我正在尝试使用测试CombineFileInputFormat来处理几个小文件(20个文件),每个文件大小为8MB。我遵循了这个博客中给出的示例。我能够实现并测试它。最终结果是正确的。但令我惊讶的是,它最终总是只有一张地图。我尝试设置属性"mapred.max.split.size"各种值,如16MB、32MB等(当然是以字节为单位),但没有成功。我还有什么需要做的吗?或者这是正确的行为吗?
我正在运行一个双节点集群,默认复制为2。下面给出的是开发的代码。非常感谢您的帮助。
package inverika.test.retail;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
import org.apache.hadoop.mapreduce.Reducer;
public class CategoryCount {
public static class CategoryMapper
extends Mapper<LongWritable, Text, Text, IntWritable> {
private final static IntWritable one = new IntWritable(1);
private String[] columns = new String[8];
@Override
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
columns = value.toString().split(",");
context.write(new Text(columns[4]), one);
}
}
public static class CategoryReducer
extends Reducer< Text, IntWritable, Text, IntWritable> {
@Override
public void reduce(Text key, Iterable<IntWritable> values, Context context)
throws IOException, InterruptedException {
int sum = 0;
for (IntWritable value : values) {
sum += value.get();
}
context.write(key, new IntWritable(sum));
}
}
public static void main(String args[]) throws Exception {
if (args.length != 2) {
System.err.println("Usage: CategoryCount <input Path> <output Path>");
System.exit(-1);
}
Configuration conf = new Configuration();
conf.set("mapred.textoutputformat.separator", ",");
conf.set("mapred.max.split.size", "16777216"); // 16 MB
Job job = new Job(conf, "Retail Category Count");
job.setJarByClass(CategoryCount.class);
job.setMapperClass(CategoryMapper.class);
job.setReducerClass(CategoryReducer.class);
job.setInputFormatClass(CombinedInputFormat.class);
//CombineFileInputFormat.setMaxInputSplitSize(job, 16777216);
CombinedInputFormat.setMaxInputSplitSize(job, 16777216);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(IntWritable.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(IntWritable.class);
job.setOutputFormatClass(TextOutputFormat.class);
FileInputFormat.addInputPath(job, new Path(args[0]) );
FileOutputFormat.setOutputPath(job, new Path(args[1]) );
//job.submit();
//System.exit(job.waitForCompletion(false) ? 0 : 1);
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
以下是CombinedFileInputFormat实现的
package inverika.test.retail;
import java.io.IOException;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.lib.input.CombineFileRecordReader;
import org.apache.hadoop.mapreduce.lib.input.CombineFileSplit;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.input.LineRecordReader;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.CombineFileInputFormat;
public class CombinedInputFormat extends CombineFileInputFormat<LongWritable, Text> {
@Override
public RecordReader<LongWritable, Text>
createRecordReader(InputSplit split, TaskAttemptContext context)
throws IOException {
CombineFileRecordReader<LongWritable, Text> reader =
new CombineFileRecordReader<LongWritable, Text>(
(CombineFileSplit) split, context, myCombineFileRecordReader.class);
return reader;
}
public static class myCombineFileRecordReader extends RecordReader<LongWritable, Text> {
private LineRecordReader lineRecordReader = new LineRecordReader();
public myCombineFileRecordReader(CombineFileSplit split,
TaskAttemptContext context, Integer index) throws IOException {
FileSplit fileSplit = new FileSplit(split.getPath(index),
split.getOffset(index),
split.getLength(index),
split.getLocations());
lineRecordReader.initialize(fileSplit, context);
}
@Override
public void initialize(InputSplit inputSplit, TaskAttemptContext context)
throws IOException, InterruptedException {
//linerecordReader.initialize(inputSplit, context);
}
@Override
public void close() throws IOException {
lineRecordReader.close();
}
@Override
public float getProgress() throws IOException {
return lineRecordReader.getProgress();
}
@Override
public LongWritable getCurrentKey() throws IOException,
InterruptedException {
return lineRecordReader.getCurrentKey();
}
@Override
public Text getCurrentValue() throws IOException, InterruptedException {
return lineRecordReader.getCurrentValue();
}
@Override
public boolean nextKeyValue() throws IOException, InterruptedException {
return lineRecordReader.nextKeyValue();
}
}
}
使用CombineFileInputFormat
作为输入格式类时,需要设置最大拆分大小。或者,当所有块都来自同一个机架时,您可能只得到一个映射器。
您可以通过以下方式之一实现这一点:
- 调用
CombineFileInputFormat.setMaxSplitSize()
方法 -
设置
mapreduce.input.fileinputformat.split.maxsize
或
mapred.max.split.size
对于exmaple,通过发出以下调用
job.getConfiguration().setLong("mapreduce.input.fileinputformat.split.maxsize", (long)(256*1024*1024));
您正在将最大拆分大小设置为256MB。
参考:
- https://hadoop.apache.org/docs/r2.2.0/api/org/apache/hadoop/mapreduce/lib/input/CombineFileInputFormat.html
- http://mail-archives.apache.org/mod_mbox/hadoop-common-user/201004.mbox/%3C35374.30384.qm@web63402.mail.re1.yahoo.com%3E
如果在使用CombineFileInputFormat时指定了maxSplitSize,那么同一节点上的块将被组合以形成单个拆分,因此在您的场景中,所有文件似乎都在同一节点,因此它们仅构成单个拆分。因此,只有一个Mapper。
有关详细信息,请参阅CombineFileInputFormat文档https://hadoop.apache.org/docs/current/api/org/apache/hadoop/mapred/lib/CombineFileInputFormat.html