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new d53ba6276f4 [SPARK-41436][CONNECT][PYTHON] Implement `collection`
functions: A~C
d53ba6276f4 is described below
commit d53ba6276f407b6e090c9610b85a56d047e56f73
Author: Ruifeng Zheng <ruife...@apache.org>
AuthorDate: Thu Dec 8 16:59:13 2022 +0800
[SPARK-41436][CONNECT][PYTHON] Implement `collection` functions: A~C
### What changes were proposed in this pull request?
Implement [`collection`
functions](https://github.com/apache/spark/blob/master/python/docs/source/reference/pyspark.sql/functions.rst#collection-functions)
alphabetically, this PR contains `A` ~ `C` except:
- `aggregate`, `array_sort` - need the support of LambdaFunction Expression
- the `int count` in `array_repeat` - need to support datatype in
LiteralExpression in the Python Client
### Why are the changes needed?
For API coverage
### Does this PR introduce _any_ user-facing change?
new APIs
### How was this patch tested?
added UT
Closes #38961 from zhengruifeng/connect_function_collect_1.
Authored-by: Ruifeng Zheng <ruife...@apache.org>
Signed-off-by: Ruifeng Zheng <ruife...@apache.org>
---
python/pyspark/sql/connect/functions.py | 608 ++++++++++++++++++++-
.../sql/tests/connect/test_connect_function.py | 156 ++++++
2 files changed, 763 insertions(+), 1 deletion(-)
diff --git a/python/pyspark/sql/connect/functions.py
b/python/pyspark/sql/connect/functions.py
index 8b36647ae5b..7eb17bd89ac 100644
--- a/python/pyspark/sql/connect/functions.py
+++ b/python/pyspark/sql/connect/functions.py
@@ -90,7 +90,10 @@ column = col
def lit(col: Any) -> Column:
- return Column(LiteralExpression(col))
+ if isinstance(col, Column):
+ return col
+ else:
+ return Column(LiteralExpression(col))
# def bitwiseNOT(col: "ColumnOrName") -> Column:
@@ -3208,6 +3211,609 @@ def variance(col: "ColumnOrName") -> Column:
return var_samp(col)
+# Collection Functions
+
+
+# TODO(SPARK-41434): need to support LambdaFunction Expression first
+# def aggregate(
+# col: "ColumnOrName",
+# initialValue: "ColumnOrName",
+# merge: Callable[[Column, Column], Column],
+# finish: Optional[Callable[[Column], Column]] = None,
+# ) -> Column:
+# """
+# Applies a binary operator to an initial state and all elements in the
array,
+# and reduces this to a single state. The final state is converted into
the final result
+# by applying a finish function.
+#
+# Both functions can use methods of :class:`~pyspark.sql.Column`,
functions defined in
+# :py:mod:`pyspark.sql.functions` and Scala ``UserDefinedFunctions``.
+# Python ``UserDefinedFunctions`` are not supported
+# (`SPARK-27052 <https://issues.apache.org/jira/browse/SPARK-27052>`__).
+#
+# .. versionadded:: 3.1.0
+#
+# Parameters
+# ----------
+# col : :class:`~pyspark.sql.Column` or str
+# name of column or expression
+# initialValue : :class:`~pyspark.sql.Column` or str
+# initial value. Name of column or expression
+# merge : function
+# a binary function ``(acc: Column, x: Column) -> Column...``
returning expression
+# of the same type as ``zero``
+# finish : function
+# an optional unary function ``(x: Column) -> Column: ...``
+# used to convert accumulated value.
+#
+# Returns
+# -------
+# :class:`~pyspark.sql.Column`
+# final value after aggregate function is applied.
