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ETL项目2:大数据清洗,处理:使用MapReduce进行离线数据分析并报表显示完整项目

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ETL项目2:大数据清洗,处理:使用MapReduce进行离线数据分析并报表显示完整项目

思路同我之前的博客的思路 https://www.cnblogs.com/symkmk123/p/10197467.html

但是数据是从web访问的数据

技术分享图片

avro第一次过滤

观察数据的格式,我们主要分析第四个字段的数据.发现有.css , .jpg .png等等等无效的数据.

通过观察数据发现有效数据都不带 . , 所以第一次过滤写入avro总表里的数据一次过滤后的有效数据,不包含 .css , .jpg , .png 这样的数据

同时count持久化到mysql

技术分享图片

orc1:海牛的topics 最受欢迎的top10

通过观察发现这个需求的有效url是 /topics/数字的 所以在第一次过滤的数据的基础上的正则就是

技术分享图片

这种保留下来的也只是/topics/数字这种格式,方便用 hql统计结果

 上代码

//Text2Avro
package mrrun.hainiuetl;

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

import org.apache.avro.Schema;
import org.apache.avro.generic.GenericData;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.mapred.AvroKey;
import org.apache.avro.mapreduce.AvroJob;
import org.apache.avro.mapreduce.AvroKeyOutputFormat;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.mapreduce.CounterGroup;
import org.apache.hadoop.mapreduce.Counters;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import mrrun.base.BaseMR;

public class Text2Avro extends BaseMR
{
    public static Schema schema = null;
    
    public static Schema.Parser parse = new Schema.Parser();
    
public static class Text2AvroMapper extends Mapper<LongWritable, Text, AvroKey<GenericRecord>, NullWritable>
{
        
    
    
        @Override
        protected void setup(Mapper<LongWritable, Text, AvroKey<GenericRecord>, NullWritable>.Context context)
                throws IOException, InterruptedException {
            //根据user_install_status.avro文件内的格式，生成指定格式的schema对象
            schema = parse.parse(Text2Avro.class.getResourceAsStream("/hainiu.avro"));
            
        }
        @Override
        protected void map(LongWritable key, Text value,Context context)
                throws IOException, InterruptedException {
            String line = value.toString();
            
            String[] splits = line.split("\001");
            if(splits == null || splits.length != 10){
                System.out.println("==============");
                System.out.println(value.toString());
                context.getCounter("etl_err", "bad line num").increment(1L);
                return;
            }
            
//            System.out.println(util.getIpArea("202.8.77.12"));
            String uip1 = splits[0];
            String uip =IPUtil.getip(uip1);
            
            String datetime = splits[2];
            StringBuilder sb=new StringBuilder(datetime);
            
            SimpleDateFormat sdf=new SimpleDateFormat("dd/MMM/yyyy:HH:mm:ss",Locale.ENGLISH);
            String sy=sb.toString();
            Date myDate = null;
            try
            {
                myDate = sdf.parse(sy);
            } catch (ParseException e)
            {
                // TODO Auto-generated catch block
                e.printStackTrace();
            }
            
            SimpleDateFormat sdf2=new SimpleDateFormat("yyyyMMddHHmmss");
            //System.out.println(myDate);
            String format = sdf2.format(myDate);
            //GET /categories/8?filter=recent&page=12 HTTP/1.1
            String url1 = splits[3];
            StringBuilder sb2=new StringBuilder(url1);
            
            String url = sb2.toString();
            String method="";
            String top="";
            String top1="";
            String http="";
            if(url!=null)
            {
                String[] s = url.split(" ");
                if(s.length==3)
                {
                    method=s[0];
                    http=s[2];
                    
                    top1=s[1];
                    if(top1.contains("."))
                    {
                        context.getCounter("etl_err", "no line num").increment(1L);
                        return;
                    }
                    else
                    {
                        top=top1;
                    }
                }
            }
            
            String status1 = splits[4];
            String status2 = splits[5];
            String post = splits[6];
            String from = splits[7];
            String usagent1 = splits[8];
            StringBuilder sb3=new StringBuilder(usagent1);
        
