2. 考虑日志文件，其中每一条日志记录是一行文本。如果想记录二进制类型，纯文本是不合适的。这种情况下，Hadoop的SequenceFile类非常合适，因为上述提供了二进制键/值对的永久存储的数据结构。当作为日志文件的存储格式时，可以自己选择键，比如由LongWritable类型表示的时间戳，以及值可以是Writable类型，用于表示日志记录的数量。SequenceFile同样为可以作为小文件的容器。而HDFS和
MapReduce是针对大文件进行优化的，所以通过SequenceFile类型将小文件包装起来，可以获得更高效率的存储和处理。
3. SequenceFile类内部有两个比较主要的内部类分别是SequenceFile.Reader和SequenceFile.Writer
SequenceFile.Reader
通过createWriter（）静态方法可以创建SequenceFile对象，并返SequenceFile.Writer实例。该静态方法有多个重载版本，但都需要指定待写入的数据流（FSDataOutputStream或FileSystem对象和Path对象），Configuration对象，以及键和值的类型。另外可选参数包括压缩类型以及相应的codec，Progressable回调函数用于通知写入的进度，以及在SequenceFile头文件中存储的Metadata实例。存储在SequenceFile中的键和值对并不一定是Writable类型。任意可以通过Serialization类实现序列化和反序列化的类型均可被使用。一旦拥有SequenceFile.Writer实例，就可以通过append（）方法在文件末尾附件键/值对。
SequenceFile.Writer
创建SequenceFile.Writer可以通过调用本身的构造函数 SequenceFile.Reader(FileSystem fs, Path file, Configuration conf) 来构造实例对象，从头到尾读取顺序文件的过程是创建SequenceFile.Reader实例后反复调用next（）方法迭代读取记录的过程。读取的是哪条记录与你使用的序列化框架相关。如果使用的是Writable类型，那么通过键和值作为参数的Next（）方法可以将数据流中的下一条键值对读入变量中：
public boolean next（Writable key，Writable val），如果键值对成功读取，则返回true，如果已读到文件末尾，则返回false。具体示例代码如下所示：
import
java.io.IOException;
import java.net.URI;
import java.util.Random;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.FileSystem;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.SequenceFile;
import org.apache.hadoop.io.Text;
public class sequence {
/**
* @param args
*/
public static FileSystem fs;
public static final String Output_path="/home/hadoop/test/A.txt";
public static Random random=new Random();
private static final String[] DATA={
"One,two,buckle my shoe",
"Three,four,shut the door",
"Five,six,pick up sticks",
"Seven,eight,lay them straight",
"Nine,ten,a big fat hen"
};
public static Configuration conf=new Configuration();
public static void write(String pathStr) throws IOException{
Path path=new Path(pathStr);
FileSystem fs=FileSystem.get(URI.create(pathStr), conf);
SequenceFile.Writer writer=SequenceFile.createWriter(fs, conf, path, Text.class, IntWritable.class);
Text key=new Text();
IntWritable value=new IntWritable();
for(int i=0;i<DATA.length;i++){
key.set(DATA[i]);
value.set(random.nextInt(10));
System.out.println(key);
System.out.println(value);
System.out.println(writer.getLength());
writer.append(key, value);
}
writer.close();
}
public static void read(String pathStr) throws IOException{
FileSystem fs=FileSystem.get(URI.create(pathStr), conf);
SequenceFile.Reader reader=new SequenceFile.Reader(fs, new Path(pathStr), conf);
Text key=new Text();
IntWritable value=new IntWritable();
while(reader.next(key, value)){
System.out.println(key);
System.out.println(value);
}
}
public static void main(String[] args) throws IOException {
// TODO Auto-generated method stub
write(Output_path);
read(Output_path);
}
}
如果需要在mapreduce中进行SequenceFile的读取和写入，则需要到SequcenFileInputFormat和SequenceFileOutputFormat，示例代码如下所示：
1）输出格式为SequenceFileOutputFormat
public class SequenceFileOutputFormatDemo extends Configured implements Tool {
2）输入格式为SequcenFileInputFormat
public class SequenceFileInputFormatDemo extends Configured implements Tool {
public static class SequenceFileInputFormatDemoMapper extends
Mapper<LongWritable, Text, Text, NullWritable> {
public void map(LongWritable key, Text value, Context context)
throws IOException, InterruptedException {
System.out.println("key: " + key.toString() + " ; value: "
+ value.toString());
}
}
public static void main(String[] args) throws Exception {
int nRet = ToolRunner.run(new Configuration(),
new SequenceFileInputFormatDemo(), args);
System.out.println(nRet);
}
@Override
public int run(String[] args) throws Exception {
Configuration conf = getConf();
Job job = new Job(conf, "sequence file input demo");
job.setJarByClass(SequenceFileInputFormatDemo.class);
FileInputFormat.addInputPaths(job, args[0]);
HdfsUtil.deleteDir(args[1]);
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(SequenceFileInputFormatDemoMapper.class);
job.setNumReduceTasks(1);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(NullWritable.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(Text.class);
job.setInputFormatClass(SequenceFileInputFormat.class);
int result = job.waitForCompletion(true) ? 0 : 1;
return result;
}
}
或者读取的时候也可以如下面的方式进行读取，但是此时输出格式就为普通FileOutputFormat了，输入格式也为普通FileInputFormat了。示例代码如下面所示 ：
public class MapReduceReadFile {
private static SequenceFile.Reader reader = null;
private static Configuration conf = new Configuration();
public static class ReadFileMapper extends
Mapper<LongWritable, Text, LongWritable, Text> {
/* (non-Javadoc)
* @see org.apache.hadoop.mapreduce.Mapper#map(KEYIN, VALUEIN, org.apache.hadoop.mapreduce.Mapper.Context)
*/
@Override
public void map(LongWritable key, Text value, Context context) {
key = (LongWritable) ReflectionUtils.newInstance(
reader.getKeyClass(), conf);
value = (Text) ReflectionUtils.newInstance(
reader.getValueClass(), conf);
try {
while (reader.next(key, value)) {
System.out.printf("%s\t%s\n", key, value);
context.write(key, value);
}
} catch (IOException e1) {
e1.printStackTrace();
} catch (InterruptedException e) {
e.printStackTrace();
}
}
}
/**
* @param args
* @throws IOException
* @throws InterruptedException
* @throws ClassNotFoundException
*/
public static void main(String[] args) throws IOException, InterruptedException, ClassNotFoundException {
Job job = new Job(conf,"read seq file");
job.setJarByClass(MapReduceReadFile.class);
job.setMapperClass(ReadFileMapper.class);
job.setMapOutputValueClass(Text.class);
Path path = new Path("logfile2");
FileSystem fs = FileSystem.get(conf);
reader = new SequenceFile.Reader(fs, path, conf);
FileInputFormat.addInputPath(job, path);
FileOutputFormat.setOutputPath(job, new Path("result"));
System.exit(job.waitForCompletion(true)?0:1);
}
}
原文地址：http://blog.csdn.net/zgc625238677/article/details/40042313
