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Posted to reviews@spark.apache.org by "allisonwang-db (via GitHub)" <gi...@apache.org> on 2023/09/11 22:08:08 UTC
[GitHub] [spark] allisonwang-db opened a new pull request, #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
allisonwang-db opened a new pull request, #42875:
URL: https://github.com/apache/spark/pull/42875
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This PR improves the docstring of the function `inline` by adding more examples.
### Why are the changes needed?
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To improve PySpark documentation.
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No
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doctest
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1322291131
##########
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:
yes, because both `structlist` and `nested` are `array<struct>`, but `structlist.nested` is `array<array<<struct>>` (this is a SQL feature)
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "HyukjinKwon (via GitHub)" <gi...@apache.org>.
HyukjinKwon commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1322290305
##########
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:
```
>>> df.select("structlist.nested").show()
+------------------+
| nested|
+------------------+
|[[{3, 4}, {5, 6}]]|
+------------------+
```
it fails because `structlist.nested` is an array of an array of structs.
```
>>> 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()
+---+---+
| c| d|
+---+---+
| 3| 4|
| 5| 6|
+---+---+
```
works because `nested` is an array of structs (?)
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1322283002
##########
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 [25]: df = spark.sql("SELECT ARRAY(STRUCT(a,b,c)) AS x FROM VALUES (1, 1.1, 'a'), (2, 2.2, 'b'), (4, 4.4, 'c') AS TAB(a, b,
...: c)")
In [26]: df
Out[26]: DataFrame[x: array<struct<a:int,b:decimal(2,1),c:string>>]
In [27]: df.createOrReplaceTempView("v")
In [28]: df.printSchema()
root
|-- x: array (nullable = false)
| |-- element: struct (containsNull = false)
| | |-- a: integer (nullable = false)
| | |-- b: decimal(2,1) (nullable = false)
| | |-- c: string (nullable = false)
In [29]: spark.sql("SELECT x.a FROM v").printSchema()
root
|-- a: array (nullable = false)
| |-- element: integer (containsNull = false)
```
@allisonwang-db I think this is at least consistent with SQL, so should be fine.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1322291131
##########
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:
yes, because both `structlist` and `nested` are `array<struct>`, but ``structlist.nested` is `array<array<<struct>>` (this is a SQL feature)
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[GitHub] [spark] allisonwang-db commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1325163642
##########
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:
I see. It's just strange to me that the type of `structlist.nested` is array<array<struct>>.
##########
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:
I see. It's just strange to me that the type of `structlist.nested` is `array<array<struct>>`.
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[GitHub] [spark] allisonwang-db commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1325163942
##########
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)])])
Review Comment:
Make sense!
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
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 reference resolution?
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[GitHub] [spark] zhengruifeng closed pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng closed pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
URL: https://github.com/apache/spark/pull/42875
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
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)
In [17]: df.select(df.structlist.a).printSchema()
root
|-- structlist.a: array (nullable = true)
| |-- element: long (containsNull = true)
```
~~I guess there is something wrong in column reference resolution?~~
`structlist` is an array of structs `struct<a, b, nested>`, actually I don't expect `structlist.a` can work. But it seems that `structlist.a` is still an array.
Maybe this is a feature? I am not aware of this before
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
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)
In [17]: df.select(df.structlist.a).printSchema()
root
|-- structlist.a: array (nullable = true)
| |-- element: long (containsNull = true)
```
~~I guess there is something wrong in column reference resolution?~~
`structlist` is an array of structs `struct<a, b, nested>`, actually I don't expect `structlist.a` can work. It seems that `structlist.a` is still an array.
Maybe this is a feature? I am not aware of this usage
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
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)
In [17]: df.select(df.structlist.a).printSchema()
root
|-- structlist.a: array (nullable = true)
| |-- element: long (containsNull = true)
```
~~I guess there is something wrong in column reference resolution?~~
`structlist` is an array of structs `struct<a, b, nested>`, actually I don't expect `structlist.a` can work. It seems that `structlist.a` is still an array.
