【Spark】SparkStreaming从不同基本数据源读取数据
文章目录
基本数据源
文件数据源
注意事项
1.SparkStreaming不支持监控嵌套目录
2.文件进入dataDirectory（受监控的文件夹）需要通过移动或者重命名实现
3.一旦文件移动进目录，则不能再修改，即使修改也不会读取修改后的数据
步骤
一、创建maven工程并导包
<properties>
<scala.version>2.11.8</scala.version>
<spark.version>2.2.0</spark.version>
</properties>
<dependencies>
<dependency>
<groupId>org.scala-lang</groupId>
<artifactId>scala-library</artifactId>
<version>${scala.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-core_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-sql_2.11</artifactId>
<version>${spark.version}</version>
</dependency>
<!-- https://mvnrepository.com/artifact/org.apache.spark/spark-streaming -->
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-streaming_2.11</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>org.apache.hadoop</groupId>
<artifactId>hadoop-client</artifactId>
<version>2.7.5</version>
</dependency>
<dependency>
<groupId>org.apache.spark</groupId>
<artifactId>spark-hive_2.11</artifactId>
<version>2.2.0</version>
</dependency>
<dependency>
<groupId>mysql</groupId>
<artifactId>mysql-connector-java</artifactId>
<version>5.1.38</version>
</dependency>
</dependencies>
<build>
<sourceDirectory>src/main/scala</sourceDirectory>
<testSourceDirectory>src/test/scala</testSourceDirectory>
<plugins>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-compiler-plugin</artifactId>
<version>3.0</version>
<configuration>
<source>1.8</source>
<target>1.8</target>
<encoding>UTF-8</encoding>
<!-- <verbal>true</verbal>-->
</configuration>
</plugin>
<plugin>
<groupId>net.alchim31.maven</groupId>
<artifactId>scala-maven-plugin</artifactId>
<version>3.2.0</version>
<executions>
<execution>
<goals>
<goal>compile</goal>
<goal>testCompile</goal>
</goals>
<configuration>
<args>
<arg>-dependencyfile</arg>
<arg>${project.build.directory}/.scala_dependencies</arg>
</args>
</configuration>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins</groupId>
<artifactId>maven-shade-plugin</artifactId>
<version>3.1.1</version>
<executions>
<execution>
<phase>package</phase>
<goals>
<goal>shade</goal>
</goals>
<configuration>
<filters>
<filter>
<artifact>*:*</artifact>
<excludes>
<exclude>META-INF/*.SF</exclude>
<exclude>META-INF/*.DSA</exclude>
<exclude>META-INF/*.RSA</exclude>
</excludes>
</filter>
</filters>
<transformers>
<transformer implementation="org.apache.maven.plugins.shade.resource.ManifestResourceTransformer">
<mainClass></mainClass>
</transformer>
</transformers>
</configuration>
</execution>
</executions>
</plugin>
</plugins>
</build>
二、在HDFS创建目录，并上传要做测试的数据
cd /export/servers/
vim wordcount.txt
hello world
abc test
hadoop hive
HDFS上创建目录
hdfs dfs -mkdir /stream_data
hdfs dfs -put wordcount.txt /stream_data
三、开发SparkStreaming代码
package cn.itcast.sparkstreaming.demo1
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
object getHdfsFiles {
// 自定义updateFunc函数
/**
* updateFunc需要两个参数
*
* @param newValues 新输入数据计数累加的值
* @param runningCount 历史数据计数累加完成的值
* @return 返回值是Option
*
* Option是scala中比较特殊的类，是some和none的父类,主要为了解决null值的问题
*/
def updateFunc(newValues: Seq[Int], runningCount: Option[Int]): Option[Int] = {
val finalResult: Int = newValues.sum + runningCount.getOrElse(0)
Option(finalResult)
}
def main(args: Array[String]): Unit = {
//获取SparkConf
val sparkConf: SparkConf = new SparkConf().setAppName("getHdfsFiles_to_wordcount").setMaster("local[6]").set("spark.driver.host", "localhost")
// 获取SparkContext
val sparkContext = new SparkContext(sparkConf)
// 设置日志级别
sparkContext.setLogLevel("WARN")
// 获取StreamingContext
val streamingContext = new StreamingContext(sparkContext, Seconds(5))
// 将历史结果都保存到一个路径下
streamingContext.checkpoint("./stream.check")
// 读取HDFS上的文件
val fileStream: DStream[String] = streamingContext.textFileStream("hdfs://node01:8020/stream_data")
// 对读取到的文件进行计数操作
val flatMapStream: DStream[String] = fileStream.flatMap(x => x.split(" "))
val wordAndOne: DStream[(String, Int)] = flatMapStream.map(x => (x, 1))
// reduceByKey不会将历史消息的值进行累加，所以需要用到updateStateByKey，需要的参数是updateFunc，需要自定义
val byKey: DStream[(String, Int)] = wordAndOne.updateStateByKey(updateFunc)
//输出结果
byKey.print()
streamingContext.start()
streamingContext.awaitTermination()
}
}
四、运行代码后，往HDFS文件夹上传文件
五、控制台输出结果
-------------------------------------------
Time: 1586856345000 ms
-------------------------------------------
-------------------------------------------