+#
+# Examples
+# --------
+# >>> df = spark.createDataFrame([(1, [20.0, 4.0, 2.0, 6.0, 10.0])],
("id", "values"))
+# >>> df.select(aggregate("values", lit(0.0), lambda acc, x: acc +
x).alias("sum")).show()
+# +----+
+# | sum|
+# +----+
+# |42.0|
+# +----+
+#
+# >>> def merge(acc, x):
+# ... count = acc.count + 1
+# ... sum = acc.sum + x
+# ... return struct(count.alias("count"), sum.alias("sum"))
+# >>> df.select(
+# ... aggregate(
+# ... "values",
+# ... struct(lit(0).alias("count"), lit(0.0).alias("sum")),
+# ... merge,
+# ... lambda acc: acc.sum / acc.count,
+# ... ).alias("mean")
+# ... ).show()
+# +----+
+# |mean|
+# +----+
+# | 8.4|
+# +----+
+# """
+# if finish is not None:
+# return _invoke_higher_order_function("ArrayAggregate", [col,
initialValue],
+# [merge, finish])
+#
+# else:
+# return _invoke_higher_order_function("ArrayAggregate", [col,
initialValue],
+# [merge])
+
+
+def array(*cols: Union["ColumnOrName", List["ColumnOrName"],
Tuple["ColumnOrName", ...]]) -> Column:
+ """Creates a new array column.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ cols : :class:`~pyspark.sql.Column` or str
+ column names or :class:`~pyspark.sql.Column`\\s that have
+ the same data type.
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ a column of array type.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], ("name", "age"))
+ >>> df.select(array('age', 'age').alias("arr")).collect()
+ [Row(arr=[2, 2]), Row(arr=[5, 5])]
+ >>> df.select(array([df.age, df.age]).alias("arr")).collect()
+ [Row(arr=[2, 2]), Row(arr=[5, 5])]
+ >>> df.select(array('age', 'age').alias("col")).printSchema()
+ root
+ |-- col: array (nullable = false)
+ | |-- element: long (containsNull = true)
+ """
+ if len(cols) == 1 and isinstance(cols[0], (list, set, tuple)):
+ cols = cols[0] # type: ignore[assignment]
+ return _invoke_function_over_columns("array", *cols) # type:
ignore[arg-type]
+
+
+def array_contains(col: "ColumnOrName", value: Any) -> Column:
+ """
+ Collection function: returns null if the array is null, true if the array
contains the
+ given value, and false otherwise.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ name of column containing array
+ value :
+ value or column to check for in array
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ a column of Boolean type.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([(["a", "b", "c"],), ([],)], ['data'])
+ >>> df.select(array_contains(df.data, "a")).collect()
+ [Row(array_contains(data, a)=True), Row(array_contains(data, a)=False)]
+ >>> df.select(array_contains(df.data, lit("a"))).collect()
+ [Row(array_contains(data, a)=True), Row(array_contains(data, a)=False)]
+ """
+ return _invoke_function("array_contains", _to_col(col), lit(value))
+
+
+def array_distinct(col: "ColumnOrName") -> Column:
+ """
+ Collection function: removes duplicate values from the array.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ name of column or expression
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ an array of unique values.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([([1, 2, 3, 2],), ([4, 5, 5, 4],)],
['data'])
+ >>> df.select(array_distinct(df.data)).collect()
+ [Row(array_distinct(data)=[1, 2, 3]), Row(array_distinct(data)=[4, 5])]
+ """
+ return _invoke_function_over_columns("array_distinct", col)
+
+
+def array_except(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
+ """
+ Collection function: returns an array of the elements in col1 but not in
col2,
+ without duplicates.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col1 : :class:`~pyspark.sql.Column` or str
+ name of column containing array
+ col2 : :class:`~pyspark.sql.Column` or str
+ name of column containing array
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ an array of values from first array that are not in the second.
+
+ Examples
+ --------
+ >>> from pyspark.sql import Row
+ >>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a",
"f"])])
+ >>> df.select(array_except(df.c1, df.c2)).collect()
+ [Row(array_except(c1, c2)=['b'])]
+ """
+ return _invoke_function_over_columns("array_except", col1, col2)
+
+
+def array_intersect(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
+ """
+ Collection function: returns an array of the elements in the intersection
of col1 and col2,
+ without duplicates.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col1 : :class:`~pyspark.sql.Column` or str
+ name of column containing array
+ col2 : :class:`~pyspark.sql.Column` or str
+ name of column containing array
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ an array of values in the intersection of two arrays.
