BryanCutler commented on a change in pull request #24095: [SPARK-27163][PYTHON]
Cleanup and consolidate Pandas UDF functionality
URL: https://github.com/apache/spark/pull/24095#discussion_r267539397
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File path: python/pyspark/sql/session.py
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@@ -530,15 +530,29 @@ def _create_from_pandas_with_arrow(self, pdf, schema,
timezone):
to Arrow data, then sending to the JVM to parallelize. If a schema is
passed in, the
data types will be used to coerce the data in Pandas to Arrow
conversion.
"""
- from pyspark.serializers import ArrowStreamSerializer, _create_batch
- from pyspark.sql.types import from_arrow_schema, to_arrow_type,
TimestampType
+ from distutils.version import LooseVersion
+ from pyspark.serializers import ArrowStreamPandasSerializer
+ from pyspark.sql.types import from_arrow_type, to_arrow_type,
TimestampType
from pyspark.sql.utils import require_minimum_pandas_version, \
require_minimum_pyarrow_version
require_minimum_pandas_version()
require_minimum_pyarrow_version()
from pandas.api.types import is_datetime64_dtype, is_datetime64tz_dtype
+ import pyarrow as pa
+
+ # Create the Spark schema from list of names passed in with Arrow types
+ if isinstance(schema, (list, tuple)):
+ if LooseVersion(pa.__version__) < LooseVersion("0.12.0"):
+ temp_batch = pa.RecordBatch.from_pandas(pdf[0:100],
preserve_index=False)
Review comment:
I'm not too thrilled with creating a record batch just to get the Arrow
schema, but this was the most reliable way I could figure to do it pre v0.12.0.
I will propose bumping the pyarrow version soon, and then this could be
removed.
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