Github user CodingCat commented on a diff in the pull request:
https://github.com/apache/spark/pull/731#discussion_r28365419
--- Diff: core/src/main/scala/org/apache/spark/deploy/master/Master.scala
---
@@ -524,52 +524,25 @@ private[master] class Master(
}
/**
- * Can an app use the given worker? True if the worker has enough memory
and we haven't already
- * launched an executor for the app on it (right now the standalone
backend doesn't like having
- * two executors on the same worker).
+ * Schedule executors to be launched on the workers.There are two modes
of launching executors.
+ * The first attempts to spread out an application's executors on as
many workers as possible,
+ * while the second does the opposite (i.e. launch them on as few
workers as possible). The former
+ * is usually better for data locality purposes and is the default. The
number of cores assigned
+ * to each executor is configurable. When this is explicitly set,
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.
*/
- private def canUse(app: ApplicationInfo, worker: WorkerInfo): Boolean = {
- worker.memoryFree >= app.desc.memoryPerSlave &&
!worker.hasExecutor(app)
- }
-
- /**
- * Schedule the currently available resources among waiting apps. This
method will be called
- * every time a new app joins or resource availability changes.
- */
- private def schedule() {
- if (state != RecoveryState.ALIVE) { return }
-
- // First schedule drivers, they take strict precedence over
applications
- // Randomization helps balance drivers
- val shuffledAliveWorkers = Random.shuffle(workers.toSeq.filter(_.state
== WorkerState.ALIVE))
- val numWorkersAlive = shuffledAliveWorkers.size
- var curPos = 0
-
- for (driver <- waitingDrivers.toList) { // iterate over a copy of
waitingDrivers
- // We assign workers to each waiting driver in a round-robin
fashion. For each driver, we
- // start from the last worker that was assigned a driver, and
continue onwards until we have
- // explored all alive workers.
- var launched = false
- var numWorkersVisited = 0
- while (numWorkersVisited < numWorkersAlive && !launched) {
- val worker = shuffledAliveWorkers(curPos)
- numWorkersVisited += 1
- if (worker.memoryFree >= driver.desc.mem && worker.coresFree >=
driver.desc.cores) {
- launchDriver(worker, driver)
- waitingDrivers -= driver
- launched = true
- }
- curPos = (curPos + 1) % numWorkersAlive
- }
- }
-
+ 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.
if (spreadOutApps) {
- // Try to spread out each app among all the nodes, until it has all
its cores
+ // 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(canUse(app, _)).sortBy(_.coresFree).reverse
+ .filter(worker => worker.memoryFree >=
app.desc.memoryPerExecutorMB &&
+ worker.coresFree > 0)
--- End diff --
should be `worker.coresFree >= app.desc.coresPerExecutor.getOrElse(1)`
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