scala实例——二

import scala.util.control.Breaks._import org.apache.spark.sql._import org.apache.spark.SparkConfimport org.apache.spark.SparkContextimport org.apache.spark.sql.{DataFrame, SQLContext, SparkSession}import org.apache.spark.rdd.RDDimport org.apache.spark.sql.execution.vectorized.ColumnarBatch.Rowimport scala.util.Randomimport scala.util.control.Breaksobject simulation_data_flow_analyse {  def main(args: Array[String]) {    val spark = SparkSession      .builder()      .appName("flow_analyse")      .config("spark.some.config.option", "some-value")      .getOrCreate()    import spark.implicits._    val df = spark.read.json("/spark_data/visit_records.json")    df.createOrReplaceTempView("visit")    // Global temporary view is tied to a system preserved database `global_temp`    var result_string = ""    var sql = "SELECT  in_time from visit  order by `in_time`  limit 1"    var min_time = (spark.sql(sql).collect()) (0).asInstanceOf[Row].getInt(0)    sql = "SELECT  in_time from visit  order by `in_time` desc  limit 1 "    var max_time = (spark.sql(sql).collect()) (0).asInstanceOf[Row].getInt(0)    var now_time = 0;    var outer = new Breaks    now_time = min_time;    while (now_time <= max_time) {     outer.breakable {       var come_num = 0;       var time1 = now_time;       var time2 = now_time + 60;       var sql2 = "SELECT  count(*) num from visit   where `in_time` >= " + time1 + " and `in_time` <= " + time2 + ""; //SQL语句       come_num = (spark.sql(sql2).collect()) (0).asInstanceOf[Row].getInt(0)       if (come_num == 0) {         now_time = now_time + 60;         outer.break;       }       var flow_num = come_num;       var time = time1;       var  rand = new Random;       var i = rand.nextInt(7)+4;       flow_num = flow_num * i;       //每一条 flow 结果 添加到 结果集       var vist_string = """{"time":"""+time +","+""""num":"""+flow_num+"}\n"       result_string = result_string + vist_string       now_time = now_time + 60;     }    }    //将结果集 存入 文件    import java.io._    val writer = new PrintWriter(new File("\\sparkdata\\people_flow.json" ))    writer.write(result_string)    writer.close()  }