我正在通过以下方式读取数百万个 xml 文件
val xmls = sc.binaryFiles(xmlDir)
该操作在本地运行良好，但在纱线上失败并显示：
client token: N/A
diagnostics: Application application_1433491939773_0012 failed 2 times due to ApplicationMaster for attempt appattempt_1433491939773_0012_000002 timed out. Failing the application.
ApplicationMaster host: N/A
ApplicationMaster RPC port: -1
queue: default
start time: 1433750951883
final status: FAILED
tracking URL: http://controller01:8088/cluster/app/application_1433491939773_0012
user: ariskk
Exception in thread "main" org.apache.spark.SparkException: Application finished with failed status
at org.apache.spark.deploy.yarn.Client.run(Client.scala:622)
at org.apache.spark.deploy.yarn.Client$.main(Client.scala:647)
at org.apache.spark.deploy.yarn.Client.main(Client.scala)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)
at sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:497)
at org.apache.spark.deploy.SparkSubmit$.org$apache$spark$deploy$SparkSubmit$$runMain(SparkSubmit.scala:569)
at org.apache.spark.deploy.SparkSubmit$.doRunMain$1(SparkSubmit.scala:166)
at org.apache.spark.deploy.SparkSubmit$.submit(SparkSubmit.scala:189)
at org.apache.spark.deploy.SparkSubmit$.main(SparkSubmit.scala:110)
at org.apache.spark.deploy.SparkSubmit.main(SparkSubmit.scala)
在 hadoop/用户日志中，我经常收到以下消息：
15/06/08 09:15:38 WARN util.AkkaUtils: Error sending message [message = Heartbeat(1,[Lscala.Tuple2;@2b4f336b,BlockManagerId(1, controller01.stratified, 58510))] in 2 attempts
java.util.concurrent.TimeoutException: Futures timed out after [30 seconds]
at scala.concurrent.impl.Promise$DefaultPromise.ready(Promise.scala:219)
at scala.concurrent.impl.Promise$DefaultPromise.result(Promise.scala:223)
at scala.concurrent.Await$$anonfun$result$1.apply(package.scala:107)
at scala.concurrent.BlockContext$DefaultBlockContext$.blockOn(BlockContext.scala:53)
at scala.concurrent.Await$.result(package.scala:107)
at org.apache.spark.util.AkkaUtils$.askWithReply(AkkaUtils.scala:195)
at org.apache.spark.executor.Executor$$anon$1.run(Executor.scala:427)
我通过spark-submit 运行我的spark 作业，它适用于仅包含37k 文件的其他HDFS 目录。有什么想法如何解决这个问题吗？
好吧，在 Sparks 邮件列表上获得一些帮助后，我发现有两个问题：
src 目录，如果以 /my_dir/ 形式给出，则会导致 Spark 失败并产生心跳问题。相反，它应该以 hdfs:///my_dir/* 的形式给出
修复 #1 后，日志中出现内存不足错误。这是在纱线上运行的 Spark 驱动程序，由于文件数量而导致内存不足（显然它将所有文件信息保存在内存中）。所以我用 --conf spark.driver.memory=8g 提交了作业，解决了这个问题。
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