Mapreduce 数据清洗 更改

Posted 2020-11-23 zlj843767688

package test;

import java.io.IOException;
import java.text.ParseException;
import java.text.SimpleDateFormat;
import java.util.Date;
import java.util.Locale;






import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
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.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;

public class Filter {
    
    public static class Map extends Mapper<Object, Text, Text, NullWritable> {
        private static Text newKey = new Text();

        /*public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
            String line = value.toString();
            System.out.println(line);
            String arr[] = line.split(" ");
            newKey.set(arr[1]);
            context.write(newKey, NullWritable.get());
            System.out.println(newKey);
        }
    }*/
    public void map(Object key, Text value, Context context) throws IOException, InterruptedException {
        String S1 = value.toString    ();
         LogParser parser = new LogParser();
            final String[] array = parser.parse(S1);
        System.out.println(S1);
        /*System.out.format(
                "解析结果：  ip=%s, time=%s,day=%s, traffic=%s, type=%s,id=%s",
                array[0], array[1], array[2], array[3], array[4],array[5]);*/
        String a=array[0];
        String u=array[1];
        String c=array[2];
        String d=array[3];
        String e=array[4];
        String f=array[5];
        
        String str = a +","+u +","+c+","+d+","+e+","+f;
        
        newKey.set(str);
        context.write(newKey, NullWritable.get());
        System.out.println(newKey);
    }
}

    public static class Reduce extends Reducer<Text, NullWritable, Text, NullWritable> {
        public void reduce(Text key, Iterable<NullWritable> values, Context context)
                throws IOException, InterruptedException {
            context.write(key, NullWritable.get());
        }
    }

    public static void main(String[] args) throws IOException, ClassNotFoundException, InterruptedException {
        Configuration conf = new Configuration();
        System.out.println("start");
        
    
        Job job = new Job(conf, "filter");
        job.setJarByClass(Filter.class);
        job.setMapperClass(Map.class);
        job.setReducerClass(Reduce.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(NullWritable.class);
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        Path in = new Path("hdfs://localhost:9000/user/hadoop/in/Result");
        Path out = new Path("hdfs://localhost:9000/user/hadoop/out");
        FileInputFormat.addInputPath(job, in);
        FileOutputFormat.setOutputPath(job, out);
        System.exit(job.waitForCompletion(true) ? 0 : 1);
    }
    static class MyMapper extends
    Mapper<LongWritable, Text, LongWritable, Text> {
LogParser logParser = new LogParser();
Text outputValue = new Text();

protected void map(
        LongWritable key,
        Text value,
        org.apache.hadoop.mapreduce.Mapper<LongWritable, Text, LongWritable, Text>.Context context)
        throws java.io.IOException, InterruptedException {
    final String[] parsed = logParser.parse(value.toString());

    // step1.过滤掉静态资源访问请求
    if (parsed[2].startsWith("GET /static/")
            || parsed[2].startsWith("GET /uc_server")) {
        return;
    }
    // step2.过滤掉开头的指定字符串
    if (parsed[2].startsWith("GET /")) {
        parsed[2] = parsed[2].substring("GET /".length());
    } else if (parsed[2].startsWith("POST /")) {
        parsed[2] = parsed[2].substring("POST /".length());
    }
    // step3.过滤掉结尾的特定字符串
    if (parsed[2].endsWith(" HTTP/1.1")) {
        parsed[2] = parsed[2].substring(0, parsed[2].length()
                - " HTTP/1.1".length());
    }
    // step4.只写入前三个记录类型项
    outputValue.set(parsed[0] + "	" + parsed[1] + "	" + parsed[2]);
    context.write(key, outputValue);
}
}

static class MyReducer extends
    Reducer<LongWritable, Text, Text, NullWritable> {
protected void reduce(
        LongWritable k2,
        java.lang.Iterable<Text> v2s,
        org.apache.hadoop.mapreduce.Reducer<LongWritable, Text, Text, NullWritable>.Context context)
        throws java.io.IOException, InterruptedException {
    for (Text v2 : v2s) {
        context.write(v2, NullWritable.get());
    }
};
}

/*
* 日志解析类
*/
static class  LogParser {
public static final SimpleDateFormat FORMAT = new SimpleDateFormat(
        "d/MMM/yyyy:HH:mm:ss", Locale.ENGLISH);
public static final SimpleDateFormat dateformat1 = new SimpleDateFormat(
        "yyyy-MM-dd HH:mm:ss");



/**
 * 解析英文时间字符串
 * 
 * @param string
 * @return
 * @throws ParseException
 */
private Date parseDateFormat(String string) {
    Date parse = null;
    try {
        parse = FORMAT.parse(string);
    } catch (ParseException e) {
        e.printStackTrace();
    }
    return parse;
}

/**
 * 解析日志的行记录
 * 
 * @param line
 * @return 数组含有5个元素，分别是ip、时间、日期、状态、流量
 */
public String[] parse(String line) {
    String ip = parseIP(line);
    String time = parseTime(line);
    String day = parseday(line);
    String traffic = parseTraffic(line);
    String  type = parsertype(line);
    String  id = parseid( line);


    return new String[] { ip, time, day,traffic , type, id };
}
private String parseIP(String line) {
    String ip = line.split(",")[0].trim();
    return ip;
}

private String parseTime(String line) {
    final int first = line.indexOf(",");
    final int last = line.indexOf(" +0800,");
    String time = line.substring(first + 1, last).trim();
    Date date = parseDateFormat(time);
    return dateformat1.format(date);
}

private String parseday(String line) {
    String riqi = line.split(",")[2].trim();
    return riqi;
}
private String parseTraffic(String line) {
    String riqi = line.split(",")[3].trim();
    return riqi;
}
//private String parseTraffic(String line) {
   // final String trim = line.substring(line.lastIndexOf(",") + 1)
      //      .trim();
    //String traffic = trim.split(" ")[0];
    //return traffic;
//}

//private String parsertype(String line) {
 //   final int first = line.indexOf(",");
   // final int last = line.lastIndexOf(",");
  //  String url = line.substring(first + 1, last);
  //  return url;
//}
private String parsertype(String line) {
    String riqi = line.split(",")[4].trim();
    return riqi;
}

private String parseid(String line) {
    final String trim = line.substring(line.lastIndexOf(",") + 1)
            .trim();
    String id = trim.split(" ")[0];
    return id;
}






}

    
    
}

将清洗后输出的分隔符改为“,”,然后建表里，用 逗号分隔开。

create table if not exists hive.data(ip string,`time` string,day string,traffic bigint,type string,id string)row format delimited fields terminated by ‘,‘; 建表语句

load data inpath ‘hdfs://localhost:9000/user/hadoop/out/part-r-00000‘ overwrite into table data;导入hive数据表里