casgie commented on code in PR #54180:
URL: https://github.com/apache/spark/pull/54180#discussion_r2787711246
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python/docs/source/tutorial/sql/python_data_source.rst:
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@@ -534,14 +535,14 @@ The following example demonstrates how to implement a
basic Data Source using Ar
class ArrowBatchDataSourceReader(DataSourceReader):
def __init__(self, schema, options):
self.schema: str = schema
+ self.arrow_schema = to_arrow_schema(self.schema)
self.options = options
def read(self, partition):
# Create Arrow Record Batch
keys = pa.array([1, 2, 3, 4, 5], type=pa.int32())
values = pa.array(["one", "two", "three", "four", "five"],
type=pa.string())
- schema = pa.schema([("key", pa.int32()), ("value", pa.string())])
- record_batch = pa.RecordBatch.from_arrays([keys, values],
schema=schema)
+ record_batch = pa.RecordBatch.from_arrays([keys, values],
schema=self.arrow_schema)
Review Comment:
Yes, that is the core idea of this PR, to avoid specifying the arrow schema
**and** the PySpark schema.
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