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Posted to reviews@spark.apache.org by "ueshin (via GitHub)" <gi...@apache.org> on 2023/05/20 00:08:13 UTC
[GitHub] [spark] ueshin opened a new pull request, #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
ueshin opened a new pull request, #41240:
URL: https://github.com/apache/spark/pull/41240
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[GitHub] [spark] ueshin commented on a diff in pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #41240:
URL: https://github.com/apache/spark/pull/41240#discussion_r1203029027
##########
python/pyspark/sql/pandas/conversion.py:
##########
@@ -375,22 +379,105 @@ def _convert_from_pandas(
assert isinstance(self, SparkSession)
if timezone is not None:
- from pyspark.sql.pandas.types import _check_series_convert_timestamps_tz_local
+ from pyspark.sql.pandas.types import (
+ _check_series_convert_timestamps_tz_local,
+ _get_local_timezone,
+ )
from pandas.core.dtypes.common import is_datetime64tz_dtype, is_timedelta64_dtype
copied = False
if isinstance(schema, StructType):
- for field in schema:
- # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
- if isinstance(field.dataType, TimestampType):
- s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
- if s is not pdf[field.name]:
- if not copied:
- # Copy once if the series is modified to prevent the original
- # Pandas DataFrame from being updated
- pdf = pdf.copy()
- copied = True
- pdf[field.name] = s
+
+ def _create_converter(data_type: DataType) -> Callable[[pd.Series], pd.Series]:
+ if isinstance(data_type, TimestampType):
+
+ def correct_timestamp(pser: pd.Series) -> pd.Series:
+ return _check_series_convert_timestamps_tz_local(pser, timezone)
+
+ return correct_timestamp
+
+ def _converter(dt: DataType) -> Optional[Callable[[Any], Any]]:
+ if isinstance(dt, ArrayType):
+ element_conv = _converter(dt.elementType) or (lambda x: x)
+
+ def convert_array(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return [element_conv(v) for v in value]
+
+ return convert_array
+
+ elif isinstance(dt, MapType):
+ key_conv = _converter(dt.keyType) or (lambda x: x)
+ value_conv = _converter(dt.valueType) or (lambda x: x)
+
+ def convert_map(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return {key_conv(k): value_conv(v) for k, v in value.items()}
+
+ return convert_map
+
+ elif isinstance(dt, StructType):
+ field_names = dt.names
+ dedup_field_names = _dedup_names(field_names)
+ field_convs = [
+ _converter(f.dataType) or (lambda x: x) for f in dt.fields
+ ]
+
+ def convert_struct(value: Any) -> Any:
+ if value is None:
+ return None
+ elif isinstance(value, dict):
+ _values = [
+ field_convs[i](value.get(name, None))
+ for i, name in enumerate(dedup_field_names)
+ ]
+ return _create_row(field_names, _values)
+ else:
+ _values = [
+ field_convs[i](value[i]) for i, name in enumerate(value)
+ ]
+ return _create_row(field_names, _values)
+
+ return convert_struct
+
+ elif isinstance(dt, TimestampType):
+
+ def convert_timestamp(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return (
+ pd.Timestamp(value)
+ .tz_localize(timezone, ambiguous=False) # type: ignore
Review Comment:
Let me leave it without the error tag. I see a weird error in CI I can't reproduce in my local:
```
starting mypy annotations test...
