大数据项目实战（某购物平台商品实时推荐系统）

一、使用hive、load、 hdfs上清洗的数据。

1.动态添加表分区
  $hive> alter table eshop.logs add partition(year=2018,month=11,day=25,hour=12,minute=51);

 2.load数据到表中。
 	 $hive> load data inpath '/data/eshop/cleaned/2018/11/25/12/58' into table eshop.logs  	partition(year=2018,month=11,day=25,hour=12,minute=51);

3.查询topN
$hive> select * from logs ;
//倒排序topN
$hive> select request,count(*) as c from logs where year = 2018 and month = 11 and day = 25 group by request order by c desc ;

4.创建统计结果表
$hive> create table stats(request string, c int) row format DELIMITED FIELDS TERMINATED BY ‘,’ LINES TERMINATED BY ‘\n’ STORED AS TEXTFILE;
$hive> insert into stats select request,count(*) as c from logs where year = 2018 and month = 11 and day = 25 group by request order by c desc ;

5.Mysql中创建表
mysql> create table stats (id int primary key auto_increment,request varchar(200), c int);

6.使用sqoop将hive中的数据导出到mysql
$>sqoop export --connect jdbc:mysql://192.168.43.1:3306/eshop --driver com.mysql.jdbc.Driver --username mysql --password mysql --table stats --columns request,c --export-dir hdfs://s100/user/hive/warehouse/eshop.db/stats

7.将以上2-5部写成脚本,使用cron进行调度.
a.描述
每天的凌晨2点整,统计昨天的日志。

 b.创建bash脚本
     1.创建准备脚本 -- 动态创建hivesql脚本文件[stat.ql]。
        [/usr/local/bin/prestats.sh]
        #!/bin/bash
        y=`date +%Y`
        m=`date +%m`
        d=`date -d "-0 day" +%d`

        m=$(( m+0 ))
        d=$(( d+0 ))
        # 删除之前的hql文件
        rm -rf stat.ql

        #添加分区
        echo "alter table eshop.logs add if not exists partition(year=${y},month=${m},day=${d},hour=9,minute=28);" >> stat.ql

        #加载数据放到分区
        echo "load data inpath 'hdfs://s201/user/centos/eshop/cleaned/${y}/${m}/${d}/9/28' into table eshop.logs  partition(year=${y},month=${m},day=${d},hour=9,minute=28);" >> stat.ql

        #统计数据，并将结果插入到stats表
        echo "insert into eshop.stats select request,count(*) as c from eshop.logs where year = ${y} and month = ${m} and day = ${d} and hour=9 and minute = 28 group by request order by c desc ;" >> stat.ql

     2.创建执行脚本
        [/usr/local/bin/exestats.sh]
        #!/bin/bash
        # 创建hive脚本文件
        ./prestats.sh

        #执行hive的ql脚本
        hive -f stat.ql

        #执行sqoop导出到mysql
        sqoop export --connect jdbc:mysql://192.168.43.1:3306/eshop  --username mysql --password mysql --table stats --columns request,c --export-dir /user/hive/warehouse/eshop.db/stats
        #sqoop export --connect jdbc:mysql://192.168.43.1:3306/eshop  --username mysql --password mysql --table stats --export-dir /user/hive/warehouse/eshop.db/stats

     3.修改所有权限
        $>sudo chmod a+x /usr/local/bin/prestats.sh
        $>sudo chmod a+x /usr/local/bin/exestats.sh

7.编写java客户端进行以上步骤
    a.在hive主机上启动hiveserver2
        $> hiveserver2 &

    b.编写java客户端通过jdbc访问hive数据
        1)新建HiveClient模块，添加maven支持
            
            
                4.0.0

                com.test
                HiveClient
                1.0-SNAPSHOT

                

                    
                        org.apache.hive
                        hive-jdbc
                        2.1.0
                    

                    
                        mysql
                        mysql-connector-java
                        5.1.17
                    

                


            
        2)编写类进行查询和插入StatDao.java
            package com.test.hiveclient;

            import org.apache.hadoop.hbase.client.Result;

            import java.sql.*;
            import java.util.HashMap;
            import java.util.Map;

            public class StatDao {

                private static Map map = new HashMap();

                public static void main(String [] args)
                {
                    try {
                        Class.forName("org.apache.hive.jdbc.HiveDriver");
                        //建立连接
                        Connection conn = DriverManager.getConnection("jdbc:hive2://192.168.43.131:10000/eshop","","");
                        System.out.println(conn);
                        PreparedStatement ppst = conn.prepareStatement("select * from stats");
                        ResultSet set = ppst.executeQuery();
                        while (set.next()) {
                            map.put(set.getString(1), set.getInt(2));
                            System.out.print(set.getString(1) + " : ");
                            System.out.print(set.getInt(2));
                            System.out.println();
                        }


                        getMysqlConn();