            String usagent = sb3.toString();
            
            
            //根据创建的Schema对象，创建一行的对象
            GenericRecord record = new GenericData.Record(Text2Avro.schema);
            record.put("uip", uip);
            record.put("datetime", format);
            record.put("method", method);
            record.put("http", http);
            record.put("top", top);
            record.put("from", from);
            record.put("status1", status1);
            record.put("status2", status2);
            record.put("post", post);
            record.put("usagent", usagent);
            
            context.getCounter("etl_good", "good line num").increment(1L);
            System.out.println(uip+"    "+format+"    "+top+"    "+from+"    "+post+"    "+usagent+"    "+status1+"    "+status2+"    "+http);
            
            
            context.write(new AvroKey<GenericRecord>(record), NullWritable.get());
            
        
        }
    }
    
    
    
    
    @Override
    public Job getJob(Configuration conf) throws IOException {
//        // 开启reduce输出压缩
//        conf.set(FileOutputFormat.COMPRESS, "true");
//        // 设置reduce输出压缩格式
//        conf.set(FileOutputFormat.COMPRESS_CODEC, SnappyCodec.class.getName());
                
        Job job = Job.getInstance(conf, getJobNameWithTaskId());

        job.setJarByClass(Text2Avro.class);
        
        job.setMapperClass(Text2AvroMapper.class);
        
        job.setMapOutputKeyClass(AvroKey.class);
        job.setMapOutputValueClass(NullWritable.class);
        
//        无reduce
        job.setNumReduceTasks(0);
        
        //设置输出的format
        job.setOutputFormatClass(AvroKeyOutputFormat.class);
        
        //根据user_install_status.avro文件内的格式，生成指定格式的schema对象
        schema = parse.parse(Text2Avro.class.getResourceAsStream("/hainiu.avro"));
        
        //设置avro文件的输出
        AvroJob.setOutputKeySchema(job, schema);
        
        FileInputFormat.addInputPath(job, getFirstJobInputPath());
        
        FileOutputFormat.setOutputPath(job, getJobOutputPath(getJobNameWithTaskId()));
        
        
         
        return job;

    }

    @Override
    public String getJobName() {


        return "etltext2avro";

    }

}
//Avro2Orc_topic10
package mrrun.hainiuetl;

import java.io.IOException;
import java.util.regex.Matcher;
import java.util.regex.Pattern;

import org.apache.avro.Schema;
import org.apache.avro.generic.GenericRecord;
import org.apache.avro.mapred.AvroKey;
import org.apache.avro.mapreduce.AvroJob;
import org.apache.avro.mapreduce.AvroKeyInputFormat;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.hive.ql.io.orc.CompressionKind;
import org.apache.hadoop.hive.ql.io.orc.OrcNewOutputFormat;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import mrrun.base.BaseMR;
import mrrun.util.OrcFormat;
import mrrun.util.OrcUtil;

public class Avro2Orc_topic10 extends BaseMR {
    public static Schema schema = null;
    
    public static Schema.Parser parse = new Schema.Parser();
    

    public static class Avro2OrcMapper extends Mapper<AvroKey<GenericRecord>, NullWritable, NullWritable, Writable>{
        OrcUtil orcUtil = new OrcUtil();
        
        @Override
        protected void setup(Context context)
                throws IOException, InterruptedException {
            orcUtil.setWriteOrcInspector(OrcFormat.etlorcSchema_topic10);
            
        }
        
        @Override
        protected void map(AvroKey<GenericRecord> key, NullWritable value,Context context)
                throws IOException, InterruptedException {
            //得到一行的对象
            GenericRecord datum = key.datum();
            
            String uip = (String) datum.get("uip");
            String datetime = (String) datum.get("datetime");
            //String method = (String) datum.get("method");
            //String http = (String) datum.get("http");
            String top1 = (String) datum.get("top");
            String top="";
            String regex="/topics/\\d+";
            Pattern pattern=Pattern.compile(regex);
            Matcher matcher=pattern.matcher(top1);
            if(matcher.find())
            {
                 top=matcher.group();
            }
            else
            {
                context.getCounter("etl_err", "notopics line num").increment(1L);
                return;
            }
            
