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为Hadoop的MapReduce程序编写makefile
最近需要把基于hadoop的MapReduce程序集成到一个大的用C/C++编写的框架中，需要在make的时候自动将MapReduce应用进行编译和打包。这里以简单的WordCount1为例说明具体的实现细节，注意：hadoop版本为2.4.0.
源代码包含两个文件，一个是WordCount1.java是具体的对单词计数实现的逻辑；第二个是CounterThread.java，其中简单的当前处理的行数做一个统计和打印。代码分别见附1. 编写makefile的关键是将hadoop提供的jar包的路径全部加载进来，看到网上很多资料都自己实现一个脚本把hadoop目录下所有的.jar文件放到一个路径中，然后进行编译，这种做法太麻烦了。当然也有些简单的办法，但是都是比较老的hadoop版本如0.20之类的。
其实，hadoop提供了一个命令hadoop classpath可以获得包含所有jar包的路径.所以只需要用 javac -classpath "`hadoop classpath`" *.java 便可，然后使用jar -cvf对class文件进行打包就可以了。具体的Makefile代码如下：
SRC_DIR = src/mypackage/*.java CLASS_DIR = bin TARGET_JAR = WordCount all:$(TARGET_JAR) $(TARGET_JAR): $(SRC_DIR) mkdir -p $(CLASS_DIR) # javac -classpath `$(HADOOP) classpath` -d $(CLASS_DIR) $(SRC_DIR) javac -classpath "`hadoop classpath`" src/mypackage/*.java -d $(CLASS_DIR) -Xlint jar -cvf $(TARGET_JAR).jar -C $(CLASS_DIR) ./ clean: rm -rf $(CLASS_DIR) *.jar
make一下：
lichao@ubuntu:WordCount1$ make mkdir -p bin javac -classpath "`hadoop classpath`" src/mypackage/*.java -d bin -Xlint warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/share/hadoop/common/lib/jaxb-api.jar": no such file or directory warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/share/hadoop/common/lib/activation.jar": no such file or directory warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/share/hadoop/common/lib/jsr173_1.0_api.jar": no such file or directory warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/share/hadoop/common/lib/jaxb1-impl.jar": no such file or directory warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/share/hadoop/yarn/lib/jaxb-api.jar": no such file or directory warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/share/hadoop/yarn/lib/activation.jar": no such file or directory warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/share/hadoop/yarn/lib/jsr173_1.0_api.jar": no such file or directory warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/share/hadoop/yarn/lib/jaxb1-impl.jar": no such file or directory warning: [path] bad path element "/home/lichao/Software/hadoop/hadoop-src/hadoop-2.4.0-src/hadoop-dist/target/hadoop-2.4.0/contrib/capacity-scheduler/*.jar": no such file or directory src/mypackage/WordCount1.java:61: warning: [deprecation] Job(Configuration,String) in Job has been deprecated Job job = new Job(conf, "WordCount1"); //建立新job ^ 10 warnings jar -cvf WordCount.jar -C bin ./ added manifest adding: mypackage/(in = 0) (out= 0)(stored 0%) adding: mypackage/WordCount1.class(in = 1970) (out= 1037)(deflated 47%) adding: mypackage/CounterThread.class(in = 1760) (out= 914)(deflated 48%) adding: mypackage/WordCount1$IntSumReducer.class(in = 1762) (out= 749)(deflated 57%) adding: mypackage/WordCount1$TokenizerMapper.class(in = 1759) (out= 762)(deflated 56%) adding: log4j.properties(in = 476) (out= 172)(deflated 63%)虽然有warning，但是不影响结果。编译后，我们来简单的测试一下。
先生成测试数据：while true; do seq 1 100000 >> tmpfile; done; 差不多可以了就Ctrl+c
然后将数据放到hdfs上，hadoop fs -put tmpfile /data/
接着运行MapReduce程序：hadoop jar WordCount.jar mypackage/WordCount1 /data/tmpfile /output2
效果如下：
