Get preferred locations for RDD’s partitions

When DAGScheduler submits a Stage for execution, it fetches the preferred locations (TaskLocations) to run tasks on partitions for a RDD from BlockManagerMaster which in turn reach out to  the driver’s RPC endpoint for the infos.  TaskLocation is either a host or (host, executorID) pair. The purpose is to achieve data locality when running a task. It is always preferred to run task on a host which has the required data block in memory.

DAGScheduler class has a getCacheLocs method. In this method, DAGScheduler will first check if the preferred locations (TaskLocation) has been fetched before by checking its internal cacheLocs hashMap. If the internal cacheLocs does not contain the information for the RDD, it will then call blockManagerMaster.getLocations to fetch the preferred locations for all the partitions within the RDD. Once fetched, task locations will be kept in the cacheLocs hashMap.

See DAGScheduler’s getCacheLocs method below.

private[scheduler]
  def getCacheLocs(rdd: RDD[_]): IndexedSeq[Seq[TaskLocation]] = cacheLocs.synchronized {
    // Note: this doesn't use `getOrElse()` because this method is called O(num tasks) times
    if (!cacheLocs.contains(rdd.id)) {
      // Note: if the storage level is NONE, we don't need to get locations from block manager.
      val locs: IndexedSeq[Seq[TaskLocation]] = if (rdd.getStorageLevel == StorageLevel.NONE) {
        IndexedSeq.fill(rdd.partitions.length)(Nil)
      } else {
        val blockIds =
          rdd.partitions.indices.map(index => RDDBlockId(rdd.id, index)).toArray[BlockId]
        blockManagerMaster.getLocations(blockIds).map { bms =>
          bms.map(bm => TaskLocation(bm.host, bm.executorId))
        }
      }
      cacheLocs(rdd.id) = locs
    }
    cacheLocs(rdd.id)
  }
/**
   * Recursive implementation for getPreferredLocs.
   *
   * This method is thread-safe because it only accesses DAGScheduler state through thread-safe
   * methods (getCacheLocs()); please be careful when modifying this method, because any new
   * DAGScheduler state accessed by it may require additional synchronization.
   */
  private def getPreferredLocsInternal(
      rdd: RDD[_],
      partition: Int,
      visited: HashSet[(RDD[_], Int)]): Seq[TaskLocation] = {
    // If the partition has already been visited, no need to re-visit.
    // This avoids exponential path exploration.  SPARK-695
    if (!visited.add((rdd, partition))) {
      // Nil has already been returned for previously visited partitions.
      return Nil
    }
    // If the partition is cached, return the cache locations
    val cached = getCacheLocs(rdd)(partition)
    if (cached.nonEmpty) {
      return cached
    }
    // If the RDD has some placement preferences (as is the case for input RDDs), get those
    val rddPrefs = rdd.preferredLocations(rdd.partitions(partition)).toList
    if (rddPrefs.nonEmpty) {
      return rddPrefs.map(TaskLocation(_))
    }

    // If the RDD has narrow dependencies, pick the first partition of the first narrow dependency
    // that has any placement preferences. Ideally we would choose based on transfer sizes,
    // but this will do for now.
    rdd.dependencies.foreach {
      case n: NarrowDependency[_] =>
        for (inPart <- n.getParents(partition)) { val locs = getPreferredLocsInternal(n.rdd, inPart, visited)           if (locs != Nil) {             return locs           }         }       case _ =>
    }

    Nil
  }

In BlockManagerMaster, getLocations method sends a RPC request to driver end point to get the preferredLocations.


  /** Get locations of multiple blockIds from the driver */
  def getLocations(blockIds: Array[BlockId]): IndexedSeq[Seq[BlockManagerId]] = {
    driverEndpoint.askSync[IndexedSeq[Seq[BlockManagerId]]](
      GetLocationsMultipleBlockIds(blockIds))
  }

 

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