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Posted to issues@spark.apache.org by "Saisai Shao (JIRA)" <ji...@apache.org> on 2015/04/09 07:07:12 UTC
[jira] [Commented] (SPARK-6770) DirectKafkaInputDStream has not
been initialized when recovery from checkpoint
[ https://issues.apache.org/jira/browse/SPARK-6770?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14486712#comment-14486712 ]
Saisai Shao commented on SPARK-6770:
------------------------------------
I think you have to put your streaming related logic into the function {{functionToCreateContext}}, you could refer to the related Spark Streaming example {{RecoverableNetworkWordCount}} to change your code.
I think it is not a bug, you'd better try again.
> DirectKafkaInputDStream has not been initialized when recovery from checkpoint
> ------------------------------------------------------------------------------
>
> Key: SPARK-6770
> URL: https://issues.apache.org/jira/browse/SPARK-6770
> Project: Spark
> Issue Type: Bug
> Components: Streaming
> Affects Versions: 1.3.0
> Reporter: yangping wu
>
> I am read data from kafka using createDirectStream method and save the received log to Mysql, the code snippets as follows
> {code}
> def functionToCreateContext(): StreamingContext = {
> val sparkConf = new SparkConf()
> val sc = new SparkContext(sparkConf)
> val ssc = new StreamingContext(sc, Seconds(10))
> ssc.checkpoint("/tmp/kafka/channel/offset") // set checkpoint directory
> ssc
> }
> val struct = StructType(StructField("log", StringType) ::Nil)
> // Get StreamingContext from checkpoint data or create a new one
> val ssc = StreamingContext.getOrCreate("/tmp/kafka/channel/offset", functionToCreateContext)
> val SDB = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](ssc, kafkaParams, topics)
> val sqlContext = new org.apache.spark.sql.SQLContext(ssc.sparkContext)
> SDB.foreachRDD(rdd => {
> val result = rdd.map(item => {
> println(item)
> val result = item._2 match {
> case e: String => Row.apply(e)
> case _ => Row.apply("")
> }
> result
> })
> println(result.count())
> val df = sqlContext.createDataFrame(result, struct)
> df.insertIntoJDBC(url, "test", overwrite = false)
> })
> ssc.start()
> ssc.awaitTermination()
> ssc.stop()
> {code}
> But when I recovery the program from checkpoint, I encountered an exception:
> {code}
> Exception in thread "main" org.apache.spark.SparkException: org.apache.spark.streaming.kafka.DirectKafkaInputDStream@41a80e5a has not been initialized
> at org.apache.spark.streaming.dstream.DStream.isTimeValid(DStream.scala:266)
> at org.apache.spark.streaming.dstream.InputDStream.isTimeValid(InputDStream.scala:51)
> at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287)
> at org.apache.spark.streaming.dstream.DStream$$anonfun$getOrCompute$1.apply(DStream.scala:287)
> at scala.Option.orElse(Option.scala:257)
> at org.apache.spark.streaming.dstream.DStream.getOrCompute(DStream.scala:284)
> at org.apache.spark.streaming.dstream.ForEachDStream.generateJob(ForEachDStream.scala:38)
> at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
> at org.apache.spark.streaming.DStreamGraph$$anonfun$1.apply(DStreamGraph.scala:116)
> at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
> at scala.collection.TraversableLike$$anonfun$flatMap$1.apply(TraversableLike.scala:251)
> at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)
> at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47)
> at scala.collection.TraversableLike$class.flatMap(TraversableLike.scala:251)
> at scala.collection.AbstractTraversable.flatMap(Traversable.scala:105)
> at org.apache.spark.streaming.DStreamGraph.generateJobs(DStreamGraph.scala:116)
> at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$restart$4.apply(JobGenerator.scala:223)
> at org.apache.spark.streaming.scheduler.JobGenerator$$anonfun$restart$4.apply(JobGenerator.scala:218)
> at scala.collection.IndexedSeqOptimized$class.foreach(IndexedSeqOptimized.scala:33)
> at scala.collection.mutable.ArrayOps$ofRef.foreach(ArrayOps.scala:108)
> at org.apache.spark.streaming.scheduler.JobGenerator.restart(JobGenerator.scala:218)
> at org.apache.spark.streaming.scheduler.JobGenerator.start(JobGenerator.scala:89)
> at org.apache.spark.streaming.scheduler.JobScheduler.start(JobScheduler.scala:67)
> at org.apache.spark.streaming.StreamingContext.start(StreamingContext.scala:512)
> at logstatstreaming.UserChannelTodb$.main(UserChannelTodb.scala:57)
> at logstatstreaming.UserChannelTodb.main(UserChannelTodb.scala)
> at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
> at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:39)
> at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:25)
> at java.lang.reflect.Method.invoke(Method.java:597)
> at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
> at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
> at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
> at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
> at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
> {code}
> Not sure if this is a bug or a feature, but it's not obvious, so wanted to create a JIRA to make sure we document this behavior.Is someone can help me to see the reasons? Thank you.
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