Spark项目之电商用户行为分析大数据平台之（十二）Spark上下文构建及模拟数据生成

一、模拟生成数据

package com.bw.test;

import java.util.ArrayList;
import java.util.Arrays;
import java.util.List;
import java.util.Random;
import java.util.UUID;

import com.bw.util.DateUtils;
import com.bw.util.StringUtils;
import org.apache.spark.api.java.JavaRDD;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.DataFrame;
import org.apache.spark.sql.Row;
import org.apache.spark.sql.RowFactory;
import org.apache.spark.sql.SQLContext;
import org.apache.spark.sql.types.DataTypes;
import org.apache.spark.sql.types.StructType;


/**
 * 模拟数据程序
 * @author Administrator
 *
 */
public class MockData {

    /**
     * 模拟数据
     * @param sc
     * @param sqlContext
     */
    public static void mock(JavaSparkContext sc,
            SQLContext sqlContext) {
        List<Row> rows = new ArrayList<Row>();
        
        String[] searchKeywords = new String[] {"火锅", "蛋糕", "重庆辣子鸡", "重庆小面",
                "呷哺呷哺", "新辣道鱼火锅", "国贸大厦", "太古商场", "日本料理", "温泉"};
        String date = DateUtils.getTodayDate();
        String[] actions = new String[]{"search", "click", "order", "pay"};
        Random random = new Random();
        
        for(int i = 0; i < 100; i++) {
            //生产100个userID
            long userid = random.nextInt(100);    
            
            for(int j = 0; j < 10; j++) {
                //每个userID有10个sessionID
                String sessionid = UUID.randomUUID().toString().replace("-", "");  
                String baseActionTime = date + " " + random.nextInt(23);
                
                Long clickCategoryId = null;
                //每个sessionID可能会做0-100之间的action操作
                for(int k = 0; k < random.nextInt(100); k++) {
                    long pageid = random.nextInt(10);    
                    String actionTime = baseActionTime + ":" + StringUtils.fulfuill(String.valueOf(random.nextInt(59))) + ":" + StringUtils.fulfuill(String.valueOf(random.nextInt(59)));
                    String searchKeyword = null;
                    Long clickProductId = null;
                    String orderCategoryIds = null;
                    String orderProductIds = null;
                    String payCategoryIds = null;
                    String payProductIds = null;
                    
                    String action = actions[random.nextInt(4)];
                    if("search".equals(action)) {
                        searchKeyword = searchKeywords[random.nextInt(10)];   
                    } else if("click".equals(action)) {
                        if(clickCategoryId == null) {
                            clickCategoryId = Long.valueOf(String.valueOf(random.nextInt(100)));    
                        }
                        clickProductId = Long.valueOf(String.valueOf(random.nextInt(100)));  
                    } else if("order".equals(action)) {
                        orderCategoryIds = String.valueOf(random.nextInt(100));  
                        orderProductIds = String.valueOf(random.nextInt(100));
                    } else if("pay".equals(action)) {
                        payCategoryIds = String.valueOf(random.nextInt(100));  
                        payProductIds = String.valueOf(random.nextInt(100));
                    }
                    
                    Row row = RowFactory.create(date, userid, sessionid, 
                            pageid, actionTime, searchKeyword,
                            clickCategoryId, clickProductId,
                            orderCategoryIds, orderProductIds,
                            payCategoryIds, payProductIds, 
                            Long.valueOf(String.valueOf(random.nextInt(10))));    
                    rows.add(row);
                }
            }
        }
        
        JavaRDD<Row> rowsRDD = sc.parallelize(rows);
        
        StructType schema = DataTypes.createStructType(Arrays.asList(
                DataTypes.createStructField("date", DataTypes.StringType, true),
                DataTypes.createStructField("user_id", DataTypes.LongType, true),
                DataTypes.createStructField("session_id", DataTypes.StringType, true),
                DataTypes.createStructField("page_id", DataTypes.LongType, true),
                DataTypes.createStructField("action_time", DataTypes.StringType, true),
                DataTypes.createStructField("search_keyword", DataTypes.StringType, true),
                DataTypes.createStructField("click_category_id", DataTypes.LongType, true),
                DataTypes.createStructField("click_product_id", DataTypes.LongType, true),
                DataTypes.createStructField("order_category_ids", DataTypes.StringType, true),
                DataTypes.createStructField("order_product_ids", DataTypes.StringType, true),
                DataTypes.createStructField("pay_category_ids", DataTypes.StringType, true),
                DataTypes.createStructField("pay_product_ids", DataTypes.StringType, true),
                DataTypes.createStructField("city_id", DataTypes.LongType, true)));

