Spark 1246 add min max to stat counter

core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala

@@ -477,6 +477,16 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable {
     new java.util.ArrayList(arr)
   }
 
+  def max(comp: Comparator[T]): T = {
+    import scala.collection.JavaConversions._
+    rdd.max()(Ordering.comparatorToOrdering(comp))
+  }
+
+  def min(comp: Comparator[T]): T = {
+    import scala.collection.JavaConversions._
+    rdd.min()(Ordering.comparatorToOrdering(comp))
+  }
+
   /**
    * Returns the first K elements from this RDD using the
    * natural ordering for T while maintain the order.

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

@@ -958,6 +958,10 @@ abstract class RDD[T: ClassTag](
    */
   def takeOrdered(num: Int)(implicit ord: Ordering[T]): Array[T] = top(num)(ord.reverse)
 
+  def max()(implicit ord: Ordering[T]):T = this.reduce{(x,y) => ord.max(x,y)}
+
+  def min()(implicit ord: Ordering[T]):T = this.reduce{(x,y) => ord.min(x,y)}
+
   /**
    * Save this RDD as a text file, using string representations of elements.
    */

core/src/main/scala/org/apache/spark/util/StatCounter.scala

@@ -29,6 +29,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
   private var n: Long = 0     // Running count of our values
   private var mu: Double = 0  // Running mean of our values
   private var m2: Double = 0  // Running variance numerator (sum of (x - mean)^2)
+  private var max_v: Double = 0 // Running max of our values
+  private var min_v: Double = 0 // Running min of our values
 
   merge(values)
 
@@ -41,6 +43,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
     n += 1
     mu += delta / n
     m2 += delta * (value - mu)
+    max_v = math.max(max_v, value)
+    min_v = math.min(min_v, value)
     this
   }
 
@@ -58,7 +62,9 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
       if (n == 0) {
         mu = other.mu
         m2 = other.m2
-        n = other.n       
+        n = other.n  
+        max_v = other.max_v
+        min_v = other.min_v
       } else if (other.n != 0) {        
         val delta = other.mu - mu
         if (other.n * 10 < n) {
@@ -70,6 +76,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
         }
         m2 += other.m2 + (delta * delta * n * other.n) / (n + other.n)
         n += other.n
+        max_v = math.max(max_v, other.max_v)
+        min_v = math.min(min_v, other.min_v)
       }
       this
     }
@@ -81,6 +89,8 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
     other.n = n
     other.mu = mu
     other.m2 = m2
+    other.max_v = max_v
+    other.min_v = min_v
     other
   }
 
@@ -90,6 +100,10 @@ class StatCounter(values: TraversableOnce[Double]) extends Serializable {
 
   def sum: Double = n * mu
 
+  def max: Double = max_v
+
+  def min: Double = min_v
+
   /** Return the variance of the values. */
   def variance: Double = {
     if (n == 0) {

core/src/test/scala/org/apache/spark/PartitioningSuite.scala

@@ -171,6 +171,8 @@ class PartitioningSuite extends FunSuite with SharedSparkContext with PrivateMet
     assert(abs(6.0/2 - rdd.mean) < 0.01)
     assert(abs(1.0 - rdd.variance) < 0.01)
     assert(abs(1.0 - rdd.stdev) < 0.01)
+    assert(abs(4.0 - stats.max) === 0)
+    assert(abs(-1.0 - stats.max) === 0)
 
     // Add other tests here for classes that should be able to handle empty partitions correctly
   }

core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala

@@ -47,6 +47,8 @@ class RDDSuite extends FunSuite with SharedSparkContext {
     assert(nums.glom().map(_.toList).collect().toList === List(List(1, 2), List(3, 4)))
     assert(nums.collect({ case i if i >= 3 => i.toString }).collect().toList === List("3", "4"))
     assert(nums.keyBy(_.toString).collect().toList === List(("1", 1), ("2", 2), ("3", 3), ("4", 4)))
+    assert(nums.max() === 4)
+    assert(nums.min() === 1)
     val partitionSums = nums.mapPartitions(iter => Iterator(iter.reduceLeft(_ + _)))
     assert(partitionSums.collect().toList === List(3, 7))
 

python/pyspark/rdd.py

@@ -534,7 +534,26 @@ def func(iterator):
         return reduce(op, vals, zeroValue)
 
     # TODO: aggregate
+
+
+    def max(self):
+        """
+        Find the maximum item in this RDD.
+
+        >>> sc.parallelize([1.0, 5.0, 43.0, 10.0]).max()
+        43.0
+        """
+        return self.stats().max()
 
+    def min(self):
+        """
+        Find the maximum item in this RDD.
+
+        >>> sc.parallelize([1.0, 5.0, 43.0, 10.0]).min()
+        1.0
+        """
+        return self.stats().min()
+
     def sum(self):
         """
         Add up the elements in this RDD.

python/pyspark/statcounter.py

@@ -26,7 +26,9 @@ def __init__(self, values=[]):
         self.n = 0L    # Running count of our values
         self.mu = 0.0  # Running mean of our values
         self.m2 = 0.0  # Running variance numerator (sum of (x - mean)^2)
-
+        self.max_v = float("-inf")
+        self.min_v = float("inf")
+
         for v in values:
             self.merge(v)
 
@@ -36,6 +38,11 @@ def merge(self, value):
         self.n += 1
         self.mu += delta / self.n
         self.m2 += delta * (value - self.mu)
+        if self.max_v < value:
+            self.max_v = value
+        if self.min_v > value:
+            self.min_v = value
+
         return self
 
     # Merge another StatCounter into this one, adding up the internal statistics.
@@ -49,7 +56,10 @@ def mergeStats(self, other):
             if self.n == 0:
                 self.mu = other.mu
                 self.m2 = other.m2
-                self.n = other.n       
+                self.n = other.n
+                self.max_v = other.max_v
+                self.min_v = other.min_v
+
             elif other.n != 0:        
                 delta = other.mu - self.mu
                 if other.n * 10 < self.n:
@@ -58,6 +68,9 @@ def mergeStats(self, other):
                     self.mu = other.mu - (delta * self.n) / (self.n + other.n)
                 else:
                     self.mu = (self.mu * self.n + other.mu * other.n) / (self.n + other.n)
+
+                    self.max_v = max(self.max_v, other.max_v)
+                    self.min_v = min(self.min_v, other.min_v)
 
                 self.m2 += other.m2 + (delta * delta * self.n * other.n) / (self.n + other.n)
                 self.n += other.n
@@ -76,6 +89,12 @@ def mean(self):
     def sum(self):
         return self.n * self.mu
 
+    def min(self):
+        return self.min_v
+
+    def max(self):
+        return self.max_v
+
     # Return the variance of the values.
     def variance(self):
         if self.n == 0: