我正在使用Spark 2.0.2和Kryo序列化。
我正在尝试实现一个自定义接收器,用于将来自Google PubSub的消息提取到Spark Streaming中:
class PubSubReceiver(project: String, topic: String, subscription: String)
extends Receiver[Array[Byte]](StorageLevel.MEMORY_AND_DISK_2) with Logging {
val projectFullName = ProjectName.create(project)
val topicName = TopicName.create(project, topic)
val subscriptionName = SubscriptionName.create(project, subscription)
val subscriber = Subscriber.defaultBuilder(subscriptionName, new receiver).build
def onStart() {
new Thread() {
override def run() {
subscriber.startAsync()
//ensure subscriber is running as well as spark receiver
while (subscriber.isRunning && !isStopped()) {
logger.info(s"${subscriber.getSubscriptionName} receiver running")
//sleep 10s
Thread.sleep(10000)
}
logger.info(s"${subscriber.getSubscriptionName} receiver stopping")
}
}.start()
}
def onStop(): Unit = {
// There is nothing much to do as the thread calling receive()
// is designed to stop by itself if isStopped() returns false
}
private class receiver extends MessageReceiver {
override def receiveMessage(message: PubsubMessage, consumer: AckReplyConsumer): Unit = {
store(ArrayBuffer(message.getData.toByteArray), message.getAttributesMap)
}
}
}
但是,当运行使用此接收器的Spark作业时,似乎我必须序列化作业本身,这似乎不正确(然后将序列化Spark上下文)。
object PubSubStreamingIngestionJob extends App {
//... setup
lazy val ssc = new StreamingContext(spark.sparkContext, batchInterval)
lazy val pubsubUnionStream =the stream
ssc.receiverStream(new PubSubReceiver(projectName, topicName, subscriptionName))
pubsubUnionStream.map( messageBytes => ...business logic... )
ssc.start()
ssc.awaitTermination()
}
抛出以下错误:
java.io.IOException: com.esotericsoftware.kryo.KryoException: java.lang.IllegalArgumentException: Class is not registered: com.c2fo.atlas.jobs.streaming.gcp.PubSubStreamingIngestionJob
Note: To register this class use: kryo.register(com.mycompany.package.PubSubStreamingIngestionJob.class);
Serialization trace:
classes (sun.misc.Launcher$AppClassLoader)
contextClassLoader (java.lang.Thread)
threads (java.lang.ThreadGroup)
parent (java.lang.ThreadGroup)
group (java.util.concurrent.Executors$DefaultThreadFactory)
val$backingThreadFactory (com.google.common.util.concurrent.ThreadFactoryBuilder$1)
threadFactory (java.util.concurrent.ScheduledThreadPoolExecutor)
e (java.util.concurrent.Executors$DelegatedScheduledExecutorService)
executor (com.google.cloud.pubsub.spi.v1.Subscriber)
subscriber (com.mycompany.package.PubSubReceiver)
array (scala.collection.mutable.WrappedArray$ofRef)
有没有更好的方法来实现这个?
问题是Subscriber
实例需要是线程本地的,以防止整个闭包被序列化。
package org.apache.spark.streaming.gcp
import com.c2fo.atlas.util.LazyLogging
import com.google.cloud.pubsub.spi.v1._
import com.google.iam.v1.ProjectName
import com.google.pubsub.v1._
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.receiver.Receiver
import scala.collection.mutable.ArrayBuffer
class PubSubReceiver(project: String, topic: String, subscription: String)
extends Receiver[PubsubMessage](StorageLevel.MEMORY_AND_DISK_2) with LazyLogging{
val projectFullName = ProjectName.create(project)
val topicName = TopicName.create(project, topic)
val subscriptionName = SubscriptionName.create(project, subscription)
def onStart() {
new Thread() {
**//crucial change below**
val subscriber = Subscriber.defaultBuilder(subscriptionName, new receiver).build
override def run() {
subscriber.startAsync()
//ensure subscriber is running as well as spark receiver
while (subscriber.isRunning && !isStopped()) {
logger.info(s"${subscriber.getSubscriptionName} receiver running")
//sleep 10s
Thread.sleep(10000)
}
logger.info(s"${subscriber.getSubscriptionName} receiver stopping")
}
}.start()
}
def onStop(): Unit = {
// There is nothing much to do as the thread calling receive()
// is designed to stop by itself if isStopped() returns false
}
class receiver extends MessageReceiver {
override def receiveMessage(message: PubsubMessage, consumer: AckReplyConsumer): Unit = {
store(ArrayBuffer(message), message.getAttributesMap)
}
}
}