Maybe this is a feature? I am not aware of this before
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[GitHub] [spark] zhengruifeng commented on pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #42875:
URL: https://github.com/apache/spark/pull/42875#issuecomment-1719261355
merged to master
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
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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[GitHub] [spark] allisonwang-db commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "allisonwang-db (via GitHub)" <gi...@apache.org>.
allisonwang-db commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1322120390
##########
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:
cc @zhengruifeng @HyukjinKwon Interestingly, if I directly use `df.select(sf.inline("structlist.nested"))` It throws an exception:
`
pyspark.errors.exceptions.captured.AnalysisException: [DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE] Cannot resolve "inline(structlist.nested)" due to data type mismatch: Parameter 1 requires the "ARRAY<STRUCT>" type, however "structlist.nested" has the type "ARRAY<ARRAY<STRUCT<c: BIGINT, d: BIGINT>>>".;
`
Is this expected? Why is the schema of "nested" inferred as array<array<struct>>?
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1322283002
##########
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 [44]: df = spark.sql("SELECT ARRAY(STRUCT(a,ARRAY(STRUCT(b,c)))) AS x FROM VALUES (1, 1.1, 'a'), (2, 2.2, 'b'), (4, 4.4, 'c'
...: ) AS TAB(a, b, c)")
In [45]: df.createOrReplaceTempView("v")
In [46]: df.printSchema()
root
|-- x: array (nullable = false)
| |-- element: struct (containsNull = false)
| | |-- a: integer (nullable = false)
| | |-- col2: array (nullable = false)
| | | |-- element: struct (containsNull = false)
| | | | |-- b: decimal(2,1) (nullable = false)
| | | | |-- c: string (nullable = false)
In [47]: spark.sql("SELECT x FROM v").printSchema()
root
|-- x: array (nullable = false)
| |-- element: struct (containsNull = false)
| | |-- a: integer (nullable = false)
| | |-- col2: array (nullable = false)
| | | |-- element: struct (containsNull = false)
| | | | |-- b: decimal(2,1) (nullable = false)
| | | | |-- c: string (nullable = false)
In [48]: spark.sql("SELECT x.col2 FROM v").printSchema()
root
|-- col2: array (nullable = false)
| |-- element: array (containsNull = false)
| | |-- element: struct (containsNull = false)
| | | |-- b: decimal(2,1) (nullable = false)
| | | |-- c: string (nullable = false)
In [49]: spark.sql("SELECT INLINE(x.col2) FROM v")
...
AnalysisException: [DATATYPE_MISMATCH.UNEXPECTED_INPUT_TYPE] Cannot resolve "inline(x.col2)" due to data type mismatch: Parameter 1 requires the "ARRAY<STRUCT>" type, however "x.col2" has the type "ARRAY<ARRAY<STRUCT<b: DECIMAL(2,1), c: STRING>>>".; line 1 pos 7;
'Project [unresolvedalias(inline(x#904.col2), None)]
+- SubqueryAlias v
+- View (`v`, [x#904])
+- Project [array(struct(a, a#905, col2, array(struct(b, b#906, c, c#907)))) AS x#904]
+- SubqueryAlias TAB
+- LocalRelation [a#905, b#906, c#907]
```
@allisonwang-db I think this is at least consistent with SQL, so should be fine.
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[GitHub] [spark] zhengruifeng commented on a diff in pull request #42875: [SPARK-45119][PYTHON][DOCS] Refine docstring of inline
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on code in PR #42875:
URL: https://github.com/apache/spark/pull/42875#discussion_r1322288692
##########
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)])])
Review Comment:
```suggestion
... Row(structlist=Row(a=1, b=2, nested=[Row(c=3, d=4), Row(c=5, d=6)]))
```
if you would like to use `df.select(sf.inline("structlist.nested"))`, you can remove the `[ ]` to make it a `struct` instead of `array<struct>`
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