Spark: get the access volume of each time period in the log (entry level - simple implementation)

Take one hour as the time period to obtain the access volume of each time period in the log , The results are printed on the console .

Here is the code , Find the log file yourself .

import java.text.SimpleDateFormat
import java.util.Date
import org.apache.spark.{SparkConf, SparkContext}

object RDD_Operator_Transform_groupBy_Test {
  def main(args: Array[String]): Unit = {

    //TODO  Create an environment 
    val sparkConf = new SparkConf().setMaster("local[*]").setAppName("RDD")
    val sc = new SparkContext(sparkConf)

    //TODO RDD operator ——groupBy
    println(" Get the access volume of each time period in the log ")
    val rdd = sc.textFile("datas/apache.log")// File path 

    val timeRDD = rdd.map(
      line => {
        val datas = line.split(" ")   // Separate... With spaces 
        val time = datas(3)     // Take the third place 
        val sdf = new SimpleDateFormat("DD/MM/YYYY:hh:mm:ss")
        val date: Date = sdf.parse(time)    // Parsing time 
        val sdf1 = new SimpleDateFormat("hh")   // The time period is hours 
        val hour: String = sdf1.format(date)    // Incoming time 
        (hour, 1)
      }
    ).groupBy(_._1)

    timeRDD.map{
      case (hour, iter) => {    // Pattern matching 
        (hour, iter.size)
      }
    }.collect().foreach(println)

    //TODO  Shut down the environment 
    sc.stop()
  }

}