Github user lianhuiwang commented on a diff in the pull request:
https://github.com/apache/spark/pull/731#discussion_r12564010
--- Diff: core/src/main/scala/org/apache/spark/deploy/master/Master.scala
---
@@ -466,30 +466,14 @@ private[spark] class Master(
* launched an executor for the app on it (right now the standalone
backend doesn't like having
* two executors on the same worker).
*/
- def canUse(app: ApplicationInfo, worker: WorkerInfo): Boolean = {
- worker.memoryFree >= app.desc.memoryPerSlave &&
!worker.hasExecutor(app)
+ private def canUse(app: ApplicationInfo, worker: WorkerInfo): Boolean = {
+ worker.memoryFree >= app.desc.memoryPerExecutor &&
!worker.hasExecutor(app) &&
+ worker.coresFree > 0
--- End diff --
maybe i think it has another environment.it schedule a single executor to a
worker for every application.memory depends on the cores of assigning to
worker,not the config 'spark.executor.memory'. because for application master
assign different cores to executor,but these executors have the same memory.
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