annotations failed mypy checks:
python/pyspark/sql/pandas/conversion.py:454: error: Unexpected keyword argument "ambiguous" for "tz_localize" of "Timestamp"; did you mean "ambigious"? [call-arg]
/usr/local/lib/python3.9/dist-packages/pandas/_libs/tslibs/timestamps.pyi:38: note: "tz_localize" of "Timestamp" defined here
python/pyspark/sql/pandas/conversion.py:456: error: unused "type: ignore" comment
Found 2 errors in 1 file (checked 512 source files)
```
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[GitHub] [spark] ueshin commented on a diff in pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #41240:
URL: https://github.com/apache/spark/pull/41240#discussion_r1203029027
##########
python/pyspark/sql/pandas/conversion.py:
##########
@@ -375,22 +379,105 @@ def _convert_from_pandas(
assert isinstance(self, SparkSession)
if timezone is not None:
- from pyspark.sql.pandas.types import _check_series_convert_timestamps_tz_local
+ from pyspark.sql.pandas.types import (
+ _check_series_convert_timestamps_tz_local,
+ _get_local_timezone,
+ )
from pandas.core.dtypes.common import is_datetime64tz_dtype, is_timedelta64_dtype
copied = False
if isinstance(schema, StructType):
- for field in schema:
- # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
- if isinstance(field.dataType, TimestampType):
- s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
- if s is not pdf[field.name]:
- if not copied:
- # Copy once if the series is modified to prevent the original
- # Pandas DataFrame from being updated
- pdf = pdf.copy()
- copied = True
- pdf[field.name] = s
+
+ def _create_converter(data_type: DataType) -> Callable[[pd.Series], pd.Series]:
+ if isinstance(data_type, TimestampType):
+
+ def correct_timestamp(pser: pd.Series) -> pd.Series:
+ return _check_series_convert_timestamps_tz_local(pser, timezone)
+
+ return correct_timestamp
+
+ def _converter(dt: DataType) -> Optional[Callable[[Any], Any]]:
+ if isinstance(dt, ArrayType):
+ element_conv = _converter(dt.elementType) or (lambda x: x)
+
+ def convert_array(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return [element_conv(v) for v in value]
+
+ return convert_array
+
+ elif isinstance(dt, MapType):
+ key_conv = _converter(dt.keyType) or (lambda x: x)
+ value_conv = _converter(dt.valueType) or (lambda x: x)
+
+ def convert_map(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return {key_conv(k): value_conv(v) for k, v in value.items()}
+
+ return convert_map
+
+ elif isinstance(dt, StructType):
+ field_names = dt.names
+ dedup_field_names = _dedup_names(field_names)
+ field_convs = [
+ _converter(f.dataType) or (lambda x: x) for f in dt.fields
+ ]
+
+ def convert_struct(value: Any) -> Any:
+ if value is None:
+ return None
+ elif isinstance(value, dict):
+ _values = [
+ field_convs[i](value.get(name, None))
+ for i, name in enumerate(dedup_field_names)
+ ]
+ return _create_row(field_names, _values)
+ else:
+ _values = [
+ field_convs[i](value[i]) for i, name in enumerate(value)
+ ]
+ return _create_row(field_names, _values)
+
+ return convert_struct
+
+ elif isinstance(dt, TimestampType):
+
+ def convert_timestamp(value: Any) -> Any:
+ if value is None:
+ return None
+ else:
+ return (
+ pd.Timestamp(value)
+ .tz_localize(timezone, ambiguous=False) # type: ignore
Review Comment:
Let me leave the type-ignore comment without the error tag. I see a weird error in CI I can't reproduce in my local:
```
starting mypy annotations test...
annotations failed mypy checks:
python/pyspark/sql/pandas/conversion.py:454: error: Unexpected keyword argument "ambiguous" for "tz_localize" of "Timestamp"; did you mean "ambigious"? [call-arg]
/usr/local/lib/python3.9/dist-packages/pandas/_libs/tslibs/timestamps.pyi:38: note: "tz_localize" of "Timestamp" defined here
python/pyspark/sql/pandas/conversion.py:456: error: unused "type: ignore" comment
Found 2 errors in 1 file (checked 512 source files)
```
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[GitHub] [spark] zhengruifeng closed pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng closed pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
URL: https://github.com/apache/spark/pull/41240
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[GitHub] [spark] BryanCutler commented on a diff in pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "BryanCutler (via GitHub)" <gi...@apache.org>.
BryanCutler commented on code in PR #41240:
URL: https://github.com/apache/spark/pull/41240#discussion_r1205811507
##########
python/pyspark/sql/pandas/conversion.py:
##########
@@ -375,22 +379,105 @@ def _convert_from_pandas(
assert isinstance(self, SparkSession)
if timezone is not None:
- from pyspark.sql.pandas.types import _check_series_convert_timestamps_tz_local
+ from pyspark.sql.pandas.types import (
+ _check_series_convert_timestamps_tz_local,
+ _get_local_timezone,
+ )
from pandas.core.dtypes.common import is_datetime64tz_dtype, is_timedelta64_dtype
copied = False
if isinstance(schema, StructType):
- for field in schema:
- # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
- if isinstance(field.dataType, TimestampType):
- s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
- if s is not pdf[field.name]:
- if not copied:
- # Copy once if the series is modified to prevent the original
- # Pandas DataFrame from being updated
- pdf = pdf.copy()
- copied = True
- pdf[field.name] = s
+
+ def _create_converter(data_type: DataType) -> Callable[[pd.Series], pd.Series]:
Review Comment:
No problem, that makes sense
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[GitHub] [spark] xinrong-meng commented on pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "xinrong-meng (via GitHub)" <gi...@apache.org>.
xinrong-meng commented on PR #41240:
URL: https://github.com/apache/spark/pull/41240#issuecomment-1561769197
nit: the TODO [here](https://github.com/apache/spark/blob/master/python/pyspark/sql/pandas/types.py#LL393C5-L393C74) seems to be removable
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[GitHub] [spark] BryanCutler commented on a diff in pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "BryanCutler (via GitHub)" <gi...@apache.org>.
