Spark RDD case: Statistics of daily new users

One 、 Put forward a task

	 The following users have access to historical data , The first column is the date when the user visited the website , The second column is the user name ：

2022-02-07,Mike
2022-02-07,Alice
2022-02-27,Brown
2022-04-02,Mike
2022-04-02,Alice
2022-04-02,Green
2022-07-12,Alice
2022-07-12,Smith
2022-07-12,Brian

Count the number of new users every day through the above data , The following statistical results are obtained ：

2022-02-07 Added 3 Users （ Respectively mike、alice、brown）

2022-04-02 Added 1 Users （green）

2022-07-12 Added 2 Users （ Respectively smith、brian）

Two 、 Realize the idea

	 Use inverted indexing , If the same user corresponds to multiple access dates , The minimum date is the registration date of the user , I.e. new date , Other dates are repeat visit dates , Should not be counted . Therefore, each user should only calculate the minimum date of user access . As shown in the figure below , Move the minimum date each user accesses to the first column , The first column is valid data , Only the occurrence times of each date in the first column are counted , That is, the number of new users on the corresponding date .

• Start the cluster HDFS And Spark
  
• stay HDFS Prepare data on - users.txt
  

3、 ... and 、 To complete the task

（ One ） Read the file , obtain RDD

val rdd1 = sc.textFile("hdfs://master:9000/input/users.txt")

（ Two ） inverted , swap RDD The element order of the middle element group

val rdd2 = rdd1.map(
   line => {
    
     val fields = line.split(",")
      (fields(1), fields(0))
   }
)

（ 3、 ... and ） Inverted RDD Key grouping

val rdd3 = rdd2.groupByKey()

（ Four ） Get the minimum value of the grouped date set , Count as 1

val rdd4 = rdd3.map(line => (line._2.min, 1))

（ 5、 ... and ） Key count , Get the number of new users per day

val result = rdd4.countByKey()

result.keys.foreach(key => println(key + "," + result(key)))

Four 、 To complete the task

（ One ） newly build Maven project – CountNewUsers

take JAVA Directory changed to scala

（ Two ） Add related dependencies and build plug-ins

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>net.cb.rdd</groupId>
    <artifactId>CountNewUsers</artifactId>
    <version>1.0-SNAPSHOT</version>
    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>2.11.12</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_2.11</artifactId>
            <version>2.1.1</version>
        </dependency>
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>2.1.1</version>
        </dependency>
    </dependencies>
    <build>
        <sourceDirectory>src/main/scala</sourceDirectory>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-assembly-plugin</artifactId>
                <version>3.3.0</version>
                <configuration>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-assembly</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
            <plugin>
                <groupId>net.alchim31.maven</groupId>
                <artifactId>scala-maven-plugin</artifactId>
                <version>3.3.2</version>
                <executions>
                    <execution>
                        <id>scala-compile-first</id>
                        <phase>process-resources</phase>
                        <goals>
                            <goal>add-source</goal>
                            <goal>compile</goal>
                        </goals>
                    </execution>
                    <execution>
                        <id>scala-test-compile</id>
                        <phase>process-test-resources</phase>
                        <goals>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

   
</project>

• And refresh the download dependency
  

（ 3、 ... and ） Create log properties file

• Create a log attribute file in the resource folder - log4j.properties

log4j.rootLogger=ERROR, stdout, logfile
log4j.appender.stdout=org.apache.log4j.ConsoleAppender
log4j.appender.stdout.layout=org.apache.log4j.PatternLayout
log4j.appender.stdout.layout.ConversionPattern=%d %p [%c] - %m%n
log4j.appender.logfile=org.apache.log4j.FileAppender
log4j.appender.logfile.File=target/spark.log
log4j.appender.logfile.layout=org.apache.log4j.PatternLayout
log4j.appender.logfile.layout.ConversionPattern=%d %p [%c] - %m%n

（ Four ） Create a single instance object for calculating the average score

• stay net.cb.sql Create in package CountNewUsersBySQL Singleton object
  

package net.cb.rdd


import org.apache.spark.{
    SparkConf, SparkContext}

/** *  function :  Statistics of daily new users  *  author :  Chen Biao  *  Time : 2022 year 06 month 18 Japan  */
object CountNewUsersBySQL {
    
  def main(args: Array[String]): Unit = {
    
    //  establish Spark conf Containers 
    val spark: SparkConf = new SparkConf()
      .setAppName("CalculusAverageByRDD")
      .setMaster("local[*]")
    val sc: SparkContext = new SparkContext(spark)
    val lines = sc.textFile("hdfs://master:9000/input/users.txt")
    //lines.collect.foreach(println)
    // swap RDD The order of elements in 
    val value = lines.map(
      line =>{
    
        val fields = line.split(",")
        val name = fields(1)
        val time = fields(0)
        (name,time)
      }
    )
    //  Group names 
    val nameGB = value.groupByKey().map(
      line => {
    
        val time = line._2.min
        val name = line._1
        (time,name)
      }
    )
    //  Key count , Get daily additions 
    val CB = nameGB.countByKey()
    CB.keys.foreach(key => println(key + "," + CB(key)))
    //  Closed container 
    sc.stop()
  }
}

（ 5、 ... and ） Local running program , View results