Spark Streaming
- Spark Streaming概念
- Spark Streaming操作
- 1 netcat传入数据
- 2 DStream 创建
- 3 自定义数据源
- 4 接受kafka数据
- DStream 转换
- 1无状态的转换
- 2有状态的转换
- updateSateByKey
- WindowOperations
Spark Streaming概念
Spark Streaming 用于流式数据的处理。
Spark Streaming 支持的数据输入源很多，例如：Kafka、 Flume 、Twitter 、ZeroMQ 和简单的 TCP 套接字等等。
数据输入后可以用 Spark 的高度抽象原语。如：map、reduce、join、window 等进行运算。而结果也能保存在很多地方，如 HDFS，数据库等。
Spark Streaming 使用离散化流(discretized stream)作为抽象表示，叫作 DStream 。DStream 是随时间推移而收到的数据的序列。在内部，每个时间区间收 到的数据都作为 RDD 存在，而 DStream 是由这些 RDD 所组成的序列(因此得名“离散化”)。所以 简单来将， DStream 就是对 RDD 在实时数据处理场景的一种封装。
Spark Streaming操作
1 netcat传入数据
import org.apache.spark.SparkConf
import org.apache.spark.streaming.{Seconds, StreamingContext}object StreamWordCount {def main(args:Array[String])={//1.初始化 Spark 配置信息val sparkConf = new SparkConf().setMaster("local[*]").setAppName("StreamWordCount")//2.初始化 SparkStreamingContextval ssc = new StreamingContext(sparkConf, Seconds(3))//3.通过监控端口创建 DStream，读进来的数据为一行行val lineStreams = ssc.socketTextStream ("localhost", 9999)//将每一行数据做切分， 形成一个个单词val wordStreams = lineStreams.flatMap(_.split(" "))//将单词映射成元组(word,1)val wordAndOneStreams = wordStreams.map((_, 1))//将相同的单词次数做统计val wordAndCountStreams = wordAndOneStreams.reduceByKey(_+_)//打印wordAndCountStreams.print()//启动 SparkStreamingContextssc.start ()ssc.awaitTermination ()}}链接: 配置netcat
下载netcat，解压到英文路径下。
将文件路径添加到环境变量中。
启动netcat。
运行StreamWordCount 程序。
2 DStream 创建
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.streaming.{Seconds, StreamingContext}
import scala.collection.mutableobject SparkStreaming02_Queue {def main(args: Array[String]) {//1.初始化 Spark 配置信息val conf = new SparkConf().setMaster("local[*]").setAppName("RDDStream")//2.初始化 SparkStreamingContextval ssc = new StreamingContext(conf, Seconds(4))//3.创建 RDD 队列val rddQueue = new mutable.Queue[RDD[Int]]()//4.创建 QueueInputDStreamval inputStream = ssc.queueStream(rddQueue,oneAtATime = false)//5.处理队列中的 RDD 数据val mappedStream = inputStream.map ((_,1))val reducedStream = mappedStream.reduceByKey(_ + _)//6.打印结果reducedStream.print()//7.启动任务ssc.start()//8.循环创建并向 RDD 队列中放入 RDDfor (i <- 1 to 5) {rddQueue += ssc.sparkContext.makeRDD(1 to 300, 10)Thread.sleep(2000)}ssc.awaitTermination()}
}
3 自定义数据源
import java.util.Randomimport org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.ReceiverInputDStream
import org.apache.spark.streaming.receiver.Receiver
import org.apache.spark.streaming.{Seconds, StreamingContext}import scala.collection.mutableobject SparkStreaming03_DIY {def main(args: Array[String]) {//1.初始化 Spark 配置信息val conf = new SparkConf().setMaster("local[*]").setAppName("RDDStream")val ssc = new StreamingContext(conf, Seconds(3))val messageDS: ReceiverInputDStream[String] = ssc.receiverStream(new MyReceiver())messageDS.print()ssc.start()ssc.awaitTermination()}/* 自定义数据采集器1.继承Receiver，定义泛型，传递参数2.重写方法*/class MyReceiver extends Receiver[String](StorageLevel.MEMORY_ONLY){private var flg =trueoverride def onStart(): Unit = {new Thread(new Runnable {override def run(): Unit = {while(flg){val message = "采集的数据为：" + new Random().nextInt(10).toStringstore(message)Thread.sleep(500)}}}).start()}override def onStop(): Unit = {flg=false;}}
}4 接受kafka数据
import java.util.Randomimport org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.receiver.Receiver
