On write operation, cast data to Iceberg Table's pyarrow schema #523
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Fokko
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pyiceberg/table/__init__.py
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| _check_schema(self.schema(), other_schema=df.schema) | ||
| _check_schema_compatible(self.schema(), other_schema=df.schema) | ||
| # the two schemas are compatible so safe to cast | ||
| df = df.cast(self.schema().as_arrow()) |
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Should _check_schema_compatible return a bool to indicate if the cast is needed? I'm not sure how costly the cast is. If we go from string to large_string then we might rewrite the Arrow buffers.
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yea, I like that idea.
_check_schema_compatible returns a boolean should_cast.
- If schema is exactly the same, return
Falseand skip cast - If schema is "compatible", return
Trueand cast - If schema is not "compatible", throws an error
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It was too complicated when _check_schema_compatible returned a boolean and threw an error.
I ended up doing an extra comparison as Arrow schemas outside and cast only if necessary
Fokko
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Mar 28, 2024
| _check_schema(self.schema(), other_schema=df.schema) | ||
| _check_schema_compatible(self.schema(), other_schema=df.schema) | ||
| # cast if the two schemas are compatible but not equal | ||
| if self.schema().as_arrow() != df.schema: |
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nit: It would be good to call as_arrow() just once in case we need to cast.
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Backport to 0.6.1
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Backport to 0.6.1
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…ma (#559) * Cast data to Iceberg Table's pyarrow schema (#523) Backport to 0.6.1 * use schema_to_pyarrow directly for backporting * remove print in test --------- Co-authored-by: Kevin Liu <[email protected]>
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This PR resolves #520 by casting the incoming pyarrow
Tableto the same Schema as the Iceberg Table. This is safe since we first check for schema compatibility._check_schema->_check_schema_compatible.Schema.as_arrow().