14/07/15 13:26:01 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable 14/07/15 13:26:03 INFO client.RMProxy: Connecting to ResourceManager at localhost/127.0.0.1:8032 14/07/15 13:26:05 INFO input.FileInputFormat: Total input paths to process : 1 14/07/15 13:26:05 INFO mapreduce.JobSubmitter: number of splits:6 14/07/15 13:26:06 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1405397597558_0003 14/07/15 13:26:06 INFO impl.YarnClientImpl: Submitted application application_1405397597558_0003 14/07/15 13:26:06 INFO mapreduce.Job: The url to track the job: http://ubuntu:8088/proxy/application_1405397597558_0003/ 14/07/15 13:26:06 INFO mapreduce.Job: Running job: job_1405397597558_0003 14/07/15 13:26:20 INFO mapreduce.Job: Job job_1405397597558_0003 running in uber mode : false 14/07/15 13:26:20 INFO mapreduce.Job: map 0% reduce 0% 14/07/15 13:26:34 WARN mapreduce.Counters: Group org.apache.hadoop.mapred.Task$Counter is deprecated. Use org.apache.hadoop.mapreduce.TaskCounter instead 输入行数：0 14/07/15 13:26:48 INFO mapreduce.Job: map 2% reduce 0% 输入行数：3138474 14/07/15 13:26:51 INFO mapreduce.Job: map 5% reduce 0% 14/07/15 13:26:54 INFO mapreduce.Job: map 6% reduce 0% 14/07/15 13:26:55 INFO mapreduce.Job: map 8% reduce 0% 14/07/15 13:26:57 INFO mapreduce.Job: map 9% reduce 0% 14/07/15 13:26:58 INFO mapreduce.Job: map 11% reduce 0% 14/07/15 13:27:00 INFO mapreduce.Job: map 12% reduce 0% 14/07/15 13:27:01 INFO mapreduce.Job: map 13% reduce 0% 输入行数：23383595 14/07/15 13:27:05 INFO mapreduce.Job: map 14% reduce 0% 输入行数：23383595 14/07/15 13:27:23 INFO mapreduce.Job: map 15% reduce 0% 14/07/15 13:27:27 INFO mapreduce.Job: map 16% reduce 0% 14/07/15 13:27:28 INFO mapreduce.Job: map 18% reduce 0% 14/07/15 13:27:30 INFO mapreduce.Job: map 19% reduce 0% 14/07/15 13:27:31 INFO mapreduce.Job: map 21% reduce 0% 14/07/15 13:27:34 INFO mapreduce.Job: map 24% reduce 0% 输入行数：38430301 14/07/15 13:27:37 INFO mapreduce.Job: map 25% reduce 0% 14/07/15 13:27:40 INFO mapreduce.Job: map 26% reduce 0% 输入行数：42826322 14/07/15 13:27:57 INFO mapreduce.Job: map 27% reduce 0% 14/07/15 13:28:00 INFO mapreduce.Job: map 29% reduce 0% 14/07/15 13:28:02 INFO mapreduce.Job: map 30% reduce 0% 14/07/15 13:28:03 INFO mapreduce.Job: map 32% reduce 0% 输入行数：54513531 14/07/15 13:28:05 INFO mapreduce.Job: map 33% reduce 0% 14/07/15 13:28:06 INFO mapreduce.Job: map 34% reduce 0% 14/07/15 13:28:08 INFO mapreduce.Job: map 35% reduce 0% 14/07/15 13:28:09 INFO mapreduce.Job: map 36% reduce 0% 输入行数：60959081 14/07/15 13:28:22 INFO mapreduce.Job: map 42% reduce 0% 14/07/15 13:28:30 INFO mapreduce.Job: map 43% reduce 0% 14/07/15 13:28:31 INFO mapreduce.Job: map 44% reduce 0% 14/07/15 13:28:34 INFO mapreduce.Job: map 45% reduce 0% 14/07/15 13:28:35 INFO mapreduce.Job: map 46% reduce 0% 输入行数：69936159 14/07/15 13:28:37 INFO mapreduce.Job: map 47% reduce 0% 14/07/15 13:28:38 INFO mapreduce.Job: map 48% reduce 0% 14/07/15 13:28:41 INFO mapreduce.Job: map 49% reduce 0% 14/07/15 13:28:44 INFO mapreduce.Job: map 50% reduce 0% 输入行数：77160461 14/07/15 13:29:01 INFO mapreduce.Job: map 51% reduce 0% 14/07/15 13:29:04 INFO mapreduce.Job: map 52% reduce 0% 14/07/15 13:29:05 INFO mapreduce.Job: map 53% reduce 0% 输入行数：83000373 14/07/15 13:29:07 INFO mapreduce.Job: map 54% reduce 0% 14/07/15 13:29:09 INFO mapreduce.Job: map 55% reduce 0% 14/07/15 13:29:10 INFO mapreduce.Job: map 56% reduce 0% 14/07/15 13:29:13 INFO mapreduce.Job: map 57% reduce 0% 14/07/15 13:29:16 INFO mapreduce.Job: map 58% reduce 0% 输入行数：93361766 14/07/15 13:29:32 INFO