import org.apache.spark.streaming.{Seconds, StreamingContext}import scala.collection.mutableobject SparkStreaming04_kafka {def main(args: Array[String]) {val conf = new SparkConf().setMaster("local[*]").setAppName("RDDStream")val ssc = new StreamingContext(conf, Seconds(3))//3.定义 Kafka 参数val kafkaPara: Map[String, Object] = Map[String, Object](ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG ->"hadoop102:9092,hadoop103:9092,hadoop104:9092",ConsumerConfig.GROUP_ID_CONFIG -> "atguigu","key.deserializer" ->"org.apache.kafka.common.serialization.StringDeserializer","value.deserializer" ->"org.apache.kafka.common.serialization.StringDeserializer")//4.读取 Kafka 数据创建 DStreamval kafkaDataDS: InputDStream[ConsumerRecord[String, String]] = KafkaUtils.createDirectStream[String, String](ssc,LocationStrategies.PreferConsistent,ConsumerStrategies.Subscribe [String, String](Set("atguiguNew"), kafkaPara))kafkaDataDS.map(_.value()).print()ssc.start()ssc.awaitTermination()}}DStream 转换
1无状态的转换
DStream 上的操作与 RDD 的类似，分为 Transformations (转换) 和 Output Operations (输 出)两种。
状态：DStream状态，每一次实时处理都要登录相关配置信息或是有一定初始状态。设置一个状态，这段时间在这个状态下设有一定的权限或记录着某种数值状态，方便后续处理。
//无状态数据操作，只对当前的采集周期内的数据进行处理
//在某些场合下，需要保留数据统计结果（状态），实现数据的汇总
import java.util.Randomimport org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.receiver.Receiver
import org.apache.spark.streaming.{Seconds, StreamingContext}import scala.collection.mutableobject SparkStreaming05_State {def main(args: Array[String]): Unit = {val sparkConf = new SparkConf().setMaster("local[*]").setAppName("SparkStreaming")val ssc = new StreamingContext(sparkConf, Seconds(3))//无状态数据操作，只对当前的采集周期内的数据进行处理//在某些场合下，需要保留数据统计结果（状态），实现数据的汇总val datas = ssc.socketTextStream("localhost",9999)val wordToOne = datas.map((_,1))val wordToCount = wordToOne.reduceByKey(_+_)wordToCount.print()ssc.start()ssc.awaitTermination()}
}
转换结构使用了reduceByKey，会直接出结果，不能和缓冲区的数据进行汇总。
val wordToCount = wordToOne.reduceByKey(+)
updateSateByKey：根据key对数据的状态进行更新
传递的参数中含有两个值
第一个值表示相同的key的value数据
第二个值表示缓冲区相同key的value数据
import java.util.Random
import org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.receiver.Receiver
import org.apache.spark.streaming.{Seconds, StreamingContext}import scala.collection.mutableobject SparkStreaming05_State {def main(args: Array[String]): Unit = {val sparkConf = new SparkConf().setMaster("local[*]").setAppName("SparkStreaming2")val ssc = new StreamingContext(sparkConf, Seconds(3))//无状态数据操作，只对当前的采集周期内的数据进行处理//在某些场合下，需要保留数据统计结果（状态），实现数据的汇总val datas = ssc.socketTextStream("localhost",9999)val wordToOne = datas.map((_,1))
// val wordToCount = wordToOne.reduceByKey(_+_)// updateSateByKey：根据key对数据的状态进行更新
// 传递的参数中含有两个值
// 第一个值表示相同的key的value数据
// 第二个值表示缓冲区相同key的value数据val state = wordToOne updateStateByKey ((seq:Seq[Int], buff:Option[Int] ) => {val newCount = buff.getOrElse(0) + seq.sumOption(newCount)})state.print()ssc.start()ssc.awaitTermination()}
}
23/10/10 15:26:41 ERROR StreamingContext: Error starting the context, marking it as stopped
java.lang.IllegalArgumentException: requirement failed: The checkpoint directory has not been set. Please set it by StreamingContext.checkpoint().Exception in thread "main" java.lang.IllegalArgumentException: requirement failed: The checkpoint directory has not been set. Please set it by StreamingContext.checkpoint().
