[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38462: [SPARK-40533] [CONNECT] [PYTHON] Support most built-in literal types for Python in Spark Connect
HyukjinKwon commented on code in PR #38462:
URL: https://github.com/apache/spark/pull/38462#discussion_r1013933320
##
python/pyspark/sql/connect/column.py:
##
@@ -99,11 +101,59 @@ def to_plan(self, session: Optional["RemoteSparkSession"])
-> "proto.Expression"
value_type = type(self._value)
exp = proto.Expression()
if value_type is int:
-exp.literal.i32 = cast(int, self._value)
+exp.literal.i64 = cast(int, self._value)
+elif value_type is bool:
+exp.literal.boolean = cast(bool, self._value)
elif value_type is str:
exp.literal.string = cast(str, self._value)
elif value_type is float:
exp.literal.fp64 = cast(float, self._value)
+elif value_type is decimal.Decimal:
+d_v = cast(decimal.Decimal, self._value)
+v_tuple = d_v.as_tuple()
+exp.literal.decimal.scale = abs(v_tuple.exponent)
+exp.literal.decimal.precision = len(v_tuple.digits) -
abs(v_tuple.exponent)
+# Two complement yeah...
+raise ValueError("Python Decimal not supported.")
+elif value_type is bytes:
+exp.literal.binary = self._value
+elif value_type is datetime.datetime:
+# Microseconds since epoch.
+dt = cast(datetime.datetime, self._value)
+v = dt - datetime.datetime(1970, 1, 1, 0, 0, 0, 0)
+exp.literal.timestamp = int(v / datetime.timedelta(microseconds=1))
Review Comment:
Can we maybe match the implementation in PySpark's:
https://github.com/apache/spark/blob/master/python/pyspark/sql/types.py#L254-L260
?
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38462: [SPARK-40533] [CONNECT] [PYTHON] Support most built-in literal types for Python in Spark Connect
HyukjinKwon commented on code in PR #38462:
URL: https://github.com/apache/spark/pull/38462#discussion_r1013930085
##
python/pyspark/sql/connect/column.py:
##
@@ -99,11 +101,59 @@ def to_plan(self, session: Optional["RemoteSparkSession"])
-> "proto.Expression"
value_type = type(self._value)
exp = proto.Expression()
if value_type is int:
-exp.literal.i32 = cast(int, self._value)
+exp.literal.i64 = cast(int, self._value)
+elif value_type is bool:
+exp.literal.boolean = cast(bool, self._value)
elif value_type is str:
exp.literal.string = cast(str, self._value)
elif value_type is float:
exp.literal.fp64 = cast(float, self._value)
+elif value_type is decimal.Decimal:
+d_v = cast(decimal.Decimal, self._value)
+v_tuple = d_v.as_tuple()
+exp.literal.decimal.scale = abs(v_tuple.exponent)
+exp.literal.decimal.precision = len(v_tuple.digits) -
abs(v_tuple.exponent)
+# Two complement yeah...
+raise ValueError("Python Decimal not supported.")
+elif value_type is bytes:
+exp.literal.binary = self._value
+elif value_type is datetime.datetime:
+# Microseconds since epoch.
+dt = cast(datetime.datetime, self._value)
+v = dt - datetime.datetime(1970, 1, 1, 0, 0, 0, 0)
+exp.literal.timestamp = int(v / datetime.timedelta(microseconds=1))
+elif value_type is datetime.time:
+# Nanoseconds of the day.
+tv = cast(datetime.time, self._value)
+offset = (tv.second + tv.minute * 60 + tv.hour * 3600) * 1000 +
tv.microsecond
+exp.literal.time = int(offset * 1000)
+elif value_type is datetime.date:
+# Days since epoch.
+days_since_epoch = (cast(datetime.date, self._value) -
datetime.date(1970, 1, 1)).days
+exp.literal.date = days_since_epoch
+elif value_type is uuid.UUID:
Review Comment:
Maybe we could remove `elif` so `else` branch throw the exception?