+
+ Examples
+ --------
+ >>> from pyspark.sql import Row
+ >>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a",
"f"])])
+ >>> df.select(array_intersect(df.c1, df.c2)).collect()
+ [Row(array_intersect(c1, c2)=['a', 'c'])]
+ """
+ return _invoke_function_over_columns("array_intersect", col1, col2)
+
+
+def array_join(
+ col: "ColumnOrName", delimiter: str, null_replacement: Optional[str] = None
+) -> Column:
+ """
+ Concatenates the elements of `column` using the `delimiter`. Null values
are replaced with
+ `null_replacement` if set, otherwise they are ignored.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ target column to work on.
+ delimiter : str
+ delimiter used to concatenate elements
+ null_replacement : str, optional
+ if set then null values will be replaced by this value
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ a column of string type. Concatenated values.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([(["a", "b", "c"],), (["a", None],)],
['data'])
+ >>> df.select(array_join(df.data, ",").alias("joined")).collect()
+ [Row(joined='a,b,c'), Row(joined='a')]
+ >>> df.select(array_join(df.data, ",", "NULL").alias("joined")).collect()
+ [Row(joined='a,b,c'), Row(joined='a,NULL')]
+ """
+ if null_replacement is None:
+ return _invoke_function("array_join", _to_col(col), lit(delimiter))
+ else:
+ return _invoke_function("array_join", _to_col(col), lit(delimiter),
lit(null_replacement))
+
+
+def array_max(col: "ColumnOrName") -> Column:
+ """
+ Collection function: returns the maximum value of the array.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ name of column or expression
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ maximum value of an array.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([([2, 1, 3],), ([None, 10, -1],)], ['data'])
+ >>> df.select(array_max(df.data).alias('max')).collect()
+ [Row(max=3), Row(max=10)]
+ """
+ return _invoke_function_over_columns("array_max", col)
+
+
+def array_min(col: "ColumnOrName") -> Column:
+ """
+ Collection function: returns the minimum value of the array.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ name of column or expression
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ minimum value of array.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([([2, 1, 3],), ([None, 10, -1],)], ['data'])
+ >>> df.select(array_min(df.data).alias('min')).collect()
+ [Row(min=1), Row(min=-1)]
+ """
+ return _invoke_function_over_columns("array_min", col)
+
+
+def array_position(col: "ColumnOrName", value: Any) -> Column:
+ """
+ Collection function: Locates the position of the first occurrence of the
given value
+ in the given array. Returns null if either of the arguments are null.
+
+ .. versionadded:: 3.4.0
+
+ Notes
+ -----
+ The position is not zero based, but 1 based index. Returns 0 if the given
+ value could not be found in the array.
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ target column to work on.
+ value : Any
+ value to look for.
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ position of the value in the given array if found and 0 otherwise.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([(["c", "b", "a"],), ([],)], ['data'])
+ >>> df.select(array_position(df.data, "a")).collect()
+ [Row(array_position(data, a)=3), Row(array_position(data, a)=0)]
+ """
+ return _invoke_function("array_position", _to_col(col), lit(value))
+
+
+def array_remove(col: "ColumnOrName", element: Any) -> Column:
+ """
+ Collection function: Remove all elements that equal to element from the
given array.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ name of column containing array
+ element :
+ element to be removed from the array
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ an array excluding given value.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([([1, 2, 3, 1, 1],), ([],)], ['data'])
+ >>> df.select(array_remove(df.data, 1)).collect()
+ [Row(array_remove(data, 1)=[2, 3]), Row(array_remove(data, 1)=[])]
+ """
+ return _invoke_function("array_remove", _to_col(col), lit(element))
+
+
+def array_repeat(col: "ColumnOrName", count: Union["ColumnOrName", int]) ->
Column:
+ """
+ Collection function: creates an array containing a column repeated count
times.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col : :class:`~pyspark.sql.Column` or str
+ column name or column that contains the element to be repeated
+ count : :class:`~pyspark.sql.Column` or str or int
+ column name, column, or int containing the number of times to repeat
the first argument
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ an array of repeated elements.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([('ab',)], ['data'])
+ >>> df.select(array_repeat(df.data, 3).alias('r')).collect()
+ [Row(r=['ab', 'ab', 'ab'])]
+ """
+ _count = lit(count) if isinstance(count, int) else _to_col(count)
+
+ return _invoke_function("array_repeat", _to_col(col), _count)
+
+
+# TODO(SPARK-41434): need to support LambdaFunction Expression first
+# def array_sort(
+# col: "ColumnOrName", comparator: Optional[Callable[[Column, Column],
Column]] = None
+# ) -> Column:
+# """
+# Collection function: sorts the input array in ascending order. The
elements of the input array
+# must be orderable. Null elements will be placed at the end of the
returned array.
+#
+# .. versionadded:: 2.4.0
+# .. versionchanged:: 3.4.0
+# Can take a `comparator` function.
+#
+# Parameters
+# ----------
+# col : :class:`~pyspark.sql.Column` or str
+# name of column or expression
+# comparator : callable, optional
+# A binary ``(Column, Column) -> Column: ...``.
+# The comparator will take two
+# arguments representing two elements of the array. It returns a
negative integer, 0, or a
+# positive integer as the first element is less than, equal to, or
greater than the second
+# element. If the comparator function returns null, the function will
fail and raise an
+# error.
+#
+# Returns
+# -------
+# :class:`~pyspark.sql.Column`
+# sorted array.
+#
+# Examples
+# --------
+# >>> df = spark.createDataFrame([([2, 1, None, 3],),([1],),([],)],
['data'])
+# >>> df.select(array_sort(df.data).alias('r')).collect()
+# [Row(r=[1, 2, 3, None]), Row(r=[1]), Row(r=[])]
+# >>> df = spark.createDataFrame([(["foo", "foobar", None,
"bar"],),(["foo"],),([],)], ['data'])
+# >>> df.select(array_sort(
+# ... "data",
+# ... lambda x, y: when(x.isNull() | y.isNull(),
lit(0)).otherwise(length(y) - length(x))
+# ... ).alias("r")).collect()
+# [Row(r=['foobar', 'foo', None, 'bar']), Row(r=['foo']), Row(r=[])]
+# """
+# if comparator is None:
+# return _invoke_function_over_columns("array_sort", col)
+# else:
+# return _invoke_higher_order_function("ArraySort", [col],
[comparator])
+
+
+def array_union(col1: "ColumnOrName", col2: "ColumnOrName") -> Column:
+ """
+ Collection function: returns an array of the elements in the union of col1
and col2,
+ without duplicates.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ col1 : :class:`~pyspark.sql.Column` or str
+ name of column containing array
+ col2 : :class:`~pyspark.sql.Column` or str
+ name of column containing array
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ an array of values in union of two arrays.
+
+ Examples
+ --------
+ >>> from pyspark.sql import Row
+ >>> df = spark.createDataFrame([Row(c1=["b", "a", "c"], c2=["c", "d", "a",
"f"])])
+ >>> df.select(array_union(df.c1, df.c2)).collect()
+ [Row(array_union(c1, c2)=['b', 'a', 'c', 'd', 'f'])]
+ """
+ return _invoke_function_over_columns("array_union", col1, col2)
+
+
+def arrays_overlap(a1: "ColumnOrName", a2: "ColumnOrName") -> Column:
+ """
+ Collection function: returns true if the arrays contain any common
non-null element; if not,
+ returns null if both the arrays are non-empty and any of them contains a
null element; returns
+ false otherwise.
+
+ .. versionadded:: 3.4.0
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ a column of Boolean type.
