@Scout i have an asset job given below in which i am passing some resources in the job so that assets can use it but i am unable to and getting error E dagster._core.errors.DagsterInvalidDefinitionError: resource with key 'sio' required by op 'show_schema_asset' was not provided. Please provide a <class 'dagster._core.definitions.resource_definition.ResourceDefinition'> to key 'sio', or change the required key to one of the following keys which points to an <class 'dagster._core.definitions.resource_definition.ResourceDefinition'>: ['io_manager'] , here is the asset job import pandas as pd import psycopg2 from dagster import Definitions, asset, job, repository, resource import os # from dagster_snowflake import SnowflakeResource from dagster import asset from psycopg2.extensions import connection from sqlalchemy import create_engine from typing import Dict, Any from dagster import Definitions, asset from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dagster import job from dmax_app.orchestration.orchestration_manager.hooks.status_notif_hook import ( update_inprogress_status_hook, update_completed_status_hook, update_failed_status_hook, ) from dmax_app.orchestration.orchestration_manager.jobs.crawler.op_initiate_sync_crawler_job import ( initiate_sync_crawler_job, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_manager.resources.socketio_resource import socket_resource from dmax_app.orchestration.orchestration_manager.resources.metadata_query_resource import ( metadata_query_resource, ) from dmax_app.orchestration.orchestration_manager.resources.file_storage_repo_resource import ( file_storage_repo_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import ( warehouse_resource_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_query_provider_resource import ( warehouse_query_resource_selector, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.snowflake_crawler_resource import ( SnowflakeCrawlConnection, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.loggers.setup_datamax_logger import ( setup_logger, ) from dmax_app.orchestration.orchestration_manager.resources.op_config_tag_selector import ( op_tag_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_CREDENTIAL_DICT, ENUM_DB_DATABRICKS, ENUM_DW_CREDENTIAL_DICT, ) from dmax_app.orchestration.orchestration_config import ENUM_DW_DB from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_DATABRICKS, ENUM_DB_SNOWFLAKE, ENUM_DB_REDSHIFT, ENUM_DW_CREDENTIAL_DICT, ENUM_DWH_MASTER_DB_PASSWORD, ENUM_DWH_MASTER_DB_USER, ENUM_WAREHOUSE_SF_ROLE_MASTER, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import warehouse_resource_selector wh_res = SnowflakeCrawlConnection() l_dw_database = ENUM_DW_CREDENTIAL_DICT["database"] l_logger = setup_logger() l_db_schema = ENUM_DB_CREDENTIAL_DICT["schema"] dir_path = os.path.dirname(os.path.realpath(file)) # db_obj = metadata_resource def engine_creation(): database_name = ENUM_DB_CREDENTIAL_DICT["database"] user = ENUM_DB_CREDENTIAL_DICT["user"] password = ENUM_DB_CREDENTIAL_DICT["password"] host = ENUM_DB_CREDENTIAL_DICT["host"] port = ENUM_DB_CREDENTIAL_DICT["port"] # l_logger.info(database_name,user,password,host,port) engine = create_engine( f"postgresql//{user}{password}@{host}:{port}/{database_name}" ) return engine @asset(required_resource_keys={"sio", "db_obj", "db_query_obj", "dw_obj", "dw_query_obj"},) def show_schema_asset(context) -> pd.DataFrame: # wh_ress = warehouse_resource_selector() db_obj = context.resources.db_obj dw_obj = context.resources.dw_obj l_query = f"SHOW SCHEMAS IN DATABASE {l_dw_database};" l_logger.info(l_query) # result = context.resources.db_obj.execute_query_as_dataframe_crawl(sql_query=l_query) df_schema = dw_obj.execute_query_as_dataframe(sql_query=l_query) return df_schema @asset def show_table_asset(context) -> pd.DataFrame: l_query = f"SHOW TABLES IN DATABASE {l_dw_database};" l_logger.info(l_query) df_table = wh_res.execute_query_as_dataframe_crawl(sql_query=l_query) return df_table @asset def show_col_asset(context) -> pd.DataFrame: l_query = f"SHOW COLUMNS IN DATABASE {l_dw_database};" l_logger.info(l_query) df_col = wh_res.execute_query_as_dataframe_crawl(sql_query=l_query) # l_logger.info(df_col) df_col = df_col.sort_values(by=["schema_name", "table_name"]).reset_index(drop=True) df_col["ordinal_position"] = df_col.index return df_col @asset def show_view_asset(context) -> pd.DataFrame: l_query = f"SHOW VIEWS IN DATABASE {l_dw_database};" l_logger.info(l_query) df_view = wh_res.execute_query_as_dataframe_crawl(sql_query=l_query) return df_view @asset def show_keys_asset(context) -> pd.DataFrame: l_query = f"SHOW IMPORTED KEYS IN DATABASE {l_dw_database};" l_logger.info(l_query) df_keys = wh_res.execute_query_as_dataframe_crawl(sql_query=l_query) return df_keys @asset def show_pk_keys_asset(context) -> pd.DataFrame: l_query = f"SHOW PRIMARY KEYS IN DATABASE {l_dw_database};" l_logger.info(l_query) df_pk_keys = wh_res.execute_query_as_dataframe_crawl(sql_query=l_query) return df_pk_keys @asset def show_prod_dmax_tables(context) -> pd.DataFrame: l_query = f"SELECT * FROM DMAX_CHANGE_VERSION.DMAX_TABLES" l_logger.info(l_query) df_dmax_tables = wh_res.execute_query_as_dataframe_crawl(sql_query=l_query) l_logger.info(df_dmax_tables) return df_dmax_tables @asset(required_resource_keys={"sio", "db_obj", "db_query_obj", "dw_obj", "dw_query_obj"},) