Comments (1)
So I think this will be an excellent issue for you to work through some of the abstractions.
Currently this function creates a dependency between two dataframe-flavored solids:
def create_dagster_pd_dependency_input(solid):
check.inst_param(solid, 'solid', Solid)
def dependency_input_fn(context, arg_dict):
check.inst_param(context, 'context', DagsterExecutionContext)
path = check.str_elem(arg_dict, 'path')
frmt = check.str_elem(arg_dict, 'format')
df = _read_df(path, frmt)
context.metric('rows', df.shape[0])
return df
return create_single_source_input(
name=solid.name,
source_fn=dependency_input_fn,
argument_def_dict={
'path': types.PATH,
'format': types.STRING,
},
depends_on=solid,
source_type='CSVORPARQUET',
)
This should instead construct two separate sources for the input. One CSV, one Parquet. (Also adding JSON would make sense etc).
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