Errore RDD operativo durante l'impostazione del delimitatore di record Spark con org.apache.hadoop.conf.Configuration

Question

Desidero elaborare un file di testo di grandi dimensioni "mydata.txt" (la dimensione del file effettivo è circa 30 GB) con Spark. Il delimitatore di record è "\ |" seguito da "\ n". Poiché il separatore di record predefinito del file di caricamento (da "sc.textFile") è "\ n", ho impostato la proprietà "textinputformat.record.delimiter" di org.apache.hadoop.conf.Configuration su "\ | \ n" per specificare il delimitatore di record:

AAAAA_|BBBBB_| 
CCCCC\ 
DDDDD 
EEEEE_FFFFFFFFFFFF\ | 
GGGGG_|HHHHH_| 
IIIII\ 
GGGGG\ 
KKKKK_|LLLLLLLLLLL\ | 
MMMM_|NNNNN_|OOOOO\ | 

Poi ho eseguito il seguente codice nel spark-shell:

import org.apache.hadoop.io.LongWritable 
import org.apache.hadoop.io.Text 
import org.apache.hadoop.conf.Configuration 
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat 

val LINE_DELIMITER = "\\ |\n" 
val FIELD_SEP = "_\\|" 

val conf = new Configuration 
conf.set("textinputformat.record.delimiter", LINE_DELIMITER) 
val raw_data = sc.newAPIHadoopFile("mydata.txt", classOf[TextInputFormat], classOf[LongWritable], classOf[Text], conf).map(_._2.toString) 

finora tutto bene. tuttavia,

scala> val data = raw_data.filter(x => x.split(FIELD_SEP).size >= 3) 
data: org.apache.spark.rdd.RDD[String] = FilteredRDD[4] at filter at <console>:22 

scala> data.collect 
org.apache.spark.SparkException: Job aborted due to stage failure: Task not serializable: java.io.NotSerializableException: org.apache.hadoop.conf.Configuration 
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1049) 
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1033) 
    at org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1031) 
    at scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59) 
    at scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:47) 
    at org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1031) 
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitMissingTasks(DAGScheduler.scala:772) 
    at org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$submitStage(DAGScheduler.scala:715) 
    at org.apache.spark.scheduler.DAGScheduler.handleJobSubmitted(DAGScheduler.scala:699) 
    at org.apache.spark.scheduler.DAGSchedulerEventProcessActor$$anonfun$receive$2.applyOrElse(DAGScheduler.scala:1203) 
    at akka.actor.ActorCell.receiveMessage(ActorCell.scala:498) 
    at akka.actor.ActorCell.invoke(ActorCell.scala:456) 
    at akka.dispatch.Mailbox.processMailbox(Mailbox.scala:237) 
    at akka.dispatch.Mailbox.run(Mailbox.scala:219) 
    at akka.dispatch.ForkJoinExecutorConfigurator$AkkaForkJoinTask.exec(AbstractDispatcher.scala:386) 
    at scala.concurrent.forkjoin.ForkJoinTask.doExec(ForkJoinTask.java:260) 
    at scala.concurrent.forkjoin.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:1339) 
    at scala.concurrent.forkjoin.ForkJoinPool.runWorker(ForkJoinPool.java:1979) 
    at scala.concurrent.forkjoin.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:107) 

scala> data.foreach(println) 
org.apache.spark.SparkException: Job aborted due to stage failure: Task not serializable: java.io.NotSerializableException: org.apache.hadoop.conf.Configuration 
    ... 

Perché non può manipolare "dati" RDD, mentre tutto va bene quando si utilizza sc.textFile("mydata.txt")? E come risolverlo?

Answer 1

Hai trovato questa eccezione perché si sta chiudendo su org.apache.hadoop.conf.Configuration ma non serializable

Caused by: java.io.NotSerializableException: org.apache.hadoop.conf.Configuration 
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1183) 
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547) 
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508) 
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431) 
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177) 
    at java.io.ObjectOutputStream.defaultWriteFields(ObjectOutputStream.java:1547) 
    at java.io.ObjectOutputStream.writeSerialData(ObjectOutputStream.java:1508) 
    at java.io.ObjectOutputStream.writeOrdinaryObject(ObjectOutputStream.java:1431) 
    at java.io.ObjectOutputStream.writeObject0(ObjectOutputStream.java:1177) 

è che si può fare due cose: 1. configurazione Registrati con un serializzatore Kyro O 2. Basta marcare il conf variabile come transient che fondamentalmente dice a Spark di non spedirlo con la chiusura.