            
            
            //orcUtil.addAttr(uip,datetime,method,http,uid,country,status1,status2,usagent);
            orcUtil.addAttr(uip,datetime,top);
            
            Writable w = orcUtil.serialize();
            context.getCounter("etl_good", "good line num").increment(1L);
            System.out.println(uip+"    "+top);
            
            context.write(NullWritable.get(), w);
            
        }
        
    }
    

    
    @Override
    public Job getJob(Configuration conf) throws IOException {
        
        //关闭map的推测执行，使得一个map处理 一个region的数据
        conf.set("mapreduce.map.spedulative", "false");
        //设置orc文件snappy压缩
        conf.set("orc.compress", CompressionKind.SNAPPY.name());
        //设置orc文件 有索引
        conf.set("orc.create.index", "true");
                
        Job job = Job.getInstance(conf, getJobNameWithTaskId());

        job.setJarByClass(Avro2Orc_topic10.class);
        
        job.setMapperClass(Avro2OrcMapper.class);
        
        job.setMapOutputKeyClass(NullWritable.class);
        job.setMapOutputValueClass(Writable.class);

        
//        无reduce
        job.setNumReduceTasks(0);
        
        job.setInputFormatClass(AvroKeyInputFormat.class);
        
        //根据user_install_status.avro文件内的格式，生成指定格式的schema对象
        schema = parse.parse(Avro2Orc_topic10.class.getResourceAsStream("/hainiu.avro"));
        
        AvroJob.setInputKeySchema(job, schema);
        
    
        job.setOutputFormatClass(OrcNewOutputFormat.class);
        
        
        FileInputFormat.addInputPath(job, getFirstJobInputPath());
        
        FileOutputFormat.setOutputPath(job, getJobOutputPath(getJobNameWithTaskId()));
        return job;

    }

    @Override
    public String getJobName() {


        return "etlAvro2Orc_topic10";

    }

}
//Text2AvroJob 
package mrrun.hainiuetl;


import java.text.SimpleDateFormat;
import java.util.Date;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.mapreduce.CounterGroup;
import org.apache.hadoop.mapreduce.Counters;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.jobcontrol.ControlledJob;
import org.apache.hadoop.mapreduce.lib.jobcontrol.JobControl;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

import mrrun.util.JobRunResult;
import mrrun.util.JobRunUtil;

public class Text2AvroJob extends Configured implements Tool{
    
    @Override
    public int run(String[] args) throws Exception {
        //获取Configuration对象
        Configuration conf = getConf();
        
        
        //创建任务链对象
        JobControl jobc = new JobControl("etltext2avro");
        
        Text2Avro avro = new Text2Avro();
        
        //只需要赋值一次就行
        avro.setConf(conf);
        
        ControlledJob orcCJob = avro.getControlledJob();
        
        Job job = orcCJob.getJob();
        job.waitForCompletion(true);
        
        JobRunResult result = JobRunUtil.run(jobc);
        result.setCounters("etl1", orcCJob.getJob().getCounters());
        
        result.print(true);
        
        Counters counterMap = result.getCounterMap("etl1");
         CounterGroup group1 = counterMap.getGroup("etl_good");
         CounterGroup group2 = counterMap.getGroup("etl_err");
        
         Counter good = group1.findCounter("good line num");
         Counter bad = group2.findCounter("bad line num");
        System.out.println("\t\t"+good.getDisplayName()+"  =  "+good.getValue());
        System.out.println("\t\t"+bad.getDisplayName()+"  =  "+bad.getValue());
        System.out.println("=======+++++++++====");
        