mapreduce.Job: map 59% reduce 0% 输入行数：98194696 14/07/15 13:29:35 INFO mapreduce.Job: map 60% reduce 0% 14/07/15 13:29:37 INFO mapreduce.Job: map 61% reduce 0% 14/07/15 13:29:38 INFO mapreduce.Job: map 62% reduce 0% 14/07/15 13:29:40 INFO mapreduce.Job: map 63% reduce 0% 14/07/15 13:29:41 INFO mapreduce.Job: map 64% reduce 0% 14/07/15 13:29:44 INFO mapreduce.Job: map 65% reduce 0% 14/07/15 13:29:48 INFO mapreduce.Job: map 66% reduce 0% 输入行数：109562184 14/07/15 13:30:04 INFO mapreduce.Job: map 67% reduce 0% 输入行数：113362818 14/07/15 13:30:06 INFO mapreduce.Job: map 68% reduce 0% 14/07/15 13:30:08 INFO mapreduce.Job: map 69% reduce 0% 14/07/15 13:30:10 INFO mapreduce.Job: map 70% reduce 0% 14/07/15 13:30:12 INFO mapreduce.Job: map 71% reduce 0% 14/07/15 13:30:15 INFO mapreduce.Job: map 72% reduce 0% 输入行数：123074119 14/07/15 13:30:32 INFO mapreduce.Job: map 76% reduce 0% 14/07/15 13:30:33 INFO mapreduce.Job: map 80% reduce 0% 14/07/15 13:30:34 INFO mapreduce.Job: map 83% reduce 0% 14/07/15 13:30:35 INFO mapreduce.Job: map 84% reduce 0% 输入行数：123074119 14/07/15 13:30:37 INFO mapreduce.Job: map 89% reduce 0% 14/07/15 13:30:38 INFO mapreduce.Job: map 92% reduce 0% 14/07/15 13:30:39 INFO mapreduce.Job: map 95% reduce 0% 14/07/15 13:30:40 INFO mapreduce.Job: map 100% reduce 0% 输入行数：123074119 14/07/15 13:30:53 INFO mapreduce.Job: map 100% reduce 100% 14/07/15 13:30:53 INFO mapreduce.Job: Job job_1405397597558_0003 completed successfully 14/07/15 13:30:53 INFO mapreduce.Job: Counters: 50 File System Counters FILE: Number of bytes read=58256119 FILE: Number of bytes written=66039749 FILE: Number of read operations=0 FILE: Number of large read operations=0 FILE: Number of write operations=0 HDFS: Number of bytes read=724520133 HDFS: Number of bytes written=1088895 HDFS: Number of read operations=21 HDFS: Number of large read operations=0 HDFS: Number of write operations=2 Job Counters Killed map tasks=2 Launched map tasks=8 Launched reduce tasks=1 Data-local map tasks=8 Total time spent by all maps in occupied slots (ms)=1528715 Total time spent by all reduces in occupied slots (ms)=17508 Total time spent by all map tasks (ms)=1528715 Total time spent by all reduce tasks (ms)=17508 Total vcore-seconds taken by all map tasks=1528715 Total vcore-seconds taken by all reduce tasks=17508 Total megabyte-seconds taken by all map tasks=1565404160 Total megabyte-seconds taken by all reduce tasks=17928192 Map-Reduce Framework Map input records=123074119 Map output records=123074119 Map output bytes=1216795535 Map output materialized bytes=7133406 Input split bytes=594 Combine input records=127374119 Combine output records=4900000 Reduce input groups=100000 Reduce shuffle bytes=7133406 Reduce input records=600000 Reduce output records=100000 Spilled Records=5500000 Shuffled Maps =6 Failed Shuffles=0 Merged Map outputs=6 GC time elapsed (ms)=39761 CPU time spent (ms)=1397060 Physical memory (bytes) snapshot=1797943296 Virtual memory (bytes) snapshot=5082316800 Total committed heap usage (bytes)=1398800384 Shuffle Errors BAD_ID=0 CONNECTION=0 IO_ERROR=0 WRONG_LENGTH=0 WRONG_MAP=0 WRONG_REDUCE=0 File Input Format Counters Bytes Read=724519539 File Output Format Counters Bytes Written=1088895
附录1：WordCount1.java和CounterThread.java的代码
//WordCount1.java代码
package mypackage;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class WordCount1 {
