根据我对上面提供的代码的理解，它加载文件并进行映射操作并保存回来。没有需要shuffle的操作。此外，没有任何操作需要将数据传输到驱动程序，因此调优与shuffle或驱动程序相关的任何内容都不会产生影响。当任务太多时，驱动程序确实会有问题，但这只是在spark 2.0.2版本之前。可能会有两件事出错。

There are only one or a few executors. Increase the number of executors so that they can be allocated to different slaves. If you are using yarn need to change num-executors config or if you are using spark standalone then need to tune num cores per executor and spark max cores conf. In standalone num executors = max cores / cores per executor . The number of partitions are very few or maybe only one. So if this is low even if we have multi-cores,multi executors it will not be of much help as parallelization is dependent on the number of partitions. So increase the partitions by doing imageBundleRDD.repartition(11)

根据我对上面提供的代码的理解，它加载文件并进行映射操作并保存回来。没有需要shuffle的操作。此外，没有任何操作需要将数据传输到驱动程序，因此调优与shuffle或驱动程序相关的任何内容都不会产生影响。当任务太多时，驱动程序确实会有问题，但这只是在spark 2.0.2版本之前。可能会有两件事出错。

There are only one or a few executors. Increase the number of executors so that they can be allocated to different slaves. If you are using yarn need to change num-executors config or if you are using spark standalone then need to tune num cores per executor and spark max cores conf. In standalone num executors = max cores / cores per executor . The number of partitions are very few or maybe only one. So if this is low even if we have multi-cores,multi executors it will not be of much help as parallelization is dependent on the number of partitions. So increase the partitions by doing imageBundleRDD.repartition(11)

设置这些确切的配置有助于解决问题。

spark-submit --conf spark.yarn.maxAppAttempts=2 --executor-memory 10g --num-executors 50 --driver-memory 12g

为了添加一个通常不被讨论的用例，我将在本地模式下通过Spark -submit提交Spark应用程序时提出一个解决方案。

根据Jacek Laskowski的giitbook Mastering Apache Spark:

您可以在本地模式下运行Spark。在这种非分布式单JVM部署模式下，Spark在同一个JVM中生成所有执行组件——驱动程序、执行程序、后端和主机。这是驱动程序用于执行的唯一模式。

因此，如果您在堆中遇到OOM错误，调整驱动程序内存而不是执行程序内存就足够了。

这里有一个例子:

spark-1.6.1/bin/spark-submit
  --class "MyClass"
  --driver-memory 12g
  --master local[*] 
  target/scala-2.10/simple-project_2.10-1.0.jar 

看看启动脚本，Java堆大小设置在那里，看起来你在运行Spark worker之前没有设置这个。

# Set SPARK_MEM if it isn't already set since we also use it for this process
SPARK_MEM=${SPARK_MEM:-512m}
export SPARK_MEM

# Set JAVA_OPTS to be able to load native libraries and to set heap size
JAVA_OPTS="$OUR_JAVA_OPTS"
JAVA_OPTS="$JAVA_OPTS -Djava.library.path=$SPARK_LIBRARY_PATH"
JAVA_OPTS="$JAVA_OPTS -Xms$SPARK_MEM -Xmx$SPARK_MEM"

您可以在这里找到部署脚本的文档。

你应该配置offHeap内存设置如下所示:

val spark = SparkSession
     .builder()
     .master("local[*]")
     .config("spark.executor.memory", "70g")
     .config("spark.driver.memory", "50g")
     .config("spark.memory.offHeap.enabled",true)
     .config("spark.memory.offHeap.size","16g")   
     .appName("sampleCodeForReference")
     .getOrCreate()

根据您机器的RAM可用性提供驱动程序内存和执行程序内存。如果仍然面临OutofMemory问题，可以增加offHeap大小。