date();
System.out.println("Job started: " + startTime);
// JobClient.runJob(job);
Date end_time = new Date();
System.out.println("Job ended: " + end_time);
System.out.println("The job took "
+ (end_time.getTime() - startTime.getTime()) / 1000
+ " seconds.");
// 删除输入和输出的临时文件
fileSys.copyToLocalFile(new Path(shortout), new Path(outputpath));
fileSys.delete(new Path(shortin), true);
fileSys.delete(new Path(shortout), true);
}
}
//执行类
public class ExampleDriver {
public static void main(String argv[]){
ProgramDriver pgd = new ProgramDriver();
try {
pgd.addClass("analysislog", LogAnalysiser.class, "A map/reduce program that analysis log .");
pgd.driver(argv);
}
catch(Throwable e){
e.printStackTrace();
}
}
}