MapReduce的方式进行HBase向HDFS导入和导出

附录代码:

HBase---->HDFS

 import java.io.IOException;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.fs.Path;
 import org.apache.hadoop.hbase.HBaseConfiguration;
 import org.apache.hadoop.hbase.client.Result;
 import org.apache.hadoop.hbase.client.Scan;
 import org.apache.hadoop.hbase.io.ImmutableBytesWritable;
 import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
 import org.apache.hadoop.hbase.mapreduce.TableMapper;
 import org.apache.hadoop.io.Text;
 import org.apache.hadoop.mapreduce.Job;
 import org.apache.hadoop.mapreduce.Mapper;
 import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

 public class HBase2HDFS {

     public static void main(String[] args) throws Exception {
         Configuration conf = HBaseConfiguration.create();
         Job job = Job.getInstance(conf, HBase2HDFS.class.getSimpleName());
         job.setJarByClass(HBase2HDFS.class);
         //MR有输入和输出,输入一般是FileInputFormat等...但是在HBase中需要用到一个特殊的工具类是TableMapReduceUtil
         TableMapReduceUtil.initTableMapperJob(args[0], new Scan(), HBase2HDFSMapper.class,
                                             Text.class, Text.class, job);
         //HBase中的具体操作打到MR的job中.
         TableMapReduceUtil.addDependencyJars(job);
         job.setMapperClass(HBase2HDFSMapper.class);
         job.setMapOutputKeyClass(Text.class);
         job.setMapOutputValueClass(Text.class);
         job.setOutputFormatClass(TextOutputFormat.class);
         FileOutputFormat.setOutputPath(job, new Path(args[1]));
         //FileOutputFormat.setOutputPath(job, new Path("/t1-out"));
         job.setNumReduceTasks(0);
         job.waitForCompletion(true);

     }
     static class HBase2HDFSMapper extends TableMapper<Text, Text>{
         private Text rowKeyText = new Text();
         private Text value = new Text();

         //这个TableMapper中的两个泛型是Map阶段的输出..HBase中的数据要想进入HBase,几乎都用引号引起来.
         //TableMapper是Mapper类的一个子类.这个类用来定义前面的两个泛型参数.
         @Override
         protected void map(
                 ImmutableBytesWritable key,
                 Result result,
                 Mapper<ImmutableBytesWritable, Result, Text, Text>.Context context)
                 throws IOException, InterruptedException {
             //结果都在result对象,用raw方法从result对象中找到数据. 这个raw()方法已经过时了.
             /*
             KeyValue[] raw = result.raw();
             for (KeyValue keyValue : raw) {
                 keyValue.getValue();
             }
             */
             /*
              * 想输出的数据格式如下: 1 zhangsan 13  (行键,name,age)
              *                     2 lisi 14
              */

             //要想精确的获得某一列的值,要根据行键,列族,列的时间戳.
             //getColumnLatestCell 是获得最新的时间戳的值 相当于时间戳已经定义好了.
             byte[] nameBytes = result.getColumnLatestCell("cf".getBytes(), "name".getBytes()).getValue();
             byte[] ageBytes = result.getColumnLatestCell("cf".getBytes(), "age".getBytes()).getValue();

             rowKeyText.set(key.get());
             value.set(new String(nameBytes) + "\t" + new String(ageBytes));
             context.write(new Text(key.get()), value);
             //这里已经把数据搞成了 1 name age 的形式....就不需要写Reduce
         }
     }
 }

HDFS---->HBase 通过MR导入到HBase

 import java.io.IOException;

 import org.apache.hadoop.conf.Configuration;
 import org.apache.hadoop.hbase.HBaseConfiguration;
 import org.apache.hadoop.hbase.client.Mutation;
 import org.apache.hadoop.hbase.client.Put;
 import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
 import org.apache.hadoop.hbase.mapreduce.TableOutputFormat;
 import org.apache.hadoop.hbase.mapreduce.TableReducer;
 import org.apache.hadoop.io.LongWritable;
 import org.apache.hadoop.io.NullWritable;
 import org.apache.hadoop.io.Text;
 import org.apache.hadoop.mapreduce.Job;
 import org.apache.hadoop.mapreduce.Mapper;
 import org.apache.hadoop.mapreduce.Reducer;
 import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
 import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

 public class HDFS2HBaseImport {

     public static void main(String[] args) throws Exception {
         Configuration conf = HBaseConfiguration.create();
         conf.set(TableOutputFormat.OUTPUT_TABLE, args[0]);

         Job job = Job.getInstance(conf, HDFS2HBaseImport.class.getSimpleName());
         job.setJarByClass(HDFS2HBaseImport.class);

         //数据到底放到哪一张表中,还是要用到TableMapReduceUtil类.
         TableMapReduceUtil.addDependencyJars(job);
         job.setMapperClass(HDFS2HBaseMapper.class);
         job.setMapOutputKeyClass(Text.class);
         job.setMapOutputValueClass(Text.class);
         job.setOutputFormatClass(TextOutputFormat.class);
         job.setReducerClass(HDFS2HBaseReducer.class);
         job.setOutputFormatClass(TableOutputFormat.class);
         FileInputFormat.setInputPaths(job, args[1]);
         job.waitForCompletion(true);
     }

     static class HDFS2HBaseMapper extends Mapper<LongWritable, Text, Text, Text>{
         private Text rowKeyText = new Text();
         private Text value = new Text();

         @Override
         protected void map(LongWritable key, Text text,
                 Mapper<LongWritable, Text, Text, Text>.Context context)
                 throws IOException, InterruptedException {
             String[] splits = text.toString().split("\t");
             rowKeyText.set(splits[0]);
             value.set(splits[1] + "\t" + splits[2]);//name\tage
             context.write(rowKeyText, value);
         }
     }
     //Reduce继承的是和在导出的时候Map extends TableMapper 对应的  因为导入的是HBase中,所以后面的参数用NullWritable代替
     static class HDFS2HBaseReducer extends TableReducer<Text, Text, NullWritable> {
         @Override
         protected void reduce(Text k2, Iterable<Text> v2s,
                 Reducer<Text, Text, NullWritable, Mutation>.Context context)
                 throws IOException, InterruptedException {
             //向HBase中插入数据一定要用到Put对象.
             Put put = new Put(k2.getBytes());

             for (Text text : v2s) {
                 String[] splits = text.toString().split("\t");
                 //加载列和对应的值
                 put.add("cf".getBytes(), "name".getBytes(), splits[0].getBytes());
                 put.add("cf".getBytes(), "age".getBytes(), splits[1].getBytes());
                 context.write(NullWritable.get(), put);//一个参数是key,一个是对应的value.
                 //导入HBase不需要key...直接用NullWritable对象和封装好数据的put对象.
             }
         }
     }
 }