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([(["a", "b"], ["b", "c"]), (["a"], ["b",
"c"])], ['x', 'y'])
+ >>> df.select(arrays_overlap(df.x, df.y).alias("overlap")).collect()
+ [Row(overlap=True), Row(overlap=False)]
+ """
+ return _invoke_function_over_columns("arrays_overlap", a1, a2)
+
+
+def arrays_zip(*cols: "ColumnOrName") -> Column:
+ """
+ Collection function: Returns a merged array of structs in which the N-th
struct contains all
+ N-th values of input arrays. If one of the arrays is shorter than others
then
+ resulting struct type value will be a `null` for missing elements.
+
+ .. versionadded:: 2.4.0
+
+ Parameters
+ ----------
+ cols : :class:`~pyspark.sql.Column` or str
+ columns of arrays to be merged.
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ merged array of entries.
+
+ Examples
+ --------
+ >>> from pyspark.sql.functions import arrays_zip
+ >>> df = spark.createDataFrame([(([1, 2, 3], [2, 4, 6], [3, 6]))],
['vals1', 'vals2', 'vals3'])
+ >>> df = df.select(arrays_zip(df.vals1, df.vals2,
df.vals3).alias('zipped'))
+ >>> df.show(truncate=False)
+ +------------------------------------+
+ |zipped |
+ +------------------------------------+
+ |[{1, 2, 3}, {2, 4, 6}, {3, 6, null}]|
+ +------------------------------------+
+ >>> df.printSchema()
+ root
+ |-- zipped: array (nullable = true)
+ | |-- element: struct (containsNull = false)
+ | | |-- vals1: long (nullable = true)
+ | | |-- vals2: long (nullable = true)
+ | | |-- vals3: long (nullable = true)
+ """
+ return _invoke_function_over_columns("arrays_zip", *cols)
+
+
+def concat(*cols: "ColumnOrName") -> Column:
+ """
+ Concatenates multiple input columns together into a single column.
+ The function works with strings, numeric, binary and compatible array
columns.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ cols : :class:`~pyspark.sql.Column` or str
+ target column or columns to work on.
+
+ Returns
+ -------
+ :class:`~pyspark.sql.Column`
+ concatenated values. Type of the `Column` depends on input columns'
type.
+
+ See Also
+ --------
+ :meth:`pyspark.sql.functions.array_join` : to concatenate string columns
with delimiter
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([('abcd','123')], ['s', 'd'])
+ >>> df = df.select(concat(df.s, df.d).alias('s'))
+ >>> df.collect()
+ [Row(s='abcd123')]
+ >>> df
+ DataFrame[s: string]
+
+ >>> df = spark.createDataFrame([([1, 2], [3, 4], [5]), ([1, 2], None,
[3])], ['a', 'b', 'c'])
+ >>> df = df.select(concat(df.a, df.b, df.c).alias("arr"))
+ >>> df.collect()
+ [Row(arr=[1, 2, 3, 4, 5]), Row(arr=None)]
+ >>> df
+ DataFrame[arr: array<bigint>]
+ """
+ return _invoke_function_over_columns("concat", *cols)
+
+
+def create_map(
+ *cols: Union["ColumnOrName", List["ColumnOrName"], Tuple["ColumnOrName",
...]]
+) -> Column:
+ """Creates a new map column.
+
+ .. versionadded:: 3.4.0
+
+ Parameters
+ ----------
+ cols : :class:`~pyspark.sql.Column` or str
+ column names or :class:`~pyspark.sql.Column`\\s that are
+ grouped as key-value pairs, e.g. (key1, value1, key2, value2, ...).
+
+ Examples
+ --------
+ >>> df = spark.createDataFrame([("Alice", 2), ("Bob", 5)], ("name", "age"))
+ >>> df.select(create_map('name', 'age').alias("map")).collect()
+ [Row(map={'Alice': 2}), Row(map={'Bob': 5})]
+ >>> df.select(create_map([df.name, df.age]).alias("map")).collect()
+ [Row(map={'Alice': 2}), Row(map={'Bob': 5})]
+ """
+ if len(cols) == 1 and isinstance(cols[0], (list, set, tuple)):
+ cols = cols[0] # type: ignore[assignment]
+ return _invoke_function_over_columns("map", *cols) # type:
ignore[arg-type]
+
+
# String/Binary functions
diff --git a/python/pyspark/sql/tests/connect/test_connect_function.py
b/python/pyspark/sql/tests/connect/test_connect_function.py
index ee3a927708e..96801c58ca9 100644
--- a/python/pyspark/sql/tests/connect/test_connect_function.py
+++ b/python/pyspark/sql/tests/connect/test_connect_function.py
@@ -63,6 +63,24 @@ class SparkConnectFunctionTests(SparkConnectFuncTestCase):
"""These test cases exercise the interface to the proto plan
generation but do not call Spark."""
