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[SPARK-24976][PYTHON] Allow None for Decimal type conversion (specific to PyArrow 0.9.0) #21928

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[SPARK-24976][PYTHON] Allow None for Decimal type conversion (specific to PyArrow 0.9.0) #21928

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10 changes: 8 additions & 2 deletions python/pyspark/serializers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -216,9 +216,10 @@ def _create_batch(series, timezone):
:param timezone: A timezone to respect when handling timestamp values
:return: Arrow RecordBatch
"""

from pyspark.sql.types import _check_series_convert_timestamps_internal
import decimal
from distutils.version import LooseVersion
import pyarrow as pa
from pyspark.sql.types import _check_series_convert_timestamps_internal
# Make input conform to [(series1, type1), (series2, type2), ...]
if not isinstance(series, (list, tuple)) or \
(len(series) == 2 and isinstance(series[1], pa.DataType)):
Expand All @@ -236,6 +237,11 @@ def create_array(s, t):
# TODO: need decode before converting to Arrow in Python 2
return pa.Array.from_pandas(s.apply(
lambda v: v.decode("utf-8") if isinstance(v, str) else v), mask=mask, type=t)
elif t is not None and pa.types.is_decimal(t) and \
LooseVersion("0.9.0") <= LooseVersion(pa.__version__) < LooseVersion("0.10.0"):
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@felixcheung felixcheung Jul 31, 2018

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consider a single place to check pyarrow versions?

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@HyukjinKwon HyukjinKwon Jul 31, 2018

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Yea, but not sure if I am aware of other issues specific to PyArrow versions. Will make a single place if I happen to fix things specific to PyArrow versions for sure.

# TODO: see ARROW-2432. Remove when the minimum PyArrow version becomes 0.10.0.
return pa.Array.from_pandas(s.apply(
lambda v: decimal.Decimal('NaN') if v is None else v), mask=mask, type=t)
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@felixcheung felixcheung Jul 31, 2018

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add test?

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@HyukjinKwon HyukjinKwon Jul 31, 2018 •
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existing test should test this test_vectorized_udf_null_decimal. This is failed without the current change when PyArrow 0.9.0 is used.

return pa.Array.from_pandas(s, mask=mask, type=t)

arrs = [create_array(s, t) for s, t in series]
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