HyukjinKwon commented on code in PR #37995:
URL: https://github.com/apache/spark/pull/37995#discussion_r982998003
##########
python/pyspark/pandas/series.py:
##########
@@ -6442,6 +6445,8 @@ def argmin(self, axis: Axis = None, skipna: bool = True)
-> int:
raise ValueError("axis can only be 0 or 'index'")
sdf = self._internal.spark_frame.select(self.spark.column,
NATURAL_ORDER_COLUMN_NAME)
seq_col_name = verify_temp_column_name(sdf,
"__distributed_sequence_column__")
+
+ cached = sdf.cache()
Review Comment:
One option is to truncate the plan by explicitly materializing the RDD. For
example,
```python
DataFrame(spark._jsparkSession.internalCreateDataFrame(df._jdf.queryExecution().toRdd(),
df._jdf.schema(), False), df.sparkSession)
```
or
```scala
Dataset.ofRows(spark,
org.apache.spark.sql.execution.LogicalRDD(df.queryExecution.executedPlan.output,
df.queryExecution.toRdd)(spark))
```
in Scala side. In this way, it can avoid triggering additional executions by
`zipWithIndex`.
##########
python/pyspark/pandas/series.py:
##########
@@ -6442,6 +6445,8 @@ def argmin(self, axis: Axis = None, skipna: bool = True)
-> int:
raise ValueError("axis can only be 0 or 'index'")
sdf = self._internal.spark_frame.select(self.spark.column,
NATURAL_ORDER_COLUMN_NAME)
seq_col_name = verify_temp_column_name(sdf,
"__distributed_sequence_column__")
+
+ cached = sdf.cache()
Review Comment:
cc @ueshin FYI
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