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
1 package com.bw.test; 2 3 import java.util.ArrayList; 4 import java.util.Arrays; 5 import java.util.List; 6 import java.util.Random; 7 import java.util.UUID; 8 9 import com.bw.util.DateUtils; 10 import com.bw.util.StringUtils; 11 import org.apache.spark.api.java.JavaRDD; 12 import org.apache.spark.api.java.JavaSparkContext; 13 import org.apache.spark.sql.DataFrame; 14 import org.apache.spark.sql.Row; 15 import org.apache.spark.sql.RowFactory; 16 import org.apache.spark.sql.SQLContext; 17 import org.apache.spark.sql.types.DataTypes; 18 import org.apache.spark.sql.types.StructType; 19 20 21 /** 22 * 模拟数据程序 23 * @author Administrator 24 * 25 */ 26 public class MockData { 27 28 /** 29 * 模拟数据 30 * @param sc 31 * @param sqlContext 32 */ 33 public static void mock(JavaSparkContext sc, 34 SQLContext sqlContext) { 35 List<Row> rows = new ArrayList<Row>(); 36 37 String[] searchKeywords = new String[] {"火锅", "蛋糕", "重庆辣子鸡", "重庆小面", 38 "呷哺呷哺", "新辣道鱼火锅", "国贸大厦", "太古商场", "日本料理", "温泉"}; 39 String date = DateUtils.getTodayDate(); 40 String[] actions = new String[]{"search", "click", "order", "pay"}; 41 Random random = new Random(); 42 43 for(int i = 0; i < 100; i++) { 44 //生产100个userID 45 long userid = random.nextInt(100); 46 47 for(int j = 0; j < 10; j++) { 48 //每个userID有10个sessionID 49 String sessionid = UUID.randomUUID().toString().replace("-", ""); 50 String baseActionTime = date + " " + random.nextInt(23); 51 52 Long clickCategoryId = null; 53 //每个sessionID可能会做0-100之间的action操作 54 for(int k = 0; k < random.nextInt(100); k++) { 55 long pageid = random.nextInt(10); 56 String actionTime = baseActionTime + ":" + StringUtils.fulfuill(String.valueOf(random.nextInt(59))) + ":" + StringUtils.fulfuill(String.valueOf(random.nextInt(59))); 57 String searchKeyword = null; 58 Long clickProductId = null; 59 String orderCategoryIds = null; 60 String orderProductIds = null; 61 String payCategoryIds = null; 62 String payProductIds = null; 63 64 String action = actions[random.nextInt(4)]; 65 if("search".equals(action)) { 66 searchKeyword = searchKeywords[random.nextInt(10)]; 67 } else if("click".equals(action)) { 68 if(clickCategoryId == null) { 69 clickCategoryId = Long.valueOf(String.valueOf(random.nextInt(100))); 70 } 71 clickProductId = Long.valueOf(String.valueOf(random.nextInt(100))); 72 } else if("order".equals(action)) { 73 orderCategoryIds = String.valueOf(random.nextInt(100)); 74 orderProductIds = String.valueOf(random.nextInt(100)); 75 } else if("pay".equals(action)) { 76 payCategoryIds = String.valueOf(random.nextInt(100)); 77 payProductIds = String.valueOf(random.nextInt(100)); 78 } 79 80 Row row = RowFactory.create(date, userid, sessionid, 81 pageid, actionTime, searchKeyword, 82 clickCategoryId, clickProductId, 83 orderCategoryIds, orderProductIds, 84 payCategoryIds, payProductIds, 85 Long.valueOf(String.valueOf(random.nextInt(10)))); 86 rows.add(row); 87 } 88 } 89 } 90 91 JavaRDD<Row> rowsRDD = sc.parallelize(rows); 92 93 StructType schema = DataTypes.createStructType(Arrays.asList( 94 DataTypes.createStructField("date", DataTypes.StringType, true), 95 DataTypes.createStructField("user_id", DataTypes.LongType, true), 96 DataTypes.createStructField("session_id", DataTypes.StringType, true), 97 DataTypes.createStructField("page_id", DataTypes.LongType, true), 98 DataTypes.createStructField("action_time", DataTypes.StringType, true), 99 DataTypes.createStructField("search_keyword", DataTypes.StringType, true), 100 DataTypes.createStructField("click_category_id", DataTypes.LongType, true), 101 DataTypes.createStructField("click_product_id", DataTypes.LongType, true), 102 DataTypes.createStructField("order_category_ids", DataTypes.StringType, true), 103 DataTypes.createStructField("order_product_ids", DataTypes.StringType, true), 104 DataTypes.createStructField("pay_category_ids", DataTypes.StringType, true), 