Ngone51 commented on a change in pull request #31876:
URL: https://github.com/apache/spark/pull/31876#discussion_r618118932
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File path: core/src/main/scala/org/apache/spark/scheduler/MapStatus.scala
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@@ -52,8 +55,41 @@ private[spark] sealed trait MapStatus {
* partitionId of the task or taskContext.taskAttemptId is used.
*/
def mapId: Long
+
}
+private[spark] class MapStatusLocationFactory(conf: SparkConf) {
+ private val locationExtension = classOf[Location]
+ private val (locationConstructor, locationName) = {
+ conf.get(config.SHUFFLE_LOCATION_PLUGIN_CLASS).map { className =>
+ val clazz = Utils.classForName(className)
+ require(locationExtension.isAssignableFrom(clazz),
+ s"$className is not a subclass of ${locationExtension.getName}.")
+ (clazz.getConstructor(), className)
+ }.orNull
+ }
+
+ private lazy val locationCache: LoadingCache[Location, Location] =
CacheBuilder.newBuilder()
+ .maximumSize(10000)
+ .build(
+ new CacheLoader[Location, Location]() {
+ override def load(loc: Location): Location = loc
+ }
+ )
+
+ def load(in: ObjectInput): Location = {
+ try {
+ Option(locationConstructor).map { ctr =>
+ val loc = ctr.newInstance().asInstanceOf[Location]
+ loc.readExternal(in)
+ locationCache.get(loc)
Review comment:
The cache mimics the `blockManagerIdCache`.
It's for reducing the Java objects of the same location in the driver.
Ideally, the cache intends to provide the singleton instance for the same
location.
For example, a 10 executors' cluster could have a task with 100 partitions.
So, generally, every 10 partitions would share the same location. Without the
cache, we will have 100 objects but only 10 with the cache.
I can add a comment for it later.
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