将统计结果写入到MySQL数据库中&黑名单过滤
1、将统计结果写入到MySQL数据库中
（1）先在本地mysql数据库，并对其创建
//创建数据库名为：imooc_spark
mysql> create database imooc_spark;
Query OK, 1 row affected (0.00 sec)
//使用数据库
mysql> use imooc_spark;
Database changed
//创建数据表
mysql> create table wordcount(word varchar(50) default null,wordcount int(10)default null);
Query OK, 0 rows affected (0.00 sec)
//查看数据表
mysql> show tables;
+-----------------------+
| Tables_in_imooc_spark |
+-----------------------+
| wordcount |
+-----------------------+
1 row in set (0.00 sec)
（2）代码编写
pom文件依赖：
<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>${scala.version}</version>
</dependency>
<!-- Kafka 依赖-->
<!--
<dependency>
<groupId>org.apache.kafka</groupId>
<artifactId>kafka_2.11</artifactId>
<version>${kafka.version}</version>
</dependency>
-->
<!-- Hadoop 依赖-->
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>${hadoop.version}</version>
</dependency>
<!-- HBase 依赖-->
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-client</artifactId>
<version>${hbase.version}</version>
</dependency>
<dependency>
<groupId>org.apache.hbase</groupId>
<artifactId>hbase-server</artifactId>
<version>${hbase.version}</version>
</dependency>
<!-- Spark Streaming 依赖-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- Spark Streaming整合Flume 依赖-->
<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>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.commons</groupId>
<artifactId>commons-lang3</artifactId>
<version>3.5</version>
</dependency>
<!-- Spark SQL 依赖-->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${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完成词频统计，并将结果写入到MySQL数据库中
*/
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()
//将结果写入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()
}
/**
* 获取MySQL的连接
*/
def createConnection() = {
Class.forName("com.mysql.jdbc.Driver")
DriverManager.getConnection("jdbc:mysql://localhost:3306/imooc_spark", "root", "123456")
}
}
通过该sql将统计结果写入到MySql
"insert into wordcount(word, wordcount) values('" + record._1 + "'," + record._2 + ")"
存在问题：
-
对于已有数据做更新，而是所有的数据均为insert
改进思路：a、在插入数据前先判断单词是否存在，如果存在就uptate，不存在则insert b、工作中：HBase/Redis -
每个RDD的partition创建connection，建议大家改成连接池
2、黑名单过滤
package com.imooc.spark
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}
/**
* 黑名单过滤
*/
object TransformApp {
def main(args: Array[String]): Unit = {
val sparkConf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount")
/**
* 创建StreamingContext需要两个参数：SparkConf和batch interval
*/
val ssc = new StreamingContext(sparkConf, Seconds(5))
/**
* 构建黑名单
*/
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()
}
}