SPARK-3211 .take() is OOM-prone with empty partitions

Instead of jumping straight from 1 partition to all partitions, do exponential
growth and double the number of partitions to attempt each time instead.

Fix proposed by Paul Nepywoda

Author: Andrew Ash <[email protected]>

Closes apache#2117 from ash211/SPARK-3211 and squashes the following commits:

8b2299a [Andrew Ash] Quadruple instead of double for a minor speedup
e5f7e4d [Andrew Ash] Update comment to better reflect what we're doing
09a27f7 [Andrew Ash] Update PySpark to be less OOM-prone as well
3a156b8 [Andrew Ash] SPARK-3211 .take() is OOM-prone with empty partitions

core/src/main/scala/org/apache/spark/rdd/RDD.scala

@@ -1064,11 +1064,10 @@ abstract class RDD[T: ClassTag](
       // greater than totalParts because we actually cap it at totalParts in runJob.
       var numPartsToTry = 1
       if (partsScanned > 0) {
-        // If we didn't find any rows after the first iteration, just try all partitions next.
-        // Otherwise, interpolate the number of partitions we need to try, but overestimate it
-        // by 50%.
+        // If we didn't find any rows after the previous iteration, quadruple and retry.  Otherwise,
+        // interpolate the number of partitions we need to try, but overestimate it by 50%.
         if (buf.size == 0) {
-          numPartsToTry = totalParts - 1
+          numPartsToTry = partsScanned * 4
         } else {
           numPartsToTry = (1.5 * num * partsScanned / buf.size).toInt
         }

python/pyspark/rdd.py

@@ -1089,11 +1089,11 @@ def take(self, num):
             # we actually cap it at totalParts in runJob.
             numPartsToTry = 1
             if partsScanned > 0:
-                # If we didn't find any rows after the first iteration, just
-                # try all partitions next. Otherwise, interpolate the number
-                # of partitions we need to try, but overestimate it by 50%.
+                # If we didn't find any rows after the previous iteration,
+                # quadruple and retry.  Otherwise, interpolate the number of
+                # partitions we need to try, but overestimate it by 50%.
                 if len(items) == 0:
-                    numPartsToTry = totalParts - 1
+                    numPartsToTry = partsScanned * 4
                 else:
                     numPartsToTry = int(1.5 * num * partsScanned / len(items))