Spark DataFrames handled as a type if using spark #267
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- This is also an overhaul of the schema system to support remote IO based dataframes and regular pandas style dataframes. All of them can be added at the user layer by implementing 2 classes SchemaReader / SchemaWriter. - If the new dataframe is to be added as a supported type then, it contributor needs to implement the TypeTransformer interface as well
kumare3
requested review from
matthewphsmith and
wild-endeavor
as code owners
wild-endeavor
approved these changes
Dec 2, 2020
| literals[k] = TypeEngine.to_literal(ctx, v, py_type, literal_type) | ||
| outputs_literal_map = _literal_models.LiteralMap(literals=literals) | ||
| return outputs_literal_map | ||
| def pre_execute(self, user_params: ExecutionParameters) -> ExecutionParameters: |
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Not a huge fan of this function signature... can we think of a way around this? i'd rather pass in the parent FlyteContext and access the user space params from there. This function name makes it seem like a generic setup call, but it always takes in and returns just the user params? that seems limiting
| return my_spark(df=df) | ||
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| x = my_wf() | ||
| reader = x.open(pandas.DataFrame) |
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what happens if pandas.DataFrame is not specified?
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default is pandas.DataFrame actually, I can drop it
Co-authored-by: Yee Hing Tong <wild-endeavor@users.noreply.github.com>
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based dataframes and regular pandas style dataframes. All of them can
be added at the user layer by implementing 2 classes SchemaReader
/ SchemaWriter.
contributor needs to implement the TypeTransformer interface as well