        DataFrame df = sqlContext.createDataFrame(rowsRDD, schema);

        df.registerTempTable("user_visit_action");  
        for(Row _row : df.take(1)) {
            System.out.println(_row);  
        }
        
        /**
         * ==================================================================
         */
        
        rows.clear();
        String[] sexes = new String[]{"male", "female"};
        for(int i = 0; i < 100; i ++) {
            long userid = i;
            String username = "user" + i;
            String name = "name" + i;
            int age = random.nextInt(60);
            String professional = "professional" + random.nextInt(100);
            String city = "city" + random.nextInt(100);
            String sex = sexes[random.nextInt(2)];
            
            Row row = RowFactory.create(userid, username, name, age, 
                    professional, city, sex);
            rows.add(row);
        }
        
        rowsRDD = sc.parallelize(rows);
        
        StructType schema2 = DataTypes.createStructType(Arrays.asList(
                DataTypes.createStructField("user_id", DataTypes.LongType, true),
                DataTypes.createStructField("username", DataTypes.StringType, true),
                DataTypes.createStructField("name", DataTypes.StringType, true),
                DataTypes.createStructField("age", DataTypes.IntegerType, true),
                DataTypes.createStructField("professional", DataTypes.StringType, true),
                DataTypes.createStructField("city", DataTypes.StringType, true),
                DataTypes.createStructField("sex", DataTypes.StringType, true)));
        
        DataFrame df2 = sqlContext.createDataFrame(rowsRDD, schema2);
        for(Row _row : df2.take(1)) {
            System.out.println(_row);  
        }
        
        df2.registerTempTable("user_info");  
        
        /**
         * ==================================================================
         */
        rows.clear();
        
        int[] productStatus = new int[]{0, 1};
        
        for(int i = 0; i < 100; i ++) {
            long productId = i;
            String productName = "product" + i;
            String extendInfo = "{"product_status": " + productStatus[random.nextInt(2)] + "}";    
            
            Row row = RowFactory.create(productId, productName, extendInfo);
            rows.add(row);
        }
        
        rowsRDD = sc.parallelize(rows);
        
        StructType schema3 = DataTypes.createStructType(Arrays.asList(
                DataTypes.createStructField("product_id", DataTypes.LongType, true),
                DataTypes.createStructField("product_name", DataTypes.StringType, true),
                DataTypes.createStructField("extend_info", DataTypes.StringType, true)));
        
        DataFrame df3 = sqlContext.createDataFrame(rowsRDD, schema3);
        for(Row _row : df3.take(1)) {
            System.out.println(_row);  
        }
        
        df3.registerTempTable("product_info"); 
    }
    
}

二、构建Spark上下文

import com.bw.conf.ConfigurationManager;
import com.bw.constant.Constants;
import com.bw.test.MockData;
import org.apache.spark.SparkConf;
import org.apache.spark.api.java.JavaSparkContext;
import org.apache.spark.sql.SQLContext;


/**
 * 用户访问session分析Spark作业
 *
 * */
public class UserVisitSessionAnalyzeSpark {

    public static void main(String[] args) {
        //构建Spark上下文
        SparkConf sparkConf = new SparkConf();
        //Spark作业本地运行
        sparkConf.setMaster("local");
        //为了符合大型企业的开发需求，不能出现硬编码，创建一个Constants接口类，定义一些常量
        sparkConf.setAppName(Constants.SPARK_APP_NAME_SESSION);

        JavaSparkContext jsc = new JavaSparkContext(sparkConf);
        SQLContext sqlContext = new SQLContext(jsc);

        mockData(jsc,sqlContext);
        jsc.stop();
    }


    /**
     * 生成模拟数据（只有本地模式，才会去生成模拟数据）
     * @param sc
     * @param sqlContext
     */
    private static void mockData(JavaSparkContext sc, SQLContext sqlContext) {
        boolean local = ConfigurationManager.getBoolean(Constants.SPARK_LOCAL);
        if(local) {
            MockData.mock(sc, sqlContext);
        }
    }
}

三、打印的测试数据

3.1　user_visit_action

用户下的订单

[2018-05-23,34,4ad62c0824194e5687467bb84b9beeb9,3,2018-05-23 18:27:37,null,null,null,null,null,8,64,8]

3.2　user_info

[0,user0,name0,26,professional11,city4,male]

3.3　product_info

[0,product0,{"product_status": 1}]