將統計結果寫入到MySQL數據庫中&黑名單過濾

1、將統計結果寫入到MySQL數據庫中

（1）先在本地mysql數據庫，並對其創建

//創建數據庫名爲：imooc_spark
mysql> create database imooc_spark; 
Query OK, 1 row affected (0.00 sec)

//使用數據庫
mysql> use imooc_spark;
Database changed
//創建數據表
mysql> create table wordcount(word varchar(50) default null,wordcount int(10)default null);
Query OK, 0 rows affected (0.00 sec)

//查看數據表
mysql> show tables;
+-----------------------+
| Tables_in_imooc_spark |
+-----------------------+
| wordcount             |
+-----------------------+
1 row in set (0.00 sec)

（2）代碼編寫
pom文件依賴：

<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/maven-v4_0_0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <groupId>com.imooc.spark</groupId>
    <artifactId>sparktrain</artifactId>
    <version>1.0</version>
    <inceptionYear>2008</inceptionYear>
    <properties>
        <scala.version>2.11.8</scala.version>
        <kafka.version>0.9.0.0</kafka.version>
        <spark.version>2.2.0</spark.version>
        <hadoop.version>2.6.0-cdh5.7.0</hadoop.version>
        <hbase.version>1.2.0-cdh5.7.0</hbase.version>
    </properties>

    <!--添加cloudera的repository-->
    <repositories>
        <repository>
            <id>cloudera</id>
            <url>https://repository.cloudera.com/artifactory/cloudera-repos</url>
        </repository>
    </repositories>

    <dependencies>
        <dependency>
            <groupId>org.scala-lang</groupId>
            <artifactId>scala-library</artifactId>
            <version>${scala.version}</version>
        </dependency>

        <!-- Kafka 依賴-->
        <!--
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.11</artifactId>
            <version>${kafka.version}</version>
        </dependency>
        -->

        <!-- Hadoop 依賴-->
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>${hadoop.version}</version>
        </dependency>

        <!-- HBase 依賴-->
        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-client</artifactId>
            <version>${hbase.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.hbase</groupId>
            <artifactId>hbase-server</artifactId>
            <version>${hbase.version}</version>
        </dependency>

        <!-- Spark Streaming 依賴-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>


        <!-- Spark Streaming整合Flume 依賴-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-flume_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-flume-sink_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka-0-8_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <dependency>
            <groupId>org.apache.commons</groupId>
            <artifactId>commons-lang3</artifactId>
            <version>3.5</version>
        </dependency>

        <!-- Spark SQL 依賴-->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-sql_2.11</artifactId>
            <version>${spark.version}</version>
        </dependency>


        <dependency>
            <groupId>com.fasterxml.jackson.module</groupId>
            <artifactId>jackson-module-scala_2.11</artifactId>
            <version>2.6.5</version>
        </dependency>

        <dependency>
            <groupId>net.jpountz.lz4</groupId>
            <artifactId>lz4</artifactId>
            <version>1.3.0</version>
        </dependency>

        <dependency>
            <groupId>mysql</groupId>
            <artifactId>mysql-connector-java</artifactId>
            <version>5.1.38</version>
        </dependency>

        <dependency>
            <groupId>org.apache.flume.flume-ng-clients</groupId>
            <artifactId>flume-ng-log4jappender</artifactId>
            <version>1.6.0</version>
        </dependency>

    </dependencies>

    <build>
        <!--
        <sourceDirectory>src/main/scala</sourceDirectory>
        <testSourceDirectory>src/test/scala</testSourceDirectory>
        -->
        <plugins>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <executions>
                    <execution>
                        <goals>
                            <goal>compile</goal>
                            <goal>testCompile</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <scalaVersion>${scala.version}</scalaVersion>
                    <args>
                        <arg>-target:jvm-1.5</arg>
                    </args>
                </configuration>
            </plugin>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-eclipse-plugin</artifactId>
                <configuration>
                    <downloadSources>true</downloadSources>
                    <buildcommands>
                        <buildcommand>ch.epfl.lamp.sdt.core.scalabuilder</buildcommand>
                    </buildcommands>
                    <additionalProjectnatures>
                        <projectnature>ch.epfl.lamp.sdt.core.scalanature</projectnature>
                    </additionalProjectnatures>
                    <classpathContainers>
                        <classpathContainer>org.eclipse.jdt.launching.JRE_CONTAINER</classpathContainer>
                        <classpathContainer>ch.epfl.lamp.sdt.launching.SCALA_CONTAINER</classpathContainer>
                    </classpathContainers>
                </configuration>
            </plugin>
        </plugins>
    </build>
    <reporting>
        <plugins>
            <plugin>
                <groupId>org.scala-tools</groupId>
                <artifactId>maven-scala-plugin</artifactId>
                <configuration>
                    <scalaVersion>${scala.version}</scalaVersion>
                </configuration>
            </plugin>
        </plugins>
    </reporting>
</project>

package com.imooc.spark

import java.sql.DriverManager

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
  * 使用Spark Streaming完成詞頻統計，並將結果寫入到MySQL數據庫中
  */
object ForeachRDDApp {

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

    val sparkConf = new SparkConf().setAppName("ForeachRDDApp").setMaster("local[2]")
    val ssc = new StreamingContext(sparkConf, Seconds(5))


    val lines = ssc.socketTextStream("localhost", 6789)

    val result = lines.flatMap(_.split(" ")).map((_, 1)).reduceByKey(_ + _)

  //  result.print()
   
   //將結果寫入MySql
    result.foreachRDD(rdd => {
      rdd.foreachPartition(partitionOfRecords => {
        val connection = createConnection()
        partitionOfRecords.foreach(record => {
          val sql = 
          
          connection.createStatement().execute(sql)
        })

        connection.close()
      })
    })


    ssc.start()
    ssc.awaitTermination()
  }


  /**
    * 獲取MySQL的連接
    */
  def createConnection() = {
    Class.forName("com.mysql.jdbc.Driver")
    DriverManager.getConnection("jdbc:mysql://localhost:3306/imooc_spark", "root", "123456")
  }

}

通過該sql將統計結果寫入到MySql

"insert into wordcount(word, wordcount) values('" + record._1 + "'," + record._2 + ")"

存在問題：

1. 對於已有數據做更新，而是所有的數據均爲insert
   改進思路：

   a、在插入數據前先判斷單詞是否存在，如果存在就uptate，不存在則insert
   b、工作中：HBase/Redis
   

2. 每個RDD的partition創建connection，建議大家改成連接池

---

2、黑名單過濾

package com.imooc.spark

import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}

/**
  * 黑名單過濾
  */
object TransformApp {


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

    val sparkConf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")

    /**
      * 創建StreamingContext需要兩個參數：SparkConf和batch interval
      */
    val ssc = new StreamingContext(sparkConf, Seconds(5))


    /**
      * 構建黑名單
      */
    val blacks = List("zs", "ls")
    val blacksRDD = ssc.sparkContext.parallelize(blacks).map(x => (x, true))

    val lines = ssc.socketTextStream("localhost", 6789)
    val clicklog = lines.map(x => (x.split(",")(1), x)).transform(rdd => {
      rdd.leftOuterJoin(blacksRDD)
        .filter(x=> x._2._2.getOrElse(false) != true)
        .map(x=>x._2._1)
    })

    clicklog.print()

    ssc.start()
    ssc.awaitTermination()
  }
}