Quais são os possíveis motivos para receber TimeoutException: futuros atingiram o tempo limite após [n segundos] ao trabalhar com o Spark [duplicado]

Esta pergunta já tem uma resposta aqui:

Por que a associação falha com "java.util.concurrent.TimeoutException: Futuros atingiram o tempo limite após [300 segundos]"? 2 respostas

Estou trabalhando em um programa Spark SQL e estou recebendo a seguinte exceção:

16/11/07 15:58:25 ERROR yarn.ApplicationMaster: User class threw exception: java.util.concurrent.TimeoutException: Futures timed out after [3000 seconds]
java.util.concurrent.TimeoutException: Futures timed out after [3000 seconds]
    at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
    at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
    at scala.concurrent.Await$anonfun$result$1.apply(package.scala:190)
    at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
    at scala.concurrent.Await$.result(package.scala:190)
    at org.apache.spark.sql.execution.joins.BroadcastHashJoin.doExecute(BroadcastHashJoin.scala:107)
    at org.apache.spark.sql.execution.SparkPlan$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.Project.doExecute(basicOperators.scala:46)
    at org.apache.spark.sql.execution.SparkPlan$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.Union$anonfun$doExecute$1.apply(basicOperators.scala:144)
    at org.apache.spark.sql.execution.Union$anonfun$doExecute$1.apply(basicOperators.scala:144)
    at scala.collection.TraversableLike$anonfun$map$1.apply(TraversableLike.scala:245)
    at scala.collection.TraversableLike$anonfun$map$1.apply(TraversableLike.scala:245)
    at scala.collection.immutable.List.foreach(List.scala:381)
    at scala.collection.TraversableLike$class.map(TraversableLike.scala:245)
    at scala.collection.immutable.List.map(List.scala:285)
    at org.apache.spark.sql.execution.Union.doExecute(basicOperators.scala:144)
    at org.apache.spark.sql.execution.SparkPlan$anonfun$execute$5.apply(SparkPlan.scala:132)
    at org.apache.spark.sql.execution.SparkPlan$anonfun$execute$5.apply(SparkPlan.scala:130)
    at org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:150)
    at org.apache.spark.sql.execution.SparkPlan.execute(SparkPlan.scala:130)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation.buildBuffers(InMemoryColumnarTableScan.scala:129)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation.<init>(InMemoryColumnarTableScan.scala:118)
    at org.apache.spark.sql.execution.columnar.InMemoryRelation$.apply(InMemoryColumnarTableScan.scala:41)
    at org.apache.spark.sql.execution.CacheManager$anonfun$cacheQuery$1.apply(CacheManager.scala:93)
    at org.apache.spark.sql.execution.CacheManager.writeLock(CacheManager.scala:60)
    at org.apache.spark.sql.execution.CacheManager.cacheQuery(CacheManager.scala:84)
    at org.apache.spark.sql.DataFrame.persist(DataFrame.scala:1581)
    at org.apache.spark.sql.DataFrame.cache(DataFrame.scala:1590)
    at com.somecompany.ml.modeling.NewModel.getTrainingSet(FlowForNewModel.scala:56)
    at com.somecompany.ml.modeling.NewModel.generateArtifacts(FlowForNewModel.scala:32)
    at com.somecompany.ml.modeling.Flow$class.run(Flow.scala:52)
    at com.somecompany.ml.modeling.lowForNewModel.run(FlowForNewModel.scala:15)
    at com.somecompany.ml.Main$anonfun$2.apply(Main.scala:54)
    at com.somecompany.ml.Main$anonfun$2.apply(Main.scala:54)
    at scala.Option.getOrElse(Option.scala:121)
    at com.somecompany.ml.Main$.main(Main.scala:46)
    at com.somecompany.ml.Main.main(Main.scala)
    at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
    at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
    at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
    at java.lang.reflect.Method.invoke(Method.java:498)
    at org.apache.spark.deploy.yarn.ApplicationMaster$anon$2.run(ApplicationMaster.scala:542)
16/11/07 15:58:25 INFO yarn.ApplicationMaster: Final app status: FAILED, exitCode: 15, (reason: User class threw exception: java.util.concurrent.TimeoutException: Futures timed out after [3000 seconds])

A última parte do meu código que reconheço do rastreamento de pilha écom.somecompany.ml.modeling.NewModel.getTrainingSet(FlowForNewModel.scala:56) o que me leva a esta linha:profilesDF.cache() Antes do armazenamento em cache, faço uma união entre 2 quadros de dados. Vi uma resposta sobre a persistência de ambos os quadros de dados antes da junçãoaqui Ainda preciso armazenar em cache o quadro de dados sindicalizado, pois o estou usando em várias de minhas transformações

E eu queria saber o que pode causar essa exceção ser lançada? A pesquisa me levou a um link que lida com a exceção de tempo limite do rpc ou com alguns problemas de segurança que não são o meu problema.

desde já, obrigado

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