CentOS HDFS如何实现数据压缩

在CentOS上使用Hadoop分布式文件系统（HDFS）实现数据压缩，可以遵循以下步骤：

1. 安装Hadoop

首先，确保你已经在CentOS上安装并配置好了Hadoop。你可以从Apache Hadoop官方网站下载并按照安装指南进行安装。

2. 配置HDFS

编辑Hadoop的配置文件，主要是core-site.xml和hdfs-site.xml。

core-site.xml

<configuration> <property> <name>fs.defaultFS</name> <value>hdfs://your-namenode:8020</value> </property> </configuration> 

hdfs-site.xml

<configuration> <property> <name>dfs.replication</name> <value>3</value> </property> <property> <name>dfs.namenode.handler.count</name> <value>100</value> </property> <property> <name>dfs.datanode.handler.count</name> <value>100</value> </property> <property> <name>dfs.blocksize</name> <value>134217728</value> <!-- 128MB --> </property> <property> <name>dfs.namenode.datanode.registration.ip-hostname-check</name> <value>false</value> </property> </configuration> 

3. 启动HDFS

启动Hadoop集群：

start-dfs.sh 

4. 配置压缩编解码器

Hadoop支持多种压缩编解码器，如Gzip、Snappy、LZO等。你可以在core-site.xml中配置默认的压缩编解码器。

core-site.xml

<configuration> <property> <name>io.compression.codecs</name> <value>org.apache.hadoop.io.compress.GzipCodec,org.apache.hadoop.io.compress.SnappyCodec</value> </property> </configuration> 

5. 使用Hadoop命令行工具进行压缩

你可以使用Hadoop命令行工具来压缩文件。例如，使用Gzip压缩一个文件：

hadoop fs -copyFromLocal -p /local/path/file.txt /user/hadoop/file.txt.gz 

6. 在MapReduce作业中使用压缩

在MapReduce作业中，你可以配置输出格式和编解码器来使用压缩。

Mapper和Reducer代码示例

import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Reducer; public class WordCount { public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { private final static IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(Object key, Text value, Context context) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> { private IntWritable result = new IntWritable(); public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } result.set(sum); context.write(key, result); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); } } 

配置MapReduce作业使用压缩

在Job对象中配置输出格式和编解码器：

Configuration conf = new Configuration(); Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(TokenizerMapper.class); job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); // 设置输出格式为SequenceFileOutputFormat，并使用Snappy压缩 job.setOutputFormatClass(SequenceFileOutputFormat.class); SequenceFileOutputFormat.setOutputCompressionType(job, CompressionType.BLOCK); SequenceFileOutputFormat.setCompressKey(job, true); SequenceFileOutputFormat.setCompressValue(job, true); SequenceFileOutputFormat.setOutputCompressionCodec(job, SnappyCodec.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); System.exit(job.waitForCompletion(true) ? 0 : 1); 

7. 验证压缩

你可以使用Hadoop命令行工具来验证文件是否已经被压缩：

hadoop fs -ls /user/hadoop/ hadoop fs -get /user/hadoop/file.txt.gz /local/path/ 

通过以上步骤，你可以在CentOS上使用HDFS实现数据压缩，并在MapReduce作业中应用压缩技术来提高数据传输和存储的效率。