+ def compare_by_show(self, df1: Any, df2: Any):
+ from pyspark.sql.dataframe import DataFrame as SDF
+ from pyspark.sql.connect.dataframe import DataFrame as CDF
+
+ assert isinstance(df1, (SDF, CDF))
+ if isinstance(df1, SDF):
+ str1 = df1._jdf.showString(20, 20, False)
+ else:
+ str1 = df1._show_string(20, 20, False)
+
+ assert isinstance(df2, (SDF, CDF))
+ if isinstance(df2, SDF):
+ str2 = df2._jdf.showString(20, 20, False)
+ else:
+ str2 = df2._show_string(20, 20, False)
+
+ self.assertEqual(str1, str2)
+
def test_normal_functions(self):
from pyspark.sql import functions as SF
from pyspark.sql.connect import functions as CF
@@ -428,6 +446,144 @@ class SparkConnectFunctionTests(SparkConnectFuncTestCase):
.toPandas(),
)
+ def test_collection_functions(self):
+ from pyspark.sql import functions as SF
+ from pyspark.sql.connect import functions as CF
+
+ query = """
+ SELECT * FROM VALUES
+ (ARRAY('a', 'ab'), ARRAY(1, 2, 3), ARRAY(1, NULL, 3), 1, 2, 'a'),
+ (ARRAY('x', NULL), NULL, ARRAY(1, 3), 3, 4, 'x'),
+ (NULL, ARRAY(-1, -2, -3), Array(), 5, 6, NULL)
+ AS tab(a, b, c, d, e, f)
+ """
+ # +---------+------------+------------+---+---+----+
+ # | a| b| c| d| e| f|
+ # +---------+------------+------------+---+---+----+
+ # | [a, ab]| [1, 2, 3]|[1, null, 3]| 1| 2| a|
+ # |[x, null]| null| [1, 3]| 3| 4| x|
+ # | null|[-1, -2, -3]| []| 5| 6|null|
+ # +---------+------------+------------+---+---+----+
+
+ cdf = self.connect.sql(query)
+ sdf = self.spark.sql(query)
+
+ for cfunc, sfunc in [
+ (CF.array_distinct, SF.array_distinct),
+ (CF.array_max, SF.array_max),
+ (CF.array_min, SF.array_min),
+ ]:
+ self.assert_eq(
+ cdf.select(cfunc("a"), cfunc(cdf.b)).toPandas(),
+ sdf.select(sfunc("a"), sfunc(sdf.b)).toPandas(),
+ )
+
+ for cfunc, sfunc in [
+ (CF.array_except, SF.array_except),
+ (CF.array_intersect, SF.array_intersect),
+ (CF.array_union, SF.array_union),
+ (CF.arrays_overlap, SF.arrays_overlap),
+ ]:
+ self.assert_eq(
+ cdf.select(cfunc("b", cdf.c)).toPandas(),
+ sdf.select(sfunc("b", sdf.c)).toPandas(),
+ )
+
+ for cfunc, sfunc in [
+ (CF.array_position, SF.array_position),
+ (CF.array_remove, SF.array_remove),
+ ]:
+ self.assert_eq(
+ cdf.select(cfunc(cdf.a, "ab")).toPandas(),
+ sdf.select(sfunc(sdf.a, "ab")).toPandas(),
+ )
+
+ # test array
+ self.assert_eq(
+ cdf.select(CF.array(cdf.d, "e")).toPandas(),
+ sdf.select(SF.array(sdf.d, "e")).toPandas(),
+ )
+ self.assert_eq(
+ cdf.select(CF.array(cdf.d, "e", CF.lit(99))).toPandas(),
+ sdf.select(SF.array(sdf.d, "e", SF.lit(99))).toPandas(),
+ )
+
+ # test array_contains
+ self.assert_eq(