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38462: [SPARK-40533] [CONNECT] [PYTHON] Support most built-in literal types for Python in Spark Connect
HyukjinKwon commented on code in PR #38462:
URL: https://github.com/apache/spark/pull/38462#discussion_r1013928764
##
python/pyspark/sql/connect/column.py:
##
@@ -99,11 +101,59 @@ def to_plan(self, session: Optional["RemoteSparkSession"])
-> "proto.Expression"
value_type = type(self._value)
exp = proto.Expression()
if value_type is int:
-exp.literal.i32 = cast(int, self._value)
+exp.literal.i64 = cast(int, self._value)
+elif value_type is bool:
+exp.literal.boolean = cast(bool, self._value)
elif value_type is str:
exp.literal.string = cast(str, self._value)
elif value_type is float:
exp.literal.fp64 = cast(float, self._value)
+elif value_type is decimal.Decimal:
+d_v = cast(decimal.Decimal, self._value)
+v_tuple = d_v.as_tuple()
+exp.literal.decimal.scale = abs(v_tuple.exponent)
+exp.literal.decimal.precision = len(v_tuple.digits) -
abs(v_tuple.exponent)
+# Two complement yeah...
+raise ValueError("Python Decimal not supported.")
Review Comment:
Can we remove if this is not implemented yet?
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[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38462: [SPARK-40533] [CONNECT] [PYTHON] Support most built-in literal types for Python in Spark Connect
HyukjinKwon commented on code in PR #38462: URL: https://github.com/apache/spark/pull/38462#discussion_r1012491071 ## python/pyspark/sql/connect/_typing.py: ## @@ -15,5 +15,7 @@ # limitations under the License. # from typing import Union +from datetime import date, time, datetime PrimitiveType = Union[str, int, bool, float] +LiteralType = Union[PrimitiveType, Union[date, time, datetime]] Review Comment: Seems not used. -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For queries about this service, please contact Infrastructure at: us...@infra.apache.org - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] [spark] HyukjinKwon commented on a diff in pull request #38462: [SPARK-40533] [CONNECT] [PYTHON] Support most built-in literal types for Python in Spark Connect
HyukjinKwon commented on code in PR #38462:
URL: https://github.com/apache/spark/pull/38462#discussion_r1012489260
##
python/pyspark/sql/connect/column.py:
##
@@ -99,11 +101,59 @@ def to_plan(self, session: Optional["RemoteSparkSession"])
-> "proto.Expression"
value_type = type(self._value)
exp = proto.Expression()
if value_type is int:
-exp.literal.i32 = cast(int, self._value)
+exp.literal.i64 = cast(int, self._value)
+elif value_type is bool:
+exp.literal.boolean = cast(bool, self._value)
elif value_type is str:
exp.literal.string = cast(str, self._value)
elif value_type is float:
exp.literal.fp64 = cast(float, self._value)
+elif value_type is decimal.Decimal:
+d_v = cast(decimal.Decimal, self._value)
+v_tuple = d_v.as_tuple()
+exp.literal.decimal.scale = abs(v_tuple.exponent)
+exp.literal.decimal.precision = len(v_tuple.digits) -
abs(v_tuple.exponent)
+# Two complement yeah...
+raise ValueError("cannnt")
+elif value_type is bytes:
+exp.literal.binary = self._value
+elif value_type is datetime.datetime:
+# Microseconds since epoch.
+dt = cast(datetime.datetime, self._value)
+v = dt - datetime.datetime(1970, 1, 1, 0, 0, 0, 0)
+exp.literal.timestamp = int(v / datetime.timedelta(microseconds=1))
+elif value_type is datetime.time:
+# Nanoseconds of the day.
+tv = cast(datetime.time, self._value)
+offset = (tv.second + tv.minute * 60 + tv.hour * 3600) * 1000 +
tv.microsecond
+exp.literal.time = int(offset * 1000)
+elif value_type is datetime.date:
+# Days since epoch.
+days_since_epoch = (cast(datetime.date, self._value) -
datetime.date(1970, 1, 1)).days
+exp.literal.date = days_since_epoch
+elif value_type is uuid.UUID:
Review Comment:
Hm, this isn't actually supported in current PySpark's `lit`. Should we
maybe exclude this for now?
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