这个错误是由于未设置 Spark Streaming 的检查点目录导致的。检查点目录用于存储 Spark Streaming 的元数据和中间状态信息，以便在故障恢复时保持一致性。
要解决这个问题，你需要在创建 StreamingContext 对象之前通过 checkpoint 方法设置检查点目录。
设置一个检查点就好了，填写对应的检查点路径。
ssc.checkpoint(“input”)
import java.util.Randomimport org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.receiver.Receiver
import org.apache.spark.streaming.{Seconds, StreamingContext}import scala.collection.mutableobject SparkStreaming05_State {def main(args: Array[String]): Unit = {val sparkConf = new SparkConf().setMaster("local[*]").setAppName("SparkStreaming2")val ssc = new StreamingContext(sparkConf, Seconds(3))ssc.checkpoint("input")//无状态数据操作，只对当前的采集周期内的数据进行处理//在某些场合下，需要保留数据统计结果（状态），实现数据的汇总val datas = ssc.socketTextStream("localhost",9999)val wordToOne = datas.map((_,1))
// val wordToCount = wordToOne.reduceByKey(_+_)// updateSateByKey：根据key对数据的状态进行更新
// 传递的参数中含有两个值
// 第一个值表示相同的key的value数据
// 第二个值表示缓冲区相同key的value数据val state = wordToOne updateStateByKey ((seq:Seq[Int], buff:Option[Int] ) => {val newCount = buff.getOrElse(0) + seq.sumOption(newCount)})state.print()ssc.start()ssc.awaitTermination()}
}
Transform
Transform 允许 DStream 上执行任意的 RDD-to-RDD 函数。即使这些函数并没有在 DStream 的 API 中暴露出来，通过该函数可以方便的扩展 Spark API。该函数每一批次调度一次。其实也
就是对 DStream 中的 RDD 应用转换。
使用Transform 的两个原因：
Transform 可以将底层RDD获取到后进行操作。
1.DStream功能不完善
2.需要RDD/代码周期性的执行
import java.util.Randomimport org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.{DStream, InputDStream, ReceiverInputDStream}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.receiver.Receiver
import org.apache.spark.streaming.{Seconds, StreamingContext}import scala.collection.mutableobject SparkStreaming06_State_Transform {def main(args: Array[String]): Unit = {val sparkConf = new SparkConf().setMaster("local[*]").setAppName("SparkStreaming2")val ssc = new StreamingContext(sparkConf, Seconds(3))val lines = ssc.socketTextStream("localhost",port=9999)//transform方法可以将底层RDD获取到后 进行操作val newDs: DStream[String] = lines.transform(rdd => {//code:Driver端，（周期性执行）rdd.map(str=>{//Code ： Executor端str})})val newDs1: DStream[String] = lines.map(data=>{data})ssc.start()ssc.awaitTermination()}
}
import org.apache.spark.SparkConf
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.{Seconds, StreamingContext}object SparkStreaming06_State_Join {def main(args: Array[String]): Unit = {val sparkConf = new SparkConf().setMaster("local[*]").setAppName("SparkStreaming2")val ssc = new StreamingContext(sparkConf, Seconds(5))val data9999 = ssc.socketTextStream("localhost",port=9999)val data8888 = ssc.socketTextStream("localhost",port=8888)val map9999: DStream[(String, Int)] = data9999.map((_, 9))val map8888: DStream[(String, Int)] = data8888.map((_, 8))//join操作就是两个RDD的join操作val joinDS: DStream[(String, (Int, Int))] = map9999.join(map8888)joinDS.print()ssc.start()ssc.awaitTermination()}
}
2有状态的转换
updateSateByKey
updateSateByKey：根据key对数据的状态进行更新
传递的参数中含有两个值
第一个值表示相同的key的value数据
第二个值表示缓冲区相同key的value数据
import java.util.Random
import org.apache.kafka.clients.consumer.{ConsumerConfig, ConsumerRecord}
import org.apache.spark.SparkConf
import org.apache.spark.rdd.RDD
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.{InputDStream, ReceiverInputDStream}
import org.apache.spark.streaming.kafka010.{ConsumerStrategies, KafkaUtils, LocationStrategies}
import org.apache.spark.streaming.receiver.Receiver
import org.apache.spark.streaming.{Seconds, StreamingContext}import scala.collection.mutableobject SparkStreaming05_State {def main(args: Array[String]): Unit = {val sparkConf = new SparkConf().setMaster("local[*]").setAppName("SparkStreaming2")val ssc = new StreamingContext(sparkConf, Seconds(3))//无状态数据操作，只对当前的采集周期内的数据进行处理//在某些场合下，需要保留数据统计结果（状态），实现数据的汇总val datas = ssc.socketTextStream("localhost",9999)val wordToOne = datas.map((_,1))
// val wordToCount = wordToOne.reduceByKey(_+_)// updateSateByKey：根据key对数据的状态进行更新
// 传递的参数中含有两个值
// 第一个值表示相同的key的value数据
// 第二个值表示缓冲区相同key的value数据val state = wordToOne updateStateByKey ((seq:Seq[Int], buff:Option[Int] ) => {val newCount = buff.getOrElse(0) + seq.sumOption(newCount)})state.print()ssc.start()ssc.awaitTermination()}
}
WindowOperations
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