MapReduce实现单表关联

核心提示：grandParentNum++;}}if (grandChildNum != 0 && grandParentNum != 0) {//两个数组的X值为grandChild-grandParent关系for (int i = 0; i < grandChildNum; i++) {for (int j = 0;

grandParentNum++;
}
}
if (grandChildNum != 0 && grandParentNum != 0) {//两个数组的X值为grandChild-grandParent关系
for (int i = 0; i < grandChildNum; i++) {
for (int j = 0; j < grandParentNum; j++) {
context.write(new Text(grandChild[i]), new Text(
grandParent[j]));
}
}
}
}
}
@Override
public int run(String[] args) throws Exception {
Configuration conf = getConf();
Job job = new Job(conf, "SingletonTableJoinJob02");
job.setJarByClass(SingletonTableJoin02.class);
FileInputFormat.setInputPaths(job, new Path(args[0]));
FileOutputFormat.setOutputPath(job, new Path(args[1]));
job.setMapperClass(MapClass.class);
//job.setCombinerClass(ReduceClass.class);
job.setReducerClass(ReduceClass.class);
job.setInputFormatClass(TextInputFormat.class);
job.setOutputFormatClass(TextOutputFormat.class);
job.setOutputKeyClass(Text.class);
job.setOutputValueClass(Text.class);
System.exit(job.waitForCompletion(true) ? 0 : 1);
return 0;
}
public static void main(String[] args) throws Exception {
int res = ToolRunner.run(new Configuration(), new SingletonTableJoin02(),
args);
System.exit(res);
}
}

这样就可以实现类型数据库表间的操作了，其实Hive也是利用MapReduce操作实现的