我们编写了一个mapreduce作业来处理日志文件。截至目前，我们有大约52GB的输入文件，但是处理数据大约需要一个小时。默认情况下它只创建一个reducer作业。通常我们会在reduce任务中看到超时错误然后重新启动并完成。以下是成功完成工作的统计数据。请告诉我们如何改进性能。
File System Counters
FILE: Number of bytes read=876100387
FILE: Number of bytes written=1767603407
FILE: Number of read operations=0
FILE: Number of large read operations=0
FILE: Number of write operations=0
HDFS: Number of bytes read=52222279591
HDFS: Number of bytes written=707429882
HDFS: Number of read operations=351
HDFS: Number of large read operations=0
HDFS: Number of write operations=2
Job Counters
Failed reduce tasks=1
Launched map tasks=116
Launched reduce tasks=2
Other local map tasks=116
Total time spent by all maps in occupied slots (ms)=9118125
Total time spent by all reduces in occupied slots (ms)=7083783
Total time spent by all map tasks (ms)=3039375
Total time spent by all reduce tasks (ms)=2361261
Total vcore-seconds taken by all map tasks=3039375
Total vcore-seconds taken by all reduce tasks=2361261
Total megabyte-seconds taken by all map tasks=25676640000
Total megabyte-seconds taken by all reduce tasks=20552415744
Map-Reduce Framework
Map input records=49452982
Map output records=5730971
Map output bytes=864140911
Map output materialized bytes=876101077
Input split bytes=13922
Combine input records=0
Combine output records=0
Reduce input groups=1082133
Reduce shuffle bytes=876101077
Reduce input records=5730971
Reduce output records=5730971
Spilled Records=11461942
Shuffled Maps =116
Failed Shuffles=0
Merged Map outputs=116
GC time elapsed (ms)=190633
CPU time spent (ms)=4536110
Physical memory (bytes) snapshot=340458307584
Virtual memory (bytes) snapshot=1082745069568
Total committed heap usage (bytes)=378565820416
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=52222265669
File Output Format Counters
Bytes Written=707429882
如果我增加reducer的数量，我会得到如下的一个classcast异常。我想问题来自分区器类。
java.lang.Exception: java.lang.ClassCastException: com.emaar.bigdata.exchg.logs.CompositeWritable cannot be cast to org.apache.hadoop.io.Text
at org.apache.hadoop.mapred.LocalJobRunner$Job.runTasks(LocalJobRunner.java:462)
at org.apache.hadoop.mapred.LocalJobRunner$Job.run(LocalJobRunner.java:522)
Caused by: java.lang.ClassCastException: com.emaar.bigdata.exchg.logs.CompositeWritable cannot be cast to org.apache.hadoop.io.Text
at com.emaar.bigdata.exchg.logs.ActualKeyPartitioner.getPartition(ActualKeyPartitioner.java:1)
at org.apache.hadoop.mapred.MapTask$NewOutputCollector.write(MapTask.java:716)
at org.apache.hadoop.mapreduce.task.TaskInputOutputContextImpl.write(TaskInputOutputContextImpl.java:89)
at org.apache.hadoop.mapreduce.lib.map.WrappedMapper$Context.write(WrappedMapper.java:112)
at com.emaar.bigdata.exchg.logs.ExchgLogsMapper.map(ExchgLogsMapper.java:56)
at com.emaar.bigdata.exchg.logs.ExchgLogsMapper.map(ExchgLogsMapper.java:1)
at org.apache.hadoop.mapreduce.Mapper.run(Mapper.java:146)
at org.apache.hadoop.mapred.MapTask.runNewMapper(MapTask.java:787)
at org.apache.hadoop.mapred.MapTask.run(MapTask.java:341)
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Partitioner;
import org.apache.hadoop.mapreduce.lib.partition.HashPartitioner;
public class ActualKeyPartitioner extends Partitioner<CompositeKey, Text> {
HashPartitioner<Text, Text> hashPartitioner = new HashPartitioner<Text, Text>();
Text newKey = new Text();
@Override
public int getPartition(CompositeKey key, Text value, int numReduceTasks) {
try {
// Execute the default partitioner over the first part of the key
newKey.set(key.getSubject());
return hashPartitioner.getPartition(newKey, value, numReduceTasks);
} catch (Exception e) {
e.printStackTrace();
return (int) (Math.random() * numReduceTasks); // this would return
// a random value in
// the range
// [0,numReduceTasks)
}
}
}
映射器代码
public class ExchgLogsMapper extends Mapper<LongWritable, List<Text>, CompositeKey, Writable> {
String recepientAddresses = "";
public static final String DELIVER = "DELIVER";
public static final String RESOLVED = "Resolved";
public static final String JUNK = "Junk E-mail";
public static final String SEMICOLON = ";";
public static final String FW1 = "FW: ";
public static final String FW2 = "Fw: ";
public static final String FW3 = "FWD: ";
public static final String FW4 = "Fwd: ";
public static final String FW5 = "fwd: ";
public static final String RE1 = "RE: ";
public static final String RE2 = "Re: ";
public static final String RE3 = "re: ";
Text mailType = new Text("NEW");
Text fwType = new Text("FW");
Text reType = new Text("RE");
Text recepientAddr = new Text();
@Override
public void map(LongWritable key, List<Text> values, Context context) throws IOException, InterruptedException {
String subj = null;
int lstSize=values.size() ;
if ((lstSize >= 26)) {
if (values.get(8).toString().equals(DELIVER)) {
if (!(ExclusionList.exclusions.contains(values.get(18).toString()))) {
if (!(JUNK.equals((values.get(12).toString())))) {
subj = values.get(17).toString();
recepientAddresses = values.get(11).toString();
String[] recepientAddressArr = recepientAddresses.split(SEMICOLON);
if (subj.startsWith(FW1) || subj.startsWith(FW2) || subj.startsWith(FW3)
|| subj.startsWith(FW4) || subj.startsWith(FW5)) {
mailType = fwType;
subj = subj.substring(4);
} else if (subj.startsWith(RE1) || subj.startsWith(RE2) || subj.startsWith(RE3)) {
mailType = reType;
subj = subj.substring(4);
}
for (int i = 0; i < recepientAddressArr.length; i++) {
CompositeKey ckey = new CompositeKey(subj, values.get(0).toString());
recepientAddr.set(recepientAddressArr[i]);
CompositeWritable out = new CompositeWritable(mailType, recepientAddr, values.get(18),
values.get(0));
context.write(ckey, out);
// System.err.println(out);
}
}
}
}
}
答案 0 :(得分：1)
循环内的reducer代码中有很少的sysout，它们写了很多日志，删除它们后，reducer在几分钟内就完成了。！