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通过SequenceFile实现合并小文件(调优技能)

作者:互联网

文章目录

0x00 文章内容
  1. 通过SequenceFile合并小文件
  2. 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

内容对应如下,其实内容可以随意:

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 shaonaiyi@163.com
 * @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 shaonaiyi@163.com
 * @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 shaonaiyi@163.com
 * @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 {

    }
}
0x02 检验结果
1. 启动HDFS和YARN

start-dfs.sh
start-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

a. 生成了一份文件
在这里插入图片描述
b. 查看到里面的内容如下,但内容很难看
在这里插入图片描述
c. 用text查看文件内容,可看到key为文件名,value为二进制的里面的内容。在这里插入图片描述

0xFF 总结
  1. Input的路径有4个文件,默认会启动4个mapTask,其实我们可以通过CombineTextInputFormat设置成只启动一个:
    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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标签:hadoop,mapreduce,调优,IOException,SequenceFile,org,apache,import,技能
来源: https://blog.51cto.com/u_12564104/2896715