文章目录
0x00 文章内容- 通过SequenceFile合并小文件
- 检验结果
说明：Hadoop集群中，元数据是交由NameNode来管理的，每个小文件就是一个split，会有自己相对应的元数据，如果小文件很多，则会对内存以及NameNode很大的压力，所以可以通过合并小文件的方式来进行优化。合并小文件其实可以有两种方式：一种是通过Sequence格式转换文件来合并，另一种是通过CombineFileInputFormat来实现。
此处选择SequeceFile类型是因为此格式为二进制格式，而且是key-value类型，我们在合并小文件的时候，可以利用此特性，将每个小文件的名称做为key，将每个小文件里面的内容做为value。
0x01 通过SequenceFile合并小文件1. 准备工作
a. 我的HDFS上有四个文件：
[hadoop-sny@master ~]$ hadoop fs -ls /files/
Found 4 items
-rw-r--r-- 1 hadoop-sny supergroup 39 2019-04-18 21:20 /files/put.txt
-rw-r--r-- 1 hadoop-sny supergroup 50 2019-12-30 17:12 /files/small1.txt
-rw-r--r-- 1 hadoop-sny supergroup 31 2019-12-30 17:10 /files/small2.txt
-rw-r--r-- 1 hadoop-sny supergroup 49 2019-12-30 17:11 /files/small3.txt
shao nai yi
nai nai yi yi
shao nai nai
hello hi hi hadoop
spark kafka shao
nai yi nai yi
hello 1
hi 1
shao 3
nai 1
yi 3
guangdong 300
hebei 200
beijing 198
tianjing 209
b. 除了在Linux上创建然后上传外，还可以直接以流的方式输入进去，如small1.txt：
hadoop fs -put - /files/small1.txt
输入完后，按ctrl + D 结束输入。
2. 完整代码
a. SmallFilesToSequenceFileConverter完整代码
package com.shaonaiyi.hadoop.filetype.smallfiles;
import com.shaonaiyi.hadoop.utils.FileUtils;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileSplit;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import java.io.IOException;
/**
* @Author [email protected]
* @Date 2019/12/30 16:29
* @Description 通过SequenceFile合并小文件
*/
public class SmallFilesToSequenceFileConverter {
static class SequenceFileMapper extends Mapper<NullWritable, BytesWritable, Text, BytesWritable> {
private Text fileNameKey;
@Override
protected void setup(Context context) {
InputSplit split = context.getInputSplit();
Path path = ((FileSplit) split).getPath();
fileNameKey = new Text(path.toString());
}
@Override
protected void map(NullWritable key, BytesWritable value, Context context) throws IOException, InterruptedException {
context.write(fileNameKey, value);
}
}
public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
Job job = Job.getInstance(new Configuration(), "SmallFilesToSequenceFileConverter");
job.setJarByClass(SmallFilesToSequenceFileConverter.class);
job.setInputFormatClass(WholeFileInputFormat.class);
job.setoutputKeyClass(Text.class);
job.setoutputValueClass(BytesWritable.class);
job.setoutputFormatClass(SequenceFileOutputFormat.class);
job.setMapperClass(SequenceFileMapper.class);
FileInputFormat.addInputPath(job, new Path(args[0]));
String outputPath = args[1];
FileUtils.deleteFileIfExists(outputPath);
FileOutputFormat.setoutputPath(job, new Path(outputPath));
System.exit(job.waitForCompletion(true) ? 0 : 1);
}
}
b. WholeFileInputFormat完整代码
package com.shaonaiyi.hadoop.filetype.smallfiles;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.mapreduce.InputSplit;
import org.apache.hadoop.mapreduce.JobContext;
import org.apache.hadoop.mapreduce.RecordReader;
import org.apache.hadoop.mapreduce.TaskAttemptContext;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import java.io.IOException;
/**
* @Author [email protected]
* @Date 2019/12/30 16:34
* @Description 实现WholeFileInputFormat类
*/
public class WholeFileInputFormat extends FileInputFormat<NullWritable, BytesWritable> {
@Override
protected boolean issplitable(JobContext context, Path filename) {
return false;
}
@Override
public RecordReader<NullWritable, BytesWritable> createRecordReader(InputSplit inputSplit, TaskAttemptContext taskAttemptContext) throws IOException, InterruptedException {
WholeFileRecordReader reader = new WholeFileRecordReader();
reader.initialize(inputSplit, taskAttemptContext);
return reader;
}
}
c. WholeFileRecordReader完整代码
package com.shaonaiyi.hadoop.filetype.smallfiles;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FSDataInputStream;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.IoUtils;
import org.apache.hadoop.io.NullWritable;
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.FileSplit;
import java.io.IOException;
/**
* @Author [email protected]
* @Date 2019/12/30 16:35
* @Description 实现WholeFileRecordReader类
*/
public class WholeFileRecordReader extends RecordReader<NullWritable, BytesWritable> {
private FileSplit fileSplit;
private Configuration configuration;
private BytesWritable value = new BytesWritable();
private boolean processed = false;
@Override
public void initialize(InputSplit inputSplit, TaskAttemptContext taskAttemptContext) throws IOException, InterruptedException {
this.fileSplit = (FileSplit)inputSplit;
this.configuration = taskAttemptContext.getConfiguration();
}
@Override
public boolean nextkeyvalue() throws IOException, InterruptedException {
if (!processed) {
byte[] contents = new byte[(int)fileSplit.getLength()];
Path file = fileSplit.getPath();
FileSystem fs = file.getFileSystem(configuration);
FSDataInputStream in = null;
try {
in = fs.open(file);
IoUtils.readFully(in, contents, 0, contents.length);
value.set(contents, 0, contents.length);
} finally {
IoUtils.closeStream(in);
}
processed = true;
return true;
}
return false;
}
@Override
public NullWritable getCurrentKey() throws IOException, InterruptedException {
return NullWritable.get();
}
@Override
public BytesWritable getCurrentValue() throws IOException, InterruptedException {
return value;
}
@Override
public float getProgress() throws IOException, InterruptedException {
return processed ? 1.0f : 0.0f;
}
@Override
public void close() throws IOException {
}
}
1. 启动HDFS和YARN
start-dfs.shstart-yarn.sh
2. 执行作业
a. 打包并上传到master上执行，需要传入两个参数
yarn jar ~/jar/hadoop-learning-1.0.jar com.shaonaiyi.hadoop.filetype.smallfiles.SmallFilesToSequenceFileConverter /files /output
3. 查看执行结果
job.setInputFormatClass(CombineTextInputFormat.class);
具体操作请参考教程：通过CombineTextInputFormat实现合并小文件(调优技能)
作者简介：邵奈一
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