Time: 1586856350000 ms
-------------------------------------------
-------------------------------------------
Time: 1586856355000 ms
-------------------------------------------
(abc,1)
(world,1)
(hadoop,1)
(hive,1)
(hello,1)
(test,1)
-------------------------------------------
Time: 1586856360000 ms
-------------------------------------------
(abc,1)
(world,1)
(hadoop,1)
(hive,1)
(hello,1)
(test,1)
-------------------------------------------
Time: 1586856365000 ms
-------------------------------------------
(abc,1)
(world,1)
(hadoop,1)
(hive,1)
(hello,1)
(test,1)
-------------------------------------------
Time: 1586856370000 ms
-------------------------------------------
(abc,2)
(world,2)
(hadoop,2)
(hive,2)
(hello,2)
(test,2)
-------------------------------------------
Time: 1586856375000 ms
-------------------------------------------
(abc,2)
(world,2)
(hadoop,2)
(hive,2)
(hello,2)
(test,2)
自定义数据源
步骤
一、使用nc工具给指定端口发送数据
nc -lk 9999
二、开发代码
import org.apache.spark.streaming.dstream.{DStream, ReceiverInputDStream}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import org.apache.spark.{SparkConf, SparkContext}
object CustomReceiver {
/**
* 自定义updateFunc函数
* @param newValues
* @param runningCount
* @return
*/
def updateFunc(newValues:Seq[Int], runningCount:Option[Int]):Option[Int] = {
val finalResult: Int = newValues.sum + runningCount.getOrElse(0)
Option(finalResult)
}
def main(args: Array[String]): Unit = {
// 获取SparkConf
val sparkConf: SparkConf = new SparkConf().setAppName("CustomReceiver").setMaster("local[6]").set("spark.driver.host", "localhost")
// 获取SparkContext
val sparkContext = new SparkContext(sparkConf)
sparkContext.setLogLevel("WARN")
// 获取StreamingContext
val streamingContext = new StreamingContext(sparkContext, Seconds(5))
streamingContext.checkpoint("./stream_check")
// 读取自定义数据源的数据
val stream: ReceiverInputDStream[String] = streamingContext.receiverStream(new MyReceiver("node01", 9999))
// 对数据进行切割、计数操作
val mapStream: DStream[String] = stream.flatMap(x => x.split(" "))
val wordAndOne: DStream[(String, Int)] = mapStream.map((_, 1))
val byKey: DStream[(String, Int)] = wordAndOne.updateStateByKey(updateFunc)
// 输出结果
byKey.print()
streamingContext.start()
streamingContext.awaitTermination()
}
}
import java.io.{BufferedReader, InputStream, InputStreamReader}
import java.net.Socket
import java.nio.charset.StandardCharsets
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.receiver.Receiver
class MyReceiver(host:String,port:Int) extends Receiver[String](StorageLevel.MEMORY_AND_DISK_2){
/**
* 自定义receive方法接收socket数据，并调用store方法将数据保存起来
*/
private def receiverDatas(): Unit ={
// 接收socket数据
val socket = new Socket(host, port)
// 获取socket数据输入流
val stream: InputStream = socket.getInputStream
//通过BufferedReader ，将输入流转换为字符串
val reader = new BufferedReader(new InputStreamReader(stream,StandardCharsets.UTF_8))
var line: String = null
//判断读取到的数据不为空且receiver没有被停掉时
while ((line = reader.readLine()) != null && !isStopped()){
store(line)
}
stream.close()
socket.close()
reader.close()
}
/**
* 重写onStart和onStop方法，主要是onStart，onStart方法会被反复调用
*/
override def onStart(): Unit = {
// 启动通过连接接收数据的线程
new Thread(){
//重写run方法
override def run(): Unit = {
// 定义一个receiverDatas接收socket数据
receiverDatas()
}
}
}
// 停止结束的时候被调用
override def onStop(): Unit = {
}
}
RDD队列
步骤
一、开发代码
package cn.itcast.sparkstreaming.demo3
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.dstream.{DStream, InputDStream}
import org.apache.spark.{SparkConf, SparkContext}
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection.mutable
object QueneReceiver {
def main(args: Array[String]): Unit = {
//获取SparkConf
val sparkConf: SparkConf = new SparkConf().setMaster("local[6]").setAppName("queneReceiver").set("spark.driver.host", "localhost")
//获取SparkContext
val sparkContext = new SparkContext(sparkConf)
sparkContext.setLogLevel("WARN")
//获取StreamingContext
val streamingContext = new StreamingContext(sparkContext, Seconds(5))
val queue = new mutable.SynchronizedQueue[RDD[Int]]
// 需要参数 queue: Queue[RDD[T]]
val inputStream: InputDStream[Int] = streamingContext.queueStream(queue)
// 对DStream进行操作
val mapStream: DStream[Int] = inputStream.map(x => x * 2)
mapStream.print()
streamingContext.start()
//定义一个RDD队列
for (x <- 1 to 100){
queue += streamingContext.sparkContext.makeRDD(1 to 10)
Thread.sleep(3000)
}
streamingContext.awaitTermination()
}
}
