val executorMemory = conf.getSizeAsBytes("spark.executor.memory") if (executorMemory < MIN_MEMORY_BYTES) { throw new IllegalArgumentException(s"Executor memory $executorMemory must be at least " + s"$MIN_MEMORY_BYTES. Please increase executor memory using the " + s"--executor-memory option...
取参数spark.testing.memory,未配置的话取运行时环境中的最大内存 val systemMemory = conf.getLong("spark.testing.memory", Runtime.getRuntime.maxMemory) // 获取预留内存reservedMemory,取参数spark.testing.reservedMemory, // 未配置的话,根据参数spark.testing...
其中core/scala/org.apache.spark.memory包包含的是内存管理的核心类,包括MemoryManager、MemoryPool以及具体实现类。 core/scala/org.apache.spark.storage/memory包只有一个类MemoryStore,主要用来将数据块以序列化/非序列化的形式写入storage memory中 core/java/org.apache.spark.memory包包含了三个类:MemoryConsumer,...
serializer org.apache.spark.serializer.KryoSerializer# spark.driver.memory 5g# spark.executor.extraJavaOptions -XX:+PrintGCDetails -Dkey=value -Dnumbers="one two three"spark.master spark://node1:7077 spark.eventLog.enabledtruespark.eventLog.dir hdfs://dw-cluster:8020/opt/spark/applicationHistory s...
val memoryFraction = conf.getDouble("spark.memory.fraction", 0.6) (usableMemory * memoryFraction).toLong } 首先给系统内存reservedMemory预留了300M,若jvm能拿到的最大内存和配置的executor内存分别不足以reservedMemory的1.5倍即450M都会抛出异常,最后storage和execution能拿到的内存为: ...
我们在《Spark源码分析之七:Task运行(一)》一文中曾经提到过,在Task被传递到Executor上去执行时,在为其分配的TaskRunner线程的run()方法内,在Task真正运行之前,我们就要构造一个任务内存管理器TaskMemoryManager,然后在反序列化Task对象的二进制数据得到Task对象后,需要将这个内存管理器TaskMemoryManager设置为Task的成员...
首先给系统内存reservedMemory预留了300M,若jvm能拿到的最大内存和配置的executor内存分别不足以reservedMemory的1.5倍即450M都会抛出异常,最后storage和execution能拿到的内存为: (heap space - 300)* spark.memory.fraction (默认为0.6) storage和execution各占所获内存的50%。
首先给系统内存reservedMemory预留了300M,若jvm能拿到的最大内存和配置的executor内存分别不足以reservedMemory的1.5倍即450M都会抛出异常,最后storage和execution能拿到的内存为: 代码语言:javascript 复制 (heap space-300)*spark.memory.fraction (默认为0.6) ...
executor空闲超时后,会被移除 对于Spark Streaming,数据按时间段到达,为了防止executor频繁出现添加移除现象,应该禁用该功能。 内存格局 spark-memory.png 说明: The application master, which is a non-executor container with the special capability of requesting containers from YARN, takes up resources of its ...
Note that in the completed state, the driver pod doesnotuse any computational or memory resources. The driver and executor pod scheduling are handled by Kubernetes. Communication to the Kubernetes API is done via fabric8. It is possible to schedule the driver and executor pods on a subset of...