Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/7274#discussion_r34964216
--- 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 scheduleExecutorsOnWorkers(
+ app: ApplicationInfo,
+ usableWorkers: Array[WorkerInfo],
+ spreadOutApps: Boolean): Array[Int] = {
+ // If the number of cores per executor is not specified, then we can
just schedule
+ // 1 core at a time since we expect a single executor to be launched
on each worker
+ val coresPerExecutor = app.desc.coresPerExecutor.getOrElse(1)
+ val memoryPerExecutor = app.desc.memoryPerExecutorMB
+ val numUsable = usableWorkers.length
+ val assignedCores = new Array[Int](numUsable) // Number of cores to
give to each worker
+ val assignedMemory = new Array[Int](numUsable) // Amount of memory to
give to each worker
+ var coresToAssign = math.min(app.coresLeft,
usableWorkers.map(_.coresFree).sum)
+ var pos = 0
if (spreadOutApps) {
- // Try to spread out each app among all the workers, until it has
all its cores
- for (app <- waitingApps if app.coresLeft > 0) {
- val usableWorkers = workers.toArray.filter(_.state ==
WorkerState.ALIVE)
- .filter(worker => worker.memoryFree >=
app.desc.memoryPerExecutorMB &&
- worker.coresFree >= app.desc.coresPerExecutor.getOrElse(1))
- .sortBy(_.coresFree).reverse
- val numUsable = usableWorkers.length
- val assigned = new Array[Int](numUsable) // Number of cores to
give on each node
- var toAssign = math.min(app.coresLeft,
usableWorkers.map(_.coresFree).sum)
- var pos = 0
- while (toAssign > 0) {
- if (usableWorkers(pos).coresFree - assigned(pos) > 0) {
- toAssign -= 1
- assigned(pos) += 1
- }
- pos = (pos + 1) % numUsable
- }
- // Now that we've decided how many cores to give on each node,
let's actually give them
- for (pos <- 0 until numUsable if assigned(pos) > 0) {
- allocateWorkerResourceToExecutors(app, assigned(pos),
usableWorkers(pos))
+ // Try to spread out executors among workers (sparse scheduling)
+ while (coresToAssign > 0) {
+ if (usableWorkers(pos).coresFree - assignedCores(pos) >=
coresPerExecutor &&
+ usableWorkers(pos).memoryFree - assignedMemory(pos) >=
memoryPerExecutor) {
+ coresToAssign -= coresPerExecutor
+ assignedCores(pos) += coresPerExecutor
+ assignedMemory(pos) += memoryPerExecutor
--- End diff --
So I stared at this loop for a little bit and I think it could bring us
into an infinite loop.
E.g. We have 3 workers, with 3, 3, and 4 cores left respectively, so that
`coresToAssign == 10`. Now let's say `coresPerExecutor == 3`, so after
allocating 3 executors we end up with `coresToAssign == 1`. What happens next?
Well, none of the usable workers can accommodate a new executor, and
`coresToAssign > 0` is still true, so this loop never exits.
Let me know if I'm missing something...
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