Avoid performance impact of a single partition mode in Spark window functions

Question

My question is triggered by the use case of calculating the differences between consecutive rows in a spark dataframe.

For example, I have:

>>> df.show()
+-----+----------+
|index|      col1|
+-----+----------+
|  0.0|0.58734024|
|  1.0|0.67304325|
|  2.0|0.85154736|
|  3.0| 0.5449719|
+-----+----------+

If I choose to calculate these using "Window" functions, then I can do that like so:

>>> winSpec = Window.partitionBy(df.index >= 0).orderBy(df.index.asc())
>>> import pyspark.sql.functions as f
>>> df.withColumn('diffs_col1', f.lag(df.col1, -1).over(winSpec) - df.col1).show()
+-----+----------+-----------+
|index|      col1| diffs_col1|
+-----+----------+-----------+
|  0.0|0.58734024|0.085703015|
|  1.0|0.67304325| 0.17850411|
|  2.0|0.85154736|-0.30657548|
|  3.0| 0.5449719|       null|
+-----+----------+-----------+

Question: I explicitly partitioned the dataframe in a single partition. What is the performance impact of this and, if there is, why is that so and how could I avoid it? Because when I do not specify a partition, I get the following warning:

16/12/24 13:52:27 WARN WindowExec: No Partition Defined for Window operation! Moving all data to a single partition, this can cause serious performance degradation.