jerryshao commented on a change in pull request #25309:
[SPARK-28577][YARN]Resource capability requested for each executor add
offHeapMemorySize
URL: https://github.com/apache/spark/pull/25309#discussion_r310943756
##########
File path:
resource-managers/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnSparkHadoopUtil.scala
##########
@@ -184,4 +184,29 @@ object YarnSparkHadoopUtil {
ConverterUtils.toContainerId(containerIdString)
}
+ /**
+ * If MEMORY_OFFHEAP_ENABLED is true, we should ensure
executorOverheadMemory requested value
+ * is not less than MEMORY_OFFHEAP_SIZE, otherwise the memory resource
requested for executor
+ * may be not enough.
+ */
+ def executorMemoryOverheadRequested(sparkConf: SparkConf): Int = {
+ val executorMemory = sparkConf.get(EXECUTOR_MEMORY).toInt
+ val overhead = sparkConf.get(EXECUTOR_MEMORY_OVERHEAD).getOrElse(
+ math.max((MEMORY_OVERHEAD_FACTOR * executorMemory).toInt,
MEMORY_OVERHEAD_MIN)).toInt
+ val offHeap = if (sparkConf.get(MEMORY_OFFHEAP_ENABLED)) {
+ val size =
+ sparkConf.getSizeAsMb(MEMORY_OFFHEAP_SIZE.key,
MEMORY_OFFHEAP_SIZE.defaultValueString)
+ require(size > 0,
+ s"${MEMORY_OFFHEAP_SIZE.key} must be > 0 when
${MEMORY_OFFHEAP_ENABLED.key} == true")
+ if (size > overhead) {
+ logWarning(s"The value of ${MEMORY_OFFHEAP_SIZE.key}(${size}MB) will
be used as " +
+ s"executorMemoryOverhead to request resource to ensure that Executor
has enough memory " +
+ s"to use. It is recommended that the configuration value of " +
+ s"${EXECUTOR_MEMORY_OVERHEAD.key} should be no less than
${MEMORY_OFFHEAP_SIZE.key} " +
+ s"when ${MEMORY_OFFHEAP_ENABLED.key} is true.")
+ }
+ size
+ } else 0
+ math.max(overhead, offHeap).toInt
Review comment:
I agree with @tgravescs 's opinion.
>yeah, I understand that, if we are going to change it, 3.0 is a good time
to change that behavior. Like I said, I had found the off heap included in the
overhead as confusing because you already had another separate config, why do I
as a user have to add it into another config.
If overhead memory includes off-heap memory, pysparkWorkMemory and others,
it makes user hard to set a proper overhead memory, users should know every
other settings and figure out a proper number. As of time 3.0, I think we
should give a good definition of overhead memory, it can be inconsistent with
old version.
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