        Date date=new Date();
        SimpleDateFormat sdf3=new SimpleDateFormat("yyyyMMdd");
        String format2 = sdf3.format(date);
        Results results=new Results();
        long bad_num = bad.getValue();
        long good_num = good.getValue();
    
        long total_num=bad_num+good_num;
        results.setBad_num(bad_num);
        results.setGood_num(good_num);
        
        results.setTotal_num(total_num);
        results.setDay(format2);
        double d=bad_num*1.0/total_num*1.0;
        
        results.setBad_rate(d);
        
        
        System.out.println((double)((double)bad_num/(double)total_num));
        
        DAO dao=new DAO();
        if(dao.getday(format2)!=null)
        {
            Results getday = dao.getday(format2);
            Long bad_num2 = getday.getBad_num();
            Long good_num2 = getday.getGood_num();
            Long total_num2 = getday.getTotal_num();
            getday.setDay(format2);
            getday.setBad_num(bad_num2+bad_num);
            getday.setGood_num(good_num2+good_num);
            
            getday.setTotal_num(total_num2+total_num);
            double badrate=(bad_num2+bad_num)*1.0/(total_num2+total_num)*1.0;
        
            
            getday.setBad_rate(badrate);
        
            dao.update(getday);
        }
        else
        {
            dao.insert(results);
        }
        
        jobc.addJob(orcCJob);
        
        return 0;
        
    }

    
    public static void main(String[] args) throws Exception {
//        -Dtask.id=1226 -Dtask.input.dir=/tmp/avro/input_hainiuetl -Dtask.base.dir=/tmp/avro
        System.exit(ToolRunner.run(new Text2AvroJob(), args));
    }

}

放一个

自动化脚本思路同第一个ETL项目

直接放代码

yitiaolong.sh

#！/bin/bash
source /etc/profile
mmdd=`date -d 1‘ days ago‘ +%m%d`
yymm=`date -d 1‘ days ago‘ +%Y%m`
dd=`date -d 1‘ days ago‘ +%d`
/usr/local/hive/bin/hive -e "use suyuan09;alter table etlavrosy add IF NOT EXISTS partition(month=‘${yymm}‘,day=‘${dd}‘);"
/usr/local/hive/bin/hive -e "use suyuan09;alter table hainiuetltopics10_orc add IF NOT EXISTS partition(month=‘${yymm}‘,day=‘${dd}‘);"
/usr/local/hive/bin/hive -e "use suyuan09;alter table hainiuetlcategories10_orc add IF NOT EXISTS partition(month=‘${yymm}‘,day=‘${dd}‘);"
/usr/local/hive/bin/hive -e "use suyuan09;alter table hainiuetlspider_orc add IF NOT EXISTS partition(month=‘${yymm}‘,day=‘${dd}‘);"
/usr/local/hive/bin/hive -e "use suyuan09;alter table hainiuetlip_orc add IF NOT EXISTS partition(month=‘${yymm}‘,day=‘${dd}‘);"
/usr/local/hive/bin/hive -e "use suyuan09;alter table hainiuetlindex5_orc add IF NOT EXISTS partition(month=‘${yymm}‘,day=‘${dd}‘);"


#3-4运行mr
hdfs_path1=/user/hainiu/data/hainiuetl/input/${yymm}/${dd}
avro_path1=/user/suyuan09/hainiuetl/hainiuavro/${yymm}/${dd}
`/usr/local/hadoop/bin/hadoop  jar /home/suyuan09/etl/hainiu/jar/181210_hbase-1.0.0-symkmk123.jar etltext2avro -Dtask.id=${mmdd} -Dtask.input.dir=${hdfs_path1} -Dtask.base.dir=${avro_path1}`

#orctopics10mr.sh

avro_path2=/user/suyuan09/hainiuetl/hainiuavro/${yymm}/${dd}/etltext2avro_${mmdd}/part-*.avro
orc_path2=/user/suyuan09/hainiuetl/orctopics10/${yymm}/${dd}
`/usr/local/hadoop/bin/hadoop  jar /home/suyuan09/etl/hainiu/jar/181210_hbase-1.0.0-symkmk123.jar etlavro2orc_topic10 -Dtask.id=${mmdd} -Dtask.input.dir=${avro_path2} -Dtask.base.dir=${orc_path2}`

