混合数据源的MapReduce作业:HBase表和HDFS文件

Question

我需要实现一个MR作业，该作业可以访问HBase表和HDFS文件中的数据。例如，mapper从HBase表和HDFS文件中读取数据，这些数据共享相同的主键，但具有不同的模式。然后，reducer将所有列（来自HBase表和HDFS文件）连接在一起。

我试着在网上查找，但找不到用这种混合数据源运行MR作业的方法。MultipleInputs似乎只适用于多个HDFS数据源。如果你有什么想法，请告诉我。示例代码会很棒。

Answer 1

经过几天的调查（并从HBase用户邮件列表中获得帮助），我终于找到了如何做到这一点

public class MixMR {
public static class Map extends Mapper<Object, Text, Text, Text> {
    public void map(Object key, Text value, Context context) throws IOException,   InterruptedException {
        String s = value.toString();
        String[] sa = s.split(",");
        if (sa.length == 2) {
            context.write(new Text(sa[0]), new Text(sa[1]));
        }
    }
}
public static class TableMap extends TableMapper<Text, Text>  {
    public static final byte[] CF = "cf".getBytes();
    public static final byte[] ATTR1 = "c1".getBytes();
    public void map(ImmutableBytesWritable row, Result value, Context context) throws IOException, InterruptedException {
        String key = Bytes.toString(row.get());
        String val = new String(value.getValue(CF, ATTR1));
        context.write(new Text(key), new Text(val));
    }
}

public static class Reduce extends Reducer  <Object, Text, Object, Text> {
    public void reduce(Object key, Iterable<Text> values, Context context)
            throws IOException, InterruptedException {
        String ks = key.toString();
        for (Text val : values){
            context.write(new Text(ks), val);
        }
    }
}
public static void main(String[] args) throws Exception {
Path inputPath1 = new Path(args[0]);
    Path inputPath2 = new Path(args[1]);
    Path outputPath = new Path(args[2]);
    String tableName = "test";
    Configuration config = HBaseConfiguration.create();
    Job job = new Job(config, "ExampleRead");
    job.setJarByClass(MixMR.class);     // class that contains mapper
    Scan scan = new Scan();
    scan.setCaching(500);        // 1 is the default in Scan, which will be bad for MapReduce jobs
    scan.setCacheBlocks(false);  // don't set to true for MR jobs
    scan.addFamily(Bytes.toBytes("cf"));
    TableMapReduceUtil.initTableMapperJob(
            tableName,        // input HBase table name
              scan,             // Scan instance to control CF and attribute selection
              TableMap.class,   // mapper
              Text.class,             // mapper output key
              Text.class,             // mapper output value
              job);

    job.setReducerClass(Reduce.class);    // reducer class
    job.setOutputFormatClass(TextOutputFormat.class);   

    // inputPath1 here has no effect for HBase table
    MultipleInputs.addInputPath(job, inputPath1, TextInputFormat.class, Map.class);
    MultipleInputs.addInputPath(job, inputPath2,  TableInputFormat.class, TableMap.class);
    FileOutputFormat.setOutputPath(job, outputPath); 
    job.waitForCompletion(true);
}

}

Answer 2

没有OOTB功能支持这一点。一个可能的解决方法是先扫描HBase表并将结果写入HDFS文件，然后使用MultipleInputs进行reduce side join。但这将产生一些额外的I/O开销。

Answer 3

pig脚本或配置单元查询可以很容易地做到这一点。

示例清管器脚本

tbl = LOAD 'hbase://SampleTable'
       USING org.apache.pig.backend.hadoop.hbase.HBaseStorage(
       'info:* ...', '-loadKey true -limit 5')
       AS (id:bytearray, info_map:map[],...);
fle = LOAD '/somefile' USING PigStorage(',') AS (id:bytearray,...);
Joined = JOIN A tbl by id,fle by id;
STORE Joined to ...