Yikun commented on code in PR #36464:
URL: https://github.com/apache/spark/pull/36464#discussion_r873437306
##########
python/pyspark/pandas/groupby.py:
##########
@@ -2110,22 +2110,60 @@ def _limit(self, n: int, asc: bool) -> FrameLike:
groupkey_scols = [psdf._internal.spark_column_for(label) for label in
groupkey_labels]
sdf = psdf._internal.spark_frame
- tmp_col = verify_temp_column_name(sdf, "__row_number__")
+ window = Window.partitionBy(*groupkey_scols)
# This part is handled differently depending on whether it is a tail
or a head.
- window = (
-
Window.partitionBy(*groupkey_scols).orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).asc())
+ ordered_window = (
+ window.orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).asc())
if asc
- else Window.partitionBy(*groupkey_scols).orderBy(
- F.col(NATURAL_ORDER_COLUMN_NAME).desc()
- )
+ else window.orderBy(F.col(NATURAL_ORDER_COLUMN_NAME).desc())
)
- sdf = (
- sdf.withColumn(tmp_col, F.row_number().over(window))
- .filter(F.col(tmp_col) <= n)
- .drop(tmp_col)
- )
+ if n >= 0 or LooseVersion(pd.__version__) < LooseVersion("1.4.0"):
+ tmp_row_num_col = verify_temp_column_name(sdf, "__row_number__")
+ sdf = (
+ sdf.withColumn(tmp_row_num_col,
F.row_number().over(ordered_window))
+ .filter(F.col(tmp_row_num_col) <= n)
+ .drop(tmp_row_num_col)
+ )
Review Comment:
BTW, we could also consider to unify here to use `lag` way:
```python
sdf = (
sdf.withColumn(tmp_lag_col, F.lag(F.lit(0), n).over(window))
# for positive case
.where(F.isnull(F.col(tmp_lag_col)))
.drop(tmp_lag_col)
)
```
I can submit a separate PR to address it, if you guys think it's necessary.
Theoretically, `lag` has better performance than `row_number` especially when
rows number is very huge.
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