public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{
private final static IntWritable one = new IntWritable(1); //建立"int"型变量one,初值为1
private Text word = new Text(); //建立"string:型变量 word,用于接收传入的单词
public void map(Object key, Text value, Context context
) throws IOException, InterruptedException {
StringTokenizer itr = new StringTokenizer(value.toString()); //将输入的文本按行分段
while (itr.hasMoreTokens()) {
word.set(itr.nextToken()); //为word赋值
context.write(word, one); // 将 键-值 对 word one 传入
}
//System.out.println("read lines:"+context.getCounter("org.apache.hadoop.mapred.Task$Counter","MAP_INPUT_RECORDS").getValue());
//System.out.println( "输入行数：" + context.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS").getValue() );
//System.out.println( "输入行数：" + context.getCounters().findCounter("", "MAP_INPUT_RECORDS").getValue() );
}
}
public static class IntSumReducer
extends Reducer<Text,IntWritable,Text,IntWritable> {
private IntWritable result = new IntWritable(); //创建整型变量result
public void reduce(Text key, Iterable<IntWritable> values,
Context context
) throws IOException, InterruptedException {
int sum = 0; //创建int 型变量sum 初值0
for (IntWritable val : values) {
sum += val.get(); //将每个key对应的所有value类间
}
result.set(sum); //sum传入result
context.write(key, result); //将 key-result对传入
}
}
public static void main(String[] args) throws Exception {
Configuration conf = new Configuration();
//String[] newArgs = new String[]{"hdfs://localhost:9000/data/tmpfile","hdfs://localhost:9000/data/wc_output"};
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
if (otherArgs.length != 2) {
System.err.println("Usage: wordcount <in> <out>");
System.exit(2);
}
Job job = new Job(conf, "WordCount1"); //建立新job
job.setJarByClass(WordCount1.class);
job.setMapperClass(TokenizerMapper.class); //设置map类
job.setCombinerClass(IntSumReducer.class); //设置combiner类
job.setReducerClass(IntSumReducer.class); //设置reducer类
job.setOutputKeyClass(Text.class); //输出的key类型
job.setOutputValueClass(IntWritable.class); //输出的value类型
FileInputFormat.addInputPath(job, new Path(otherArgs[0])); //输入输出参数(在设置中指定)
FileOutputFormat.setOutputPath(job, new Path(otherArgs[1]));
CounterThread ct = new CounterThread(job);
ct.start();
job.waitForCompletion(true);
System.exit(0);
//System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}//CounterThread.java的代码
package mypackage;
import java.lang.*;
import java.io.IOException;
import java.util.StringTokenizer;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.JobStatus;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.GenericOptionsParser;
public class CounterThread extends Thread{
public CounterThread(Job job) {
_job = job;
}
public void run() {
while(true){
try {
Thread.sleep(1000*5);
} catch (InterruptedException e1) {
// TODO Auto-generated catch block
e1.printStackTrace();
}
try {
if(_job.getStatus().getState() == JobStatus.State.RUNNING)
//continue;
System.out.println( "输入行数：" + _job.getCounters().findCounter("org.apache.hadoop.mapred.Task$Counter", "MAP_INPUT_RECORDS").getValue() );
} catch (IOException e) {
// TODO Auto-generated catch block
e.printStackTrace();
} catch (InterruptedException e) {
// TODO Auto-generated catch block
e.printStackTrace();
}
}
}
private Job _job;
}
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