Github user HyukjinKwon commented on a diff in the pull request:
https://github.com/apache/spark/pull/19459#discussion_r145293860
--- Diff: python/pyspark/sql/session.py ---
@@ -414,6 +415,39 @@ def _createFromLocal(self, data, schema):
data = [schema.toInternal(row) for row in data]
return self._sc.parallelize(data), schema
+ def _createFromPandasWithArrow(self, pdf, schema):
+ """
+ Create a DataFrame from a given pandas.DataFrame by slicing it
into partitions, converting
+ 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 ArrowSerializer
+ from pyspark.sql.types import from_arrow_schema, to_arrow_schema
+ import pyarrow as pa
+
+ # Slice the DataFrame into batches
+ step = -(-len(pdf) // self.sparkContext.defaultParallelism) #
round int up
+ pdf_slices = (pdf[start:start + step] for start in xrange(0,
len(pdf), step))
+ arrow_schema = to_arrow_schema(schema) if schema is not None else
None
+ batches = [pa.RecordBatch.from_pandas(pdf_slice,
schema=arrow_schema, preserve_index=False)
+ for pdf_slice in pdf_slices]
--- End diff --
However, if we go 1. way, I think we should avoid creating whole batches
first. I think falling back might make sense if its cost is cheap.
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