zhengruifeng commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1322274339
##########
python/pyspark/sql/functions.py:
##########
@@ -12451,37 +12451,132 @@ def inline(col: "ColumnOrName") -> Column:
"""
Explodes an array of structs into a table.
+ This function takes an input column containing an array of structs and
returns a
+ new column where each struct in the array is exploded into a separate row.
+
.. versionadded:: 3.4.0
Parameters
----------
col : :class:`~pyspark.sql.Column` or str
- input column of values to explode.
+ Input column of values to explode.
Returns
-------
:class:`~pyspark.sql.Column`
- generator expression with the inline exploded result.
+ Generator expression with the inline exploded result.
See Also
--------
- :meth:`explode`
-
- Notes
- -----
- Supports Spark Connect.
+ :meth:`pyspark.functions.explode`
+ :meth:`pyspark.functions.inline_outer`
Examples
--------
+ Example 1: Using inline with a single struct array column
+
+ >>> import pyspark.sql.functions as sf
+ >>> from pyspark.sql import Row
+ >>> df = spark.createDataFrame([Row(structlist=[Row(a=1, b=2), Row(a=3,
b=4)])])
+ >>> df.select(sf.inline(df.structlist)).show()
+ +---+---+
+ | a| b|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+ Example 2: Using inline with a column name
+
+ >>> import pyspark.sql.functions as sf
>>> from pyspark.sql import Row
>>> df = spark.createDataFrame([Row(structlist=[Row(a=1, b=2), Row(a=3,
b=4)])])
- >>> df.select(inline(df.structlist)).show()
+ >>> df.select(sf.inline("structlist")).show()
+---+---+
| a| b|
+---+---+
| 1| 2|
| 3| 4|
+---+---+
+
+ Example 3: Using inline with an alias
+
+ >>> import pyspark.sql.functions as sf
+ >>> from pyspark.sql import Row
+ >>> df = spark.createDataFrame([Row(structlist=[Row(a=1, b=2), Row(a=3,
b=4)])])
+ >>> df.select(sf.inline("structlist").alias("c1", "c2")).show()
+ +---+---+
+ | c1| c2|
+ +---+---+
+ | 1| 2|
+ | 3| 4|
+ +---+---+
+
+ Example 4: Using inline with multiple struct array columns
+
+ >>> import pyspark.sql.functions as sf
+ >>> from pyspark.sql import Row
+ >>> df = spark.createDataFrame([
+ ... Row(structlist1=[Row(a=1, b=2), Row(a=3, b=4)],
+ ... structlist2=[Row(c=5, d=6), Row(c=7, d=8)])
+ ... ])
+ >>> df.select(sf.inline("structlist1"), "structlist2") \
+ ... .select("a", "b", sf.inline("structlist2")).show()
+ +---+---+---+---+
+ | a| b| c| d|
+ +---+---+---+---+
+ | 1| 2| 5| 6|
+ | 1| 2| 7| 8|
+ | 3| 4| 5| 6|
+ | 3| 4| 7| 8|
+ +---+---+---+---+
+
+ Example 5: Using inline with a nested struct array column
+
+ >>> import pyspark.sql.functions as sf
+ >>> from pyspark.sql import Row
+ >>> df = spark.createDataFrame([
+ ... Row(structlist=[Row(a=1, b=2, nested=[Row(c=3, d=4), Row(c=5,
d=6)])])
+ ... ])
+ >>> df.select(sf.inline("structlist")).show()
+ +---+---+----------------+
+ | a| b| nested|
+ +---+---+----------------+
+ | 1| 2|[{3, 4}, {5, 6}]|
+ +---+---+----------------+
+ >>> df.select(sf.inline("structlist")).select(sf.inline("nested")).show()
Review Comment:
```
In [14]: df.select("structlist").printSchema()
root
|-- structlist: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- a: long (nullable = true)
| | |-- b: long (nullable = true)
| | |-- nested: array (nullable = true)
| | | |-- element: struct (containsNull = true)
| | | | |-- c: long (nullable = true)
| | | | |-- d: long (nullable = true)
In [15]: df.select("structlist.nested").printSchema()
root
|-- nested: array (nullable = true)
| |-- element: array (containsNull = true)
| | |-- element: struct (containsNull = true)
| | | |-- c: long (nullable = true)
| | | |-- d: long (nullable = true)
In [16]: df.select(df.structlist.nested).printSchema()
root
|-- structlist.nested: array (nullable = true)
| |-- element: array (containsNull = true)
| | |-- element: struct (containsNull = true)
| | | |-- c: long (nullable = true)
| | | |-- d: long (nullable = true)
```
I guess there is something wrong in column resolution?
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