From 11ed2b180ec86523a94679a8b8132fadb911ccd5 Mon Sep 17 00:00:00 2001
From: Davies Liu <davies@databricks.com>
Date: Fri, 14 Aug 2015 13:55:29 -0700
Subject: [PATCH] [SPARK-9978] [PYSPARK] [SQL] fix Window.orderBy and doc of
 ntile()

Author: Davies Liu <davies@databricks.com>

Closes #8213 from davies/fix_window.
---
 python/pyspark/sql/functions.py |  7 ++++---
 python/pyspark/sql/tests.py     | 23 +++++++++++++++++++++++
 python/pyspark/sql/window.py    |  2 +-
 3 files changed, 28 insertions(+), 4 deletions(-)

diff --git a/python/pyspark/sql/functions.py b/python/pyspark/sql/functions.py
index e98979533f..41dfee9f54 100644
--- a/python/pyspark/sql/functions.py
+++ b/python/pyspark/sql/functions.py
@@ -530,9 +530,10 @@ def lead(col, count=1, default=None):
 @since(1.4)
 def ntile(n):
     """
-    Window function: returns a group id from 1 to `n` (inclusive) in a round-robin fashion in
-    a window partition. Fow example, if `n` is 3, the first row will get 1, the second row will
-    get 2, the third row will get 3, and the fourth row will get 1...
+    Window function: returns the ntile group id (from 1 to `n` inclusive)
+    in an ordered window partition. Fow example, if `n` is 4, the first
+    quarter of the rows will get value 1, the second quarter will get 2,
+    the third quarter will get 3, and the last quarter will get 4.
 
     This is equivalent to the NTILE function in SQL.
 
diff --git a/python/pyspark/sql/tests.py b/python/pyspark/sql/tests.py
index 38c83c427a..9b748101b5 100644
--- a/python/pyspark/sql/tests.py
+++ b/python/pyspark/sql/tests.py
@@ -1124,5 +1124,28 @@ class HiveContextSQLTests(ReusedPySparkTestCase):
         for r, ex in zip(rs, expected):
             self.assertEqual(tuple(r), ex[:len(r)])
 
+    def test_window_functions_without_partitionBy(self):
+        df = self.sqlCtx.createDataFrame([(1, "1"), (2, "2"), (1, "2"), (1, "2")], ["key", "value"])
+        w = Window.orderBy("key", df.value)
+        from pyspark.sql import functions as F
+        sel = df.select(df.value, df.key,
+                        F.max("key").over(w.rowsBetween(0, 1)),
+                        F.min("key").over(w.rowsBetween(0, 1)),
+                        F.count("key").over(w.rowsBetween(float('-inf'), float('inf'))),
+                        F.rowNumber().over(w),
+                        F.rank().over(w),
+                        F.denseRank().over(w),
+                        F.ntile(2).over(w))
+        rs = sorted(sel.collect())
+        expected = [
+            ("1", 1, 1, 1, 4, 1, 1, 1, 1),
+            ("2", 1, 1, 1, 4, 2, 2, 2, 1),
+            ("2", 1, 2, 1, 4, 3, 2, 2, 2),
+            ("2", 2, 2, 2, 4, 4, 4, 3, 2)
+        ]
+        for r, ex in zip(rs, expected):
+            self.assertEqual(tuple(r), ex[:len(r)])
+
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/python/pyspark/sql/window.py b/python/pyspark/sql/window.py
index c74745c726..eaf4d7e986 100644
--- a/python/pyspark/sql/window.py
+++ b/python/pyspark/sql/window.py
@@ -64,7 +64,7 @@ class Window(object):
         Creates a :class:`WindowSpec` with the partitioning defined.
         """
         sc = SparkContext._active_spark_context
-        jspec = sc._jvm.org.apache.spark.sql.expressions.Window.partitionBy(_to_java_cols(cols))
+        jspec = sc._jvm.org.apache.spark.sql.expressions.Window.orderBy(_to_java_cols(cols))
         return WindowSpec(jspec)