前言
在做需求时,经常遇到多个目录,也就是多个维度进行join,这里分析一下,数据是怎么流动的。
1、多目录输入
使用MultipleInputs.addInputPath() 对多目录制定格式和map
2、数据流分析
map按行读入数据,需要对不同的输入目录,打上不同的标记(这个方法又叫reduce端连接),map在输出后会进行partition和sort,按照key进行排序,然后输出到reduce进行处理。
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import java.io.IOException; import java.util.Iterator; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.LongWritable; import org.apache.hadoop.io.Text; import org.apache.hadoop.mapreduce.Job; import org.apache.hadoop.mapreduce.Mapper; import org.apache.hadoop.mapreduce.Partitioner; import org.apache.hadoop.mapreduce.Reducer; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; import util.TextPair; import com.sina.hadoop.MultipleInputs; public class Main extends Configured implements Tool { public static void main(String[] args) throws Exception { int exitcode = ToolRunner.run(new Main(), args); System.exit(exitcode); } /** * 分区 */ static class TextPairKeyPartitioner extends Partitioner<TextPair, Text> { public int getPartition(TextPair key, Text value, int numPartitions) { return (key.getFirst().hashCode() & Integer.MAX_VALUE) % numPartitions; } } public int run(String[] arg0) throws Exception { int exitcode = 0; if (exitcode == 0) { Job job1 = new Job(); job1.setJobName("testMultipleInputs"); job1.setJarByClass(Main.class); MultipleInputs.addInputPath(job1, new Path("xx/testMultipleInputs/input/a/"), TextInputFormat.class, AMapper.class); MultipleInputs.addInputPath(job1, new Path("xx/testMultipleInputs/input/b/"), TextInputFormat.class, BMapper.class); MultipleInputs.addInputPath(job1, new Path("xx/testMultipleInputs/input/c/"), TextInputFormat.class, CMapper.class); job1.setReducerClass(TestReducer.class); FileOutputFormat.setOutputPath(job1, new Path("xx/testMultipleInputs/output/")); job1.setOutputKeyClass(Text.class); job1.setOutputValueClass(Text.class); job1.setPartitionerClass(TextPairKeyPartitioner.class); job1.setGroupingComparatorClass(TextPair.FirstComparator.class); job1.setMapOutputKeyClass(TextPair.class); job1.setMapOutputValueClass(Text.class); job1.setNumReduceTasks(1); exitcode = job1.waitForCompletion(true) ? 0 : 1; } return exitcode; } public class AMapper extends Mapper<LongWritable, Text, TextPair, Text> { public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String[] data = value.toString().split("\t", -1); String id = ""; if (data.length >= 1) { id = data[0]; if (!"".equals(id)) { context.write(new TextPair(id, "1"), new Text("0")); } } } } public class BMapper extends Mapper<LongWritable, Text, TextPair, Text> { public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String[] data = value.toString().split("\t", -1); String id1 = ""; String id2 = ""; if (data.length >= 2) { id1 = data[0]; id2 = data[1]; if (!"".equals(id1)) { context.write(new TextPair(id1, "2"), new Text(id2)); } } } } public class CMapper extends Mapper<LongWritable, Text, TextPair, Text> { public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String[] data = value.toString().split("\t", -1); String id1 = ""; String id2 = ""; if (data.length >= 2) { id1 = data[0]; id2 = data[1]; if (!"".equals(id1)) { context.write(new TextPair(id1, "3"), new Text(id2)); } } } } public class TestReducer extends Reducer<TextPair, Text, Text, Text> { public void reduce(TextPair key, Iterable<Text> values, Context context) throws IOException, InterruptedException { String data = ""; Iterator<Text> i = values.iterator(); while (i.hasNext()) { data = i.next().toString(); context.write(key.getFirst(), new Text(data)); } } } }
Hadoop多目录输入,join,进入reduce,数据流分析,布布扣,bubuko.com
Hadoop多目录输入,join,进入reduce,数据流分析
原文地址:http://blog.csdn.net/smile0198/article/details/35573315