Join samples

1. Prepare the jar package for the test program, assuming the name is maid and the local storage path is data \ resources.
2. Prepare tables and resources for testing the Join operation.
   • Create tables:

     create table mr_Join_src1(key bigint, value string);
     create table mr_Join_src2(key bigint, value string);
     create table mr_Join_out(key bigint, value1 string,value2 string);

   • Add resources:

     add jar data\resources\mapreduce-examples.jar -f;

3. Run tunnel to import the data:

   tunnel upload data1 mr_Join_src1;
   tunnel upload data2 mr_Join_src2;

   Import the contents of the maid data as follows:

    1, hello
    2, ODPS

   Import the contents of the maid data as follows:

   1, ODPS
   3,hello
   4, ODPS

Join in odpscmd as follows:-

jar -resources mapreduce-examples.jar -classpath data\resources\mapreduce-examples.jar
com.aliyun.odps.mapred.open.example.Join mr_Join_src1 mr_Join_src2 mr_Join_out;

After the job is completed successfully, the contents of the table maid are output, as follows:

+------------+------------+------------+
| key | value1 | value2 |
+------------+------------+------------+
| 1 | hello | odps | 
+------------+------------+------------+

    package com.aliyun.odps.mapred.open.example;
    import java.io.IOException;
    Import java. util. arraylist;
    import java.util.Iterator;
    import java.util.List;
    import org.apache.commons.logging.Log;
    import org.apache.commons.logging.LogFactory;
    Import com. aliyun. ODPS. Data. record;-
    import com.aliyun.odps.data.TableInfo;
    import com.aliyun.odps.mapred.JobClient;
    import com.aliyun.odps.mapred.MapperBase;
    import com.aliyun.odps.mapred.ReducerBase;
    import com.aliyun.odps.mapred.conf.JobConf;
    import com.aliyun.odps.mapred.utils.InputUtils;
    import com.aliyun.odps.mapred.utils.OutputUtils;
    import com.aliyun.odps.mapred.utils.SchemaUtils;
    /**
     * Join, mr_Join_src1/mr_Join_src2(key bigint, value string), mr_Join_out(key
     * bigint, value1 string, value2 string)
     * 
     */
    public class Join {
      public static final Log LOG = LogFactory.getLog(Join.class);
      public static class JoinMapper extends MapperBase {
        private Record mapkey;
        private Record mapvalue;
        private long tag;
        @Override
        public void setup(TaskContext context) throws IOException{
          mapkey = context.createMapOutputKeyRecord();
          mapvalue = context.createMapOutputValueRecord();
          tag = context.getInputTableInfo().getLabel().equals("left") ? 0: 1;
        }
        @Override
        public void map(long key,Record record, TaskContext context)
            Throws ioexception {
          mapkey.set(0,record.get(0));
          mapkey.set(1,tag);
          for (int i = 1; i< record.getColumnCount();i++) {
            mapvalue.set(i -1, record.get(i));
          }
          context.write(mapkey,mapvalue);
        }
      }
      public static class JoinReducer extends ReducerBase {
        private Record result = null;
        @Override
        public void setup(TaskContext context) throws IOException{
          result = context.createOutputRecord();
        }
        //Reduce function all records for each input will be the same key
        @Override
        public void reduce(Record key,Iterator<Record>values,TaskContext context)
            Throws ioexception {
          long k = key.getBigint(0);
          List<Object[]> leftValues = new ArrayList<Object[]>();
          //Is a key + tag combination because it is set up, this ensures that record data in the left table is in front of the input record for the reduce function.
          while(values.hasNext()) {
            Record value = values.next();
            long tag = (Long)key.get(1);
            //The data for the left table is first cached into memory
            if (tag == 0) {
              leftValues.add(value.toArray().clone());
            }else {
              //The data that touches the right table is output by a join with all the data on the left table, the data for the left table is all in memory.
//This implementation is just a functional display with relatively low performance and is not recommended for practical production.
              for (Object[] leftValue :leftValues) {
                int index = 0;
                result.set(index++,k);
                for (int i = 0;i<leftValue.length;i++) {
                  result.set(index++,leftValue[i]);
                }
                for (int i = 0;i< value.getColumnCount();i++) {
                  result.set(index++,value.get(i));
                }
                context.write(result);
              }
            }
          }
        }
      }
      public static void main(String[] args) throws Exception {
        if (args.length ! = 3) {
          System.err.println("Usage: Join <input table1> <input table2> <out>");
          System.exit(2);
        }
        JobConf job = new JobConf();
        job.setMapperClass(JoinMapper.class);
        job.setReducerClass(JoinReducer.class);
        job.setMapOutputKeySchema(SchemaUtils.fromString("key:bigint,tag:bigint"));
        job.setMapOutputValueSchema(SchemaUtils.fromString("value:string"));
        job.setPartitionColumns(new String[]{"key"});
        job.setOutputKeySortColumns(new String[]{"key", "tag"});
        job.setOutputGroupingColumns(new String[]{"key"});
        job.setNumReduceTasks(1);
        InputUtils.addTable(TableInfo.builder().tableName(args[0]).label("left").build(), job);
        InputUtils.addTable(TableInfo.builder().tableName(args[1]).label("right").build(), job);
        OutputUtils.addTable(TableInfo.builder().tableName(args[2]).build(), job);
        Jobclient. runjob (job );
      }
    }