scala> @transient val conf = new Configuration 
conf: org.apache.hadoop.conf.Configuration = Configuration: core-default.xml, core-site.xml, mapred-default.xml, mapred-site.xml 

scala> val raw_data = sc.newAPIHadoopFile("../test.txt", classOf[TextInputFormat], classOf[LongWritable], classOf[Text], conf).map(_._2.toString) 
14/11/28 00:54:03 INFO MemoryStore: ensureFreeSpace(32937) called with curMem=70594, maxMem=278302556 
14/11/28 00:54:03 INFO MemoryStore: Block broadcast_4 stored as values in memory (estimated size 32.2 KB, free 265.3 MB) 
raw_data: org.apache.spark.rdd.RDD[String] = MappedRDD[5] at map at <console>:18 

scala> val data = raw_data.filter{x => x.split(FIELD_SEP).size >= 3} 
data: org.apache.spark.rdd.RDD[String] = FilteredRDD[6] at filter at <console>:22 

scala> data.count 
14/11/28 00:54:16 INFO FileInputFormat: Total input paths to process : 1 
14/11/28 00:54:16 INFO SparkContext: Starting job: count at <console>:25 
14/11/28 00:54:16 INFO DAGScheduler: Got job 2 (count at <console>:25) with 1 output partitions (allowLocal=false) 
14/11/28 00:54:16 INFO DAGScheduler: Final stage: Stage 2(count at <console>:25) 
14/11/28 00:54:16 INFO DAGScheduler: Parents of final stage: List() 
14/11/28 00:54:16 INFO DAGScheduler: Missing parents: List() 
14/11/28 00:54:16 INFO DAGScheduler: Submitting Stage 2 (FilteredRDD[6] at filter at <console>:22), which has no missing parents 
14/11/28 00:54:16 INFO MemoryStore: ensureFreeSpace(4488) called with curMem=103531, maxMem=278302556 
14/11/28 00:54:16 INFO MemoryStore: Block broadcast_5 stored as values in memory (estimated size 4.4 KB, free 265.3 MB) 
14/11/28 00:54:16 INFO DAGScheduler: Submitting 1 missing tasks from Stage 2 (FilteredRDD[6] at filter at <console>:22) 
14/11/28 00:54:16 INFO TaskSchedulerImpl: Adding task set 2.0 with 1 tasks 
14/11/28 00:54:16 INFO TaskSetManager: Starting task 0.0 in stage 2.0 (TID 2, localhost, PROCESS_LOCAL, 1223 bytes) 
14/11/28 00:54:16 INFO Executor: Running task 0.0 in stage 2.0 (TID 2) 
14/11/28 00:54:16 INFO NewHadoopRDD: Input split: file:/Users/ssimanta/spark/test.txt:0+123 
14/11/28 00:54:16 INFO Executor: Finished task 0.0 in stage 2.0 (TID 2). 1731 bytes result sent to driver 
14/11/28 00:54:16 INFO TaskSetManager: Finished task 0.0 in stage 2.0 (TID 2) in 19 ms on localhost (1/1) 
14/11/28 00:54:16 INFO DAGScheduler: Stage 2 (count at <console>:25) finished in 0.019 s 
14/11/28 00:54:16 INFO TaskSchedulerImpl: Removed TaskSet 2.0, whose tasks have all completed, from pool 
14/11/28 00:54:16 INFO DAGScheduler: Job 2 finished: count at <console>:25, took 0.025300 s 
res5: Long = 1 

scala> data.collect 
14/11/28 00:55:16 INFO SparkContext: Starting job: collect at <console>:25 
14/11/28 00:55:16 INFO DAGScheduler: Got job 3 (collect at <console>:25) with 1 output partitions (allowLocal=false) 
14/11/28 00:55:16 INFO DAGScheduler: Final stage: Stage 3(collect at <console>:25) 
14/11/28 00:55:16 INFO DAGScheduler: Parents of final stage: List() 
14/11/28 00:55:16 INFO DAGScheduler: Missing parents: List() 
14/11/28 00:55:16 INFO DAGScheduler: Submitting Stage 3 (FilteredRDD[6] at filter at <console>:22), which has no missing parents 
14/11/28 00:55:16 INFO MemoryStore: ensureFreeSpace(4504) called with curMem=108019, maxMem=278302556 
14/11/28 00:55:16 INFO MemoryStore: Block broadcast_6 stored as values in memory (estimated size 4.4 KB, free 265.3 MB) 
14/11/28 00:55:16 INFO DAGScheduler: Submitting 1 missing tasks from Stage 3 (FilteredRDD[6] at filter at <console>:22) 
14/11/28 00:55:16 INFO TaskSchedulerImpl: Adding task set 3.0 with 1 tasks 
14/11/28 00:55:16 INFO TaskSetManager: Starting task 0.0 in stage 3.0 (TID 3, localhost, PROCESS_LOCAL, 1223 bytes) 
14/11/28 00:55:16 INFO Executor: Running task 0.0 in stage 3.0 (TID 3) 
14/11/28 00:55:16 INFO NewHadoopRDD: Input split: file:/Users/ssimanta/spark/test.txt:0+123 
14/11/28 00:55:16 INFO Executor: Finished task 0.0 in stage 3.0 (TID 3). 1717 bytes result sent to driver 
14/11/28 00:55:16 INFO TaskSetManager: Finished task 0.0 in stage 3.0 (TID 3) in 16 ms on localhost (1/1) 
14/11/28 00:55:16 INFO DAGScheduler: Stage 3 (collect at <console>:25) finished in 0.017 s 
14/11/28 00:55:16 INFO TaskSchedulerImpl: Removed TaskSet 3.0, whose tasks have all completed, from pool 
14/11/28 00:55:16 INFO DAGScheduler: Job 3 finished: collect at <console>:25, took 0.021439 s 
res6: Array[String] = Array(MMMM_|NNNNN_|OOOOO\ |)