BryanCutler commented on code in PR #41240:
URL: https://github.com/apache/spark/pull/41240#discussion_r1204658486
##########
python/pyspark/sql/pandas/conversion.py:
##########
@@ -375,22 +379,105 @@ def _convert_from_pandas(
assert isinstance(self, SparkSession)
if timezone is not None:
- from pyspark.sql.pandas.types import _check_series_convert_timestamps_tz_local
+ from pyspark.sql.pandas.types import (
+ _check_series_convert_timestamps_tz_local,
+ _get_local_timezone,
+ )
from pandas.core.dtypes.common import is_datetime64tz_dtype, is_timedelta64_dtype
copied = False
if isinstance(schema, StructType):
- for field in schema:
- # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
- if isinstance(field.dataType, TimestampType):
- s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
- if s is not pdf[field.name]:
- if not copied:
- # Copy once if the series is modified to prevent the original
- # Pandas DataFrame from being updated
- pdf = pdf.copy()
- copied = True
- pdf[field.name] = s
+
+ def _create_converter(data_type: DataType) -> Callable[[pd.Series], pd.Series]:
Review Comment:
Just wondering if you had considered "un-nesting" the Arrow field first, then applying the conversions on flat timestamp fields only, then putting the nested fields back together again? It would be a little more complicated to do this, but would have the benefit of working the same for any level of nested fields, and it's easier to work with Arrow nested fields vs in Pandas.
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[GitHub] [spark] zhengruifeng commented on pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "zhengruifeng (via GitHub)" <gi...@apache.org>.
zhengruifeng commented on PR #41240:
URL: https://github.com/apache/spark/pull/41240#issuecomment-1562176512
merged to master
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[GitHub] [spark] ueshin commented on a diff in pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on code in PR #41240:
URL: https://github.com/apache/spark/pull/41240#discussion_r1204706187
##########
python/pyspark/sql/pandas/conversion.py:
##########
@@ -375,22 +379,105 @@ def _convert_from_pandas(
assert isinstance(self, SparkSession)
if timezone is not None:
- from pyspark.sql.pandas.types import _check_series_convert_timestamps_tz_local
+ from pyspark.sql.pandas.types import (
+ _check_series_convert_timestamps_tz_local,
+ _get_local_timezone,
+ )
from pandas.core.dtypes.common import is_datetime64tz_dtype, is_timedelta64_dtype
copied = False
if isinstance(schema, StructType):
- for field in schema:
- # TODO: handle nested timestamps, such as ArrayType(TimestampType())?
- if isinstance(field.dataType, TimestampType):
- s = _check_series_convert_timestamps_tz_local(pdf[field.name], timezone)
- if s is not pdf[field.name]:
- if not copied:
- # Copy once if the series is modified to prevent the original
- # Pandas DataFrame from being updated
- pdf = pdf.copy()
- copied = True
- pdf[field.name] = s
+
+ def _create_converter(data_type: DataType) -> Callable[[pd.Series], pd.Series]:
Review Comment:
Yes, I had, but I dropped the idea because, in the series of my recent PRs, I wanted to consolidate the conversion logic in various cases, like with/without Arrow, and Spark Connect, or conversions of array, map, and struct types.
Now we can reuse `_create_converter_to_pandas` and `_create_converter_from_pandas` in `pyspark.sql.pandas.types` package in many cases.
The function you pointed was an unfortunate case, I couldn't consolidate only that part. We might need to revisit here.
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[GitHub] [spark] ueshin commented on pull request #41240: [SPARK-43545][SQL][PYTHON] Support nested timestamp type
Posted by "ueshin (via GitHub)" <gi...@apache.org>.
ueshin commented on PR #41240:
URL: https://github.com/apache/spark/pull/41240#issuecomment-1561786519
> nit: the TODO [here](https://github.com/apache/spark/blob/master/python/pyspark/sql/pandas/types.py?rgh-link-date=2023-05-24T18%3A50%3A58Z#LL393C5-L393C74) seems to be removable
I don't think so. The function only support top level timestamp type.
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