Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/7274#discussion_r34915631
--- Diff: core/src/main/scala/org/apache/spark/deploy/master/Master.scala
---
@@ -543,39 +544,72 @@ private[master] class Master(
* multiple executors from the same application may be launched on the
same worker if the worker
* has enough cores and memory. Otherwise, each executor grabs all the
cores available on the
* worker by default, in which case only one executor may be launched on
each worker.
+ *
+ * It is important to allocate coresPerExecutor on each worker at a time
(instead of 1 core
+ * at a time). Consider the following example: cluster has 4 workers
with 16 cores each.
+ * User requests 3 executors (spark.cores.max = 48, spark.executor.cores
= 16). If 1 core is
+ * allocated at a time, 12 cores from each worker would be assigned to
each executor.
+ * Since 12 < 16, no executors would launch [SPARK-8881].
*/
- private def startExecutorsOnWorkers(): Unit = {
- // Right now this is a very simple FIFO scheduler. We keep trying to
fit in the first app
- // in the queue, then the second app, etc.
+ private[master] def coresToAssign(
+ app: ApplicationInfo,
+ usableWorkers: Array[WorkerInfo],
+ spreadOutApps: Boolean): Array[Int] = {
+ // Default value for number of cores per executor is 1
--- End diff --
as commented elsewhere, this is incorrect. We just happen to do
`getOrElse(1)` here to allocate 1 core at a time if this is not set.
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