#orccategories10mr.sh


avro_path3=/user/suyuan09/hainiuetl/hainiuavro/${yymm}/${dd}/etltext2avro_${mmdd}/part-*.avro
orc_path3=/user/suyuan09/hainiuetl/orccategories10/${yymm}/${dd}
`/usr/local/hadoop/bin/hadoop  jar /home/suyuan09/etl/hainiu/jar/181210_hbase-1.0.0-symkmk123.jar etlavro2orc_categories10 -Dtask.id=${mmdd} -Dtask.input.dir=${avro_path3} -Dtask.base.dir=${orc_path3}`

#orcspidermr.sh

avro_path4=/user/suyuan09/hainiuetl/hainiuavro/${yymm}/${dd}/etltext2avro_${mmdd}/part-*.avro
orc_path4=/user/suyuan09/hainiuetl/orcspider/${yymm}/${dd}
`/usr/local/hadoop/bin/hadoop  jar /home/suyuan09/etl/hainiu/jar/181210_hbase-1.0.0-symkmk123.jar etlavro2orc_spider -Dtask.id=${mmdd} -Dtask.input.dir=${avro_path4} -Dtask.base.dir=${orc_path4}`

#orcipmr.sh

avro_path5=/user/suyuan09/hainiuetl/hainiuavro/${yymm}/${dd}/etltext2avro_${mmdd}/part-*.avro
orc_path5=/user/suyuan09/hainiuetl/orcip/${yymm}/${dd}
`/usr/local/hadoop/bin/hadoop  jar /home/suyuan09/etl/hainiu/jar/181210_hbase-1.0.0-symkmk123.jar etlavro2orc_ip -Dtask.id=${mmdd} -Dtask.input.dir=${avro_path5} -Dtask.base.dir=${orc_path5}`

#orcindex5mr.sh

avro_path6=/user/suyuan09/hainiuetl/hainiuavro/${yymm}/${dd}/etltext2avro_${mmdd}/part-*.avro
orc_path6=/user/suyuan09/hainiuetl/orcindex5/${yymm}/${dd}
`/usr/local/hadoop/bin/hadoop  jar /home/suyuan09/etl/hainiu/jar/181210_hbase-1.0.0-symkmk123.jar etlavro2orc_index5 -Dtask.id=${mmdd} -Dtask.input.dir=${avro_path6} -Dtask.base.dir=${orc_path6}`

#把orc挪到分区目录  
#orc2etl.sh

/usr/local/hadoop/bin/hadoop fs -cp hdfs://ns1/user/suyuan09/hainiuetl/orctopics10/${yymm}/${dd}/etlAvro2Orc_topic10_${mmdd}/part-*  hdfs://ns1/user/suyuan09/etlorc/hainiuetltopics10_orc/month=${yymm}/day=${dd}
/usr/local/hadoop/bin/hadoop fs -cp hdfs://ns1/user/suyuan09/hainiuetl/orccategories10/${yymm}/${dd}/etlAvro2Orc_categories10_${mmdd}/part-*  hdfs://ns1/user/suyuan09/etlorc/hainiuetlcategories10_orc/month=${yymm}/day=${dd}
/usr/local/hadoop/bin/hadoop fs -cp hdfs://ns1/user/suyuan09/hainiuetl/orcspider/${yymm}/${dd}/etlAvro2Orc_spider_${mmdd}/part-*  hdfs://ns1/user/suyuan09/etlorc/hainiuetlspider_orc/month=${yymm}/day=${dd}
/usr/local/hadoop/bin/hadoop fs -cp hdfs://ns1/user/suyuan09/hainiuetl/orcindex5/${yymm}/${dd}/etlAvro2Orc_index5_${mmdd}/part-*  hdfs://ns1/user/suyuan09/etlorc/hainiuetlindex5_orc/month=${yymm}/day=${dd}
/usr/local/hadoop/bin/hadoop fs -cp hdfs://ns1/user/suyuan09/hainiuetl/orcip/${yymm}/${dd}/etlAvro2Orc_ip_${mmdd}/part-*  hdfs://ns1/user/suyuan09/etlorc/hainiuetlip_orc/month=${yymm}/day=${dd}




