hadoop编程实践 - “清洗”(根据具体需求)
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项目文件:Github
需求一:
package test.dataclean; import java.io.IOException; import java.net.URI; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; 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.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; /* * @ author:Kouch * * “清洗”思路: * 1 map: 获取的一行数据;判断一行字符串长度; * 2 reduce: * * 注:结合具体需求; */ public class DataHandle1 //map public static class Map extends Mapper<Object,Text,Text,Text> private static Text line=new Text(); public void map(Object key,Text value,Context context) throws IOException, InterruptedException line=value; //测试 System.out.println("内容:"+line); //一行字符串长度; String str=line.toString(); //System.out.println("zhuan:"+str); if(str.length()>20) context.write(line, new Text("")); //reduce public static class Reduce extends Reducer<Text,Text,Text,Text> public void reduce(Text key,Iterable<Text>values,Context context) throws IOException, InterruptedException //测试 //System.out.println("内容:"+key); context.write(key, new Text("")); //main public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException //配置类 Configuration conf=new Configuration(); conf.set("mapred.job.tracker", "localhost:9000"); //获取传参 //方式一: String[] ioArgs=new String[] "input/dailydata1.txt","out"; String[] otherArgs=new GenericOptionsParser(conf,ioArgs).getRemainingArgs(); if(otherArgs.length!=2) System.err.println("Usage:Data Clean <in> <out> - path?"); System.exit(2); //判断输出文件是否存在;存在-删除; String url="hdfs://localhost:9000/user/kouch/"+ioArgs[1]; FileSystem fs=FileSystem.get(URI.create(url), conf); if(fs.delete(new Path(url), true)) //true:文件夹下所有文件;false:如果此文件存在其他文件就不删除 System.out.println("删除"+url); //Job设置 Job job=Job.getInstance(); job.setJarByClass(Deduplication.class); job.setMapperClass(Map.class); job.setCombinerClass(Reduce.class); job.setReducerClass(Reduce.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(Text.class); //设置输入输出目录 FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); //等待job完成之后再返回结果并退出程序 System.exit(job.waitForCompletion(true)?0:1);
需求二:
package test.dataclean; import java.io.IOException; import java.net.URI; import java.util.Iterator; import org.apache.hadoop.conf.Configuration; import org.apache.hadoop.fs.FileSystem; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.IntWritable; 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.FileOutputFormat; import org.apache.hadoop.util.GenericOptionsParser; /* * @ author:Kouch * * “清洗”思路: * 1 map: 获取的一行数据;去除错误数据;截取有效字段;输入context; * 2 reduce: * * 注:结合具体需求; * * 统计:get/post/head 请求; */ public class DataHandle2 //map public static class Map extends Mapper<Object,Text,Text,IntWritable> private static final IntWritable one = new IntWritable(1); private static Text line=new Text(); public void map(Object key,Text value,Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException line=value; //测试 //System.out.println("内容:"+line); String str=line.toString(); if(!(str.indexOf("%")>0)) //System.out.println("内容:"+line); String[] strs=str.split("\""); //System.out.println("内容:"+strs[1]); String need=strs[1]; if(need.startsWith("G")) //System.out.println("G"); context.write(new Text("Get"), one); else if(need.startsWith("H")) //System.out.println("H"); context.write(new Text("Head"), one); else if(need.startsWith("P")) //System.out.println("P"); context.write(new Text("Post"), one); else //reduce public static class Reduce extends Reducer<Text,IntWritable,Text,IntWritable> private IntWritable result = new IntWritable(); public void reduce(Text key,Iterable<IntWritable>values,Context context) throws IOException, InterruptedException //测试 //System.out.println("内容:"+key); int sum=0; //迭代累计频率; IntWritable val; for(Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) val = (IntWritable)i$.next(); this.result.set(sum); context.write(key, this.result); //main public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException //配置类 Configuration conf=new Configuration(); conf.set("mapred.job.tracker", "localhost:9000"); //获取传参 //方式一: String[] ioArgs=new String[] "input/daya2.txt","out3"; String[] otherArgs=new GenericOptionsParser(conf,ioArgs).getRemainingArgs(); if(otherArgs.length!=2) System.err.println("Usage:Data Clean <in> <out> - path?"); System.exit(2); //判断输出文件是否存在;存在-删除; String url="hdfs://localhost:9000/user/kouch/"+ioArgs[1]; FileSystem fs=FileSystem.get(URI.create(url), conf); if(fs.delete(new Path(url), true)) //true:文件夹下所有文件;false:如果此文件存在其他文件就不删除 System.out.println("删除"+url); //Job设置 Job job=Job.getInstance(); job.setJarByClass(Deduplication.class); job.setMapperClass(Map.class); job.setCombinerClass(Reduce.class); job.setReducerClass(Reduce.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); //设置输入输出目录 FileInputFormat.addInputPath(job, new Path(otherArgs[0])); FileOutputFormat.setOutputPath(job, new Path(otherArgs[1])); //等待job完成之后再返回结果并退出程序 System.exit(job.waitForCompletion(true)?0:1);
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