105 DataTypes.createStructField("pay_product_ids", DataTypes.StringType, true), 106 DataTypes.createStructField("city_id", DataTypes.LongType, true))); 107 108 DataFrame df = sqlContext.createDataFrame(rowsRDD, schema); 109 110 df.registerTempTable("user_visit_action"); 111 for(Row _row : df.take(1)) { 112 System.out.println(_row); 113 } 114 115 /** 116 * ================================================================== 117 */ 118 119 rows.clear(); 120 String[] sexes = new String[]{"male", "female"}; 121 for(int i = 0; i < 100; i ++) { 122 long userid = i; 123 String username = "user" + i; 124 String name = "name" + i; 125 int age = random.nextInt(60); 126 String professional = "professional" + random.nextInt(100); 127 String city = "city" + random.nextInt(100); 128 String sex = sexes[random.nextInt(2)]; 129 130 Row row = RowFactory.create(userid, username, name, age, 131 professional, city, sex); 132 rows.add(row); 133 } 134 135 rowsRDD = sc.parallelize(rows); 136 137 StructType schema2 = DataTypes.createStructType(Arrays.asList( 138 DataTypes.createStructField("user_id", DataTypes.LongType, true), 139 DataTypes.createStructField("username", DataTypes.StringType, true), 140 DataTypes.createStructField("name", DataTypes.StringType, true), 141 DataTypes.createStructField("age", DataTypes.IntegerType, true), 142 DataTypes.createStructField("professional", DataTypes.StringType, true), 143 DataTypes.createStructField("city", DataTypes.StringType, true), 144 DataTypes.createStructField("sex", DataTypes.StringType, true))); 145 146 DataFrame df2 = sqlContext.createDataFrame(rowsRDD, schema2); 147 for(Row _row : df2.take(1)) { 148 System.out.println(_row); 149 } 150 151 df2.registerTempTable("user_info"); 152 153 /** 154 * ================================================================== 155 */ 156 rows.clear(); 157 158 int[] productStatus = new int[]{0, 1}; 159 160 for(int i = 0; i < 100; i ++) { 161 long productId = i; 162 String productName = "product" + i; 163 String extendInfo = "{\"product_status\": " + productStatus[random.nextInt(2)] + "}"; 164 165 Row row = RowFactory.create(productId, productName, extendInfo); 166 rows.add(row); 167 } 168 169 rowsRDD = sc.parallelize(rows); 170 171 StructType schema3 = DataTypes.createStructType(Arrays.asList( 172 DataTypes.createStructField("product_id", DataTypes.LongType, true), 173 DataTypes.createStructField("product_name", DataTypes.StringType, true), 174 DataTypes.createStructField("extend_info", DataTypes.StringType, true))); 175 176 DataFrame df3 = sqlContext.createDataFrame(rowsRDD, schema3); 177 for(Row _row : df3.take(1)) { 178 System.out.println(_row); 179 } 180 181 df3.registerTempTable("product_info"); 182 } 183 184 }
二、构建Spark上下文
1 import com.bw.conf.ConfigurationManager; 2 import com.bw.constant.Constants; 3 import com.bw.test.MockData; 4 import org.apache.spark.SparkConf; 5 import org.apache.spark.api.java.JavaSparkContext; 6 import org.apache.spark.sql.SQLContext; 7 8 9 /** 10 * 用户访问session分析Spark作业 11 * 12 * */ 13 public class UserVisitSessionAnalyzeSpark { 14 15 public static void main(String[] args) { 16 //构建Spark上下文 17 SparkConf sparkConf = new SparkConf(); 18 //Spark作业本地运行 19 sparkConf.setMaster("local"); 20 //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 21 sparkConf.setAppName(Constants.SPARK_APP_NAME_SESSION); 22 23 JavaSparkContext jsc = new JavaSparkContext(sparkConf); 24 SQLContext sqlContext = new SQLContext(jsc); 25 26 mockData(jsc,sqlContext); 27 jsc.stop(); 28 } 29 30 31 /** 32 * 生成模拟数据（只有本地模式，才会去生成模拟数据） 33 * @param sc 34 * @param sqlContext 35 */ 36 private static void mockData(JavaSparkContext sc, SQLContext sqlContext) { 37 boolean local = ConfigurationManager.getBoolean(Constants.SPARK_LOCAL); 38 if(local) { 39 MockData.mock(sc, sqlContext); 40 } 41 } 42 }
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}]