#自动从hive到mysql脚本
#hive2data.sh

/usr/local/hive/bin/hive  -e "use suyuan09;select t.top,t.num from(select top,count(*) num from hainiuetlindex5_orc group by top) t  sort by t.num desc limit 5;" >  /home/suyuan09/etl/hainiu/orc2mysql/myindex5${yymmdd}
/usr/local/hive/bin/hive  -e "use suyuan09;select t.top,t.num from(select top,count(*) num from hainiuetltopics10_orc group by top) t    sort by t.num desc limit 10;" >    /home/suyuan09/etl/hainiu/orc2mysql/mytopics10${yymmdd}
/usr/local/hive/bin/hive  -e "use suyuan09;select t.top,t.num from(select top,count(*) num from hainiuetlcategories10_orc  group by top) t  sort by t.num desc limit 10;" >   /home/suyuan09/etl/hainiu/orc2mysql/mycategories10${yymmdd}
/usr/local/hive/bin/hive  -e "use suyuan09;select t.uip,t.num from(select uip,count(*) num from hainiuetlip_orc  group by uip) t  sort by t.num desc;" >   /home/suyuan09/etl/hainiu/orc2mysql/myip${yymmdd}
/usr/local/hive/bin/hive  -e "use suyuan09;select t.usagent,t.num from(select usagent,count(*) num  from hainiuetlspider_orc  group by usagent) t   sort by t.num desc;" >  /home/suyuan09/etl/hainiu/orc2mysql/myspider${yymmdd}


#data->mysql脚本
#data2mysql.sh

#mysql -h 172.33.101.123 -P 3306 -u tony -pYourPassword -D YourDbName <<EOF
/bin/mysql -h192.168.88.195 -p3306 -uhainiu -p12345678 -Dhainiutest <<EOF

LOAD DATA LOCAL INFILE "/home/suyuan09/etl/hainiu/orc2mysql/mytopics10${yymmdd}" INTO TABLE suyuan09_etl_orctopics10mysql FIELDS TERMINATED BY ‘\t‘;
LOAD DATA LOCAL INFILE "/home/suyuan09/etl/hainiu/orc2mysql/mycategories10${yymmdd}" INTO TABLE suyuan09_etl_orccategories10mysql FIELDS TERMINATED BY ‘\t‘;
LOAD DATA LOCAL INFILE "/home/suyuan09/etl/hainiu/orc2mysql/myindex5${yymmdd}" INTO TABLE suyuan09_etl_orcindex5mysql FIELDS TERMINATED BY ‘\t‘;
LOAD DATA LOCAL INFILE "/home/suyuan09/etl/hainiu/orc2mysql/myspider${yymmdd}" INTO TABLE suyuan09_etl_orcspidermysql FIELDS TERMINATED BY ‘\t‘;
LOAD DATA LOCAL INFILE "/home/suyuan09/etl/hainiu/orc2mysql/myip${yymmdd}" INTO TABLE suyuan09_etl_orcipmysql FIELDS TERMINATED BY ‘\t‘;

EOF

报表展示

技术分享图片

技术分享图片

技术分享图片

技术分享图片

技术分享图片

 

其中 mysql没有自带排序函数,自己写一个

技术分享图片

技术分享图片

 

热力图参考之前我之前的博客 https://www.cnblogs.com/symkmk123/p/9309322.html

图表也参考我之前的博客 https://www.cnblogs.com/symkmk123/p/9010514.html

 

ETL项目2:大数据清洗,处理:使用MapReduce进行离线数据分析并报表显示完整项目

标签：password   day   执行   技术   hba   tls   match   apache   mys   

原文地址：https://www.cnblogs.com/symkmk123/p/10197633.html
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