def extract():
json_data = request.get_json(force=True)
graph = json_data['graph']
dag_properties = json_data['dag_properties']
code_info, success, errors, additional_info = PipelineGenerator.generate_pipeline(
graph, dag_properties)
result = {
"codes": code_info,
"result_code": success,
"errors": errors,
"additional_info": additional_info
}
json_string = json.dumps(result)
return json_string
def interpret_graph():
json_data = request.get_json(force=True)
json_data = convert(json_data)
print(json_data)
print(type(json_data))
graph = json_data['graph']
print(graph)
print("------------")
dag_properties = json_data['dag_properties']
print(dag_properties)
print("------------")
code_info, success, errors, additional_info = PipelineGenerator.generate_pipeline(
graph, dag_properties)
result = {
"codes": code_info,
"result_code": success,
"errors": errors,
"additional_info": additional_info
}
json_string = json.dumps(result)
return json_string
}
}
},
"task1": {
"id": "task1",
"parent": None,
"node_type": 1
}
},
"edges": {
"node1-node2": {
"type": "dataframe"
},
"node1-node3": {
"type": "dataframe"
}
}
},
"dag_properties": {
"app_id": "MyFirstApp",
"bash_command": "sh /usr/local/shell_scripts/run.sh",
"schedule_interval": "@once",
"default_args": {
"owner": "airflow",
"start_date": "01/01/2018"
}
}
}
code_info, success, errors, additional_info = PipelineGenerator.generate_pipeline(
data["graph"], data["dag_properties"])