                    } catch (Exception e) {
                        e.printStackTrace();
                    }
                }


                public static void getMysqlConn()
                {
                    try {
                        //加载类(加载驱动程序)
                        Class.forName("com.mysql.jdbc.Driver");
                        //数据库连接url
                        String url = "jdbc:mysql://192.168.43.1:3306/eshop" ;
                        //username
                        String user = "mysql";
                        //password
                        String pass = "mysql" ;

                        //得到连接
                        Connection conn = DriverManager.getConnection(url, user, pass);
                        //创建语句对象
                        Statement st = conn.createStatement();
                        for(String key : map.keySet() )
                        {
                            PreparedStatement pt =  conn.prepareStatement("insert into stats (request,count) values(?,?)");
                            pt.setString(1, key);
                            pt.setInt(2, map.get(key));
                            pt.executeUpdate();
                        }

                    } catch (Exception e) {
                        e.printStackTrace();
                    }
                }
            }

二、JFreeChart生成统计图表

1.pom.xml
  
      jfree
      jfreechart
      1.0.13
  

2.使用JFreechart生成图片
    package com.test.eshop.test;

    import org.jfree.chart.ChartFactory;
    import org.jfree.chart.ChartUtilities;
    import org.jfree.chart.JFreeChart;
    import org.jfree.chart.plot.PiePlot;
    import org.jfree.chart.plot.PiePlot3D;
    import org.jfree.data.general.DefaultPieDataset;
    import org.jfree.data.general.PieDataset;
    import org.junit.Test;

    import java.awt.*;
    import java.io.File;
    import java.io.IOException;

    /**
     * 测试饼图
     */
    public class TestJfreechart {

        @Test
        public void pie() throws Exception {
            File f = new File("d:/pie.png");

            //数据集
            DefaultPieDataset ds = new DefaultPieDataset();
            ds.setValue("HuaWei",3000);
            ds.setValue("Apple",5000);
            ds.setValue("Mi",1890);

            JFreeChart chart = ChartFactory.createPieChart("饼图演示", ds, false, false, false);

            Font font = new Font("宋体",Font.BOLD,15);
            chart.getTitle().setFont(font);
            //背景透明

            ((PiePlot)chart.getPlot()).setForegroundAlpha(0.2f);
            ((PiePlot)chart.getPlot()).setExplodePercent("Apple",0.1f);
            ((PiePlot)chart.getPlot()).setExplodePercent("HuaWei",0.2f);
            ((PiePlot)chart.getPlot()).setExplodePercent("Mi",0.3f);


            //创建3D饼图
            ChartUtilities.saveChartAsJPEG(f, chart,400,300);
        }
    }

三、引入Spark推荐系统

1.设计用户商品表 -- Mysql
    create table useritems(id int primary key auto_increment ,userid int, itemid int, score int , time timestamp);

2.添加映射文件UserItem.hbm.xml

3.Dao + Service

4.controller

5.spark部分
  a)通过sqoop到处mysql数据到hdfs
     $> sqoop import --connect jdbc:mysql://192.168.43.1:3306/eshop --driver com.mysql.jdbc.Driver --username mysql --password mysql --table useritems --columns userid,itemid,score -m 2 --target-dir /data/eshop/recommends --check-column id --incremental append --last-value 0

  b)启动spark集群

  c)启动spark-shell
     $> spark-shell --master spark://s100:7077

     #内置SparkSession--spark
     $scala>
        import org.apache.spark.ml.evaluation.RegressionEvaluator
        import org.apache.spark.ml.recommendation.ALS
        import spark.implicits._

        case class UserItem(userId: Int, itemId: Int, score : Int);

        def parseRating(str: String): UserItem = {
        val fields = str.split(",")
        UserItem(fields(0).toInt, fields(1).toInt, fields(2).toInt)
        }

        val useritems = spark.read.textFile("hdfs://s100/data/eshop/recommends").map(parseRating).toDF()

        //val test = spark.read.textFile("hdfs://s100/data/eshop/testdata.txt").map(parseRating).toDF()
        val Array(training, test) = useritems.randomSplit(Array(0.8, 0.2))

        val als = new ALS()
        .setMaxIter(5)
        .setRegParam(0.01)
        .setUserCol("userId")
        .setItemCol("itemId")
        .setRatingCol("score")
        val model = als.fit(training)

        val predictions = model.transform(test)

        val evaluator = new RegressionEvaluator().setMetricName("rmse").setLabelCol("score").setPredictionCol("prediction")
        val rmse = evaluator.evaluate(predictions)
        println(s"Root-mean-square error = $rmse")


        //保存ALS模型
        model.save("hdfs://s100/data/eshop/rec/model");
        spark.stop()


        //加载模型
        import org.apache.spark.ml.recommendation.ALSModel;
        val model = ALSModel.load("hdfs://s201/user/centos/eshop/rec/model");
        val predictions = model.transform(test)

---

作者：葛红富
来源：CSDN
原文：https://blog.csdn.net/xcvbxv01/article/details/84503060
版权声明：本文为博主原创文章，转载请附上博文链接！