+ cdf.select(CF.array_contains(cdf.a, "ab")).toPandas(),
+ sdf.select(SF.array_contains(sdf.a, "ab")).toPandas(),
+ )
+ self.assert_eq(
+ cdf.select(CF.array_contains(cdf.a, cdf.f)).toPandas(),
+ sdf.select(SF.array_contains(sdf.a, sdf.f)).toPandas(),
+ )
+
+ # test array_join
+ self.assert_eq(
+ cdf.select(
+ CF.array_join(cdf.a, ","), CF.array_join("b", ":"),
CF.array_join("c", "~")
+ ).toPandas(),
+ sdf.select(
+ SF.array_join(sdf.a, ","), SF.array_join("b", ":"),
SF.array_join("c", "~")
+ ).toPandas(),
+ )
+ self.assert_eq(
+ cdf.select(
+ CF.array_join(cdf.a, ",", "_null_"),
+ CF.array_join("b", ":", ".null."),
+ CF.array_join("c", "~", "NULL"),
+ ).toPandas(),
+ sdf.select(
+ SF.array_join(sdf.a, ",", "_null_"),
+ SF.array_join("b", ":", ".null."),
+ SF.array_join("c", "~", "NULL"),
+ ).toPandas(),
+ )
+
+ # test array_repeat
+ self.assert_eq(
+ cdf.select(CF.array_repeat(cdf.f, "d")).toPandas(),
+ sdf.select(SF.array_repeat(sdf.f, "d")).toPandas(),
+ )
+ self.assert_eq(
+ cdf.select(CF.array_repeat("f", cdf.d)).toPandas(),
+ sdf.select(SF.array_repeat("f", sdf.d)).toPandas(),
+ )
+ # TODO: Make Literal contains DataType
+ # Cannot resolve "array_repeat(f, 3)" due to data type mismatch:
+ # Parameter 2 requires the "INT" type, however "3" has the type
"BIGINT".
+ # self.assert_eq(
+ # cdf.select(CF.array_repeat("f", 3)).toPandas(),
+ # sdf.select(SF.array_repeat("f", 3)).toPandas(),
+ # )
+
+ # test arrays_zip
+ # TODO: Make toPandas support complex nested types like Array<Struct>
+ # DataFrame.iloc[:, 0] (column name="arrays_zip(b, c)") values are
different (66.66667 %)
+ # [index]: [0, 1, 2]
+ # [left]: [[{'b': 1, 'c': 1.0}, {'b': 2, 'c': None}, {'b': 3, 'c':
3.0}], None,
+ # [{'b': -1, 'c': None}, {'b': -2, 'c': None}, {'b': -3,
'c': None}]]
+ # [right]: [[(1, 1), (2, None), (3, 3)], None, [(-1, None), (-2,
None), (-3, None)]]
+ self.compare_by_show(
+ cdf.select(CF.arrays_zip(cdf.b, "c")),
+ sdf.select(SF.arrays_zip(sdf.b, "c")),
+ )
+
+ # test concat
+ self.assert_eq(
+ cdf.select(CF.concat("d", cdf.e, CF.lit(-1))).toPandas(),
+ sdf.select(SF.concat("d", sdf.e, SF.lit(-1))).toPandas(),
+ )
+
+ # test create_map
+ self.compare_by_show(
+ cdf.select(CF.create_map(cdf.d, cdf.e)),
sdf.select(SF.create_map(sdf.d, sdf.e))
+ )
+ self.compare_by_show(
+ cdf.select(CF.create_map(cdf.d, "e", "e", CF.lit(1))),
+ sdf.select(SF.create_map(sdf.d, "e", "e", SF.lit(1))),
+ )
+
def test_string_functions(self):
from pyspark.sql import functions as SF
from pyspark.sql.connect import functions as CF
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