Hi Team, I am try to add data validations and creating user defined data type.I want to perform a check if the file read is csv from one node and the next node also receives csv . I am getting error dagster.core.errors.DagsterInvalidDefinitionError: Input "pdd" in solid "Input1" is not connected to any outputs and can not be hydrated from configuration, creating an impossible to execute pipeline Sample code from dagster import execute_pipeline, pipeline, solid,as_dagster_type,lambda_solid,input_hydration_config import pandas as pd

DataFrame = as_dagster_type(

pd.DataFrame,

name='PandasDataFrame',

description='''Two-dimensional size-mutable, potentially heterogeneous

tabular data structure with labeled axes (rows and columns).

See <http://pandas.pydata.org/>''',

)

@lambda_solid

def Input1(pdd: DataFrame) -> DataFrame:

r = pdd.read_csv('file1.csv')

return r

@lambda_solid

def Merge(r: DataFrame,r2: DataFrame,pdd: DataFrame) -> DataFrame:

r3=pdd.concat([r,r2], axis=1)

return

@lambda_solid

def Input2(pdd: DataFrame) -> DataFrame:

r2 = pdd.read_csv('file2.csv')

return r2

@lambda_solid

def Result_output(y: DataFrame) -> DataFrame:

y3=y

return

@pipeline

def actual_dag_pipeline() :

y=Merge(Input1(),Input2())

Result_output(y)

the

pdd

input to

Input1/2

is the source of the problem - based on the fact your are reading a csv from a known path, i think you want to make a new data frame in the solid instead of defining an input

Thanks alex for you response .Do you mean I should add input_hydration_config also

you should do that if you want to be able to provide for example the csv path as config and load the dataframe that way

I am continuing with my existing code which I shared and did the changes as suggested by you

@lambda_solid

def Input1() -> DataFrame:

r = pd.read_csv('file1.csv')

return r

I can see the pipline execution is failing for agster.core.definitions.events.Failure: Value None should be of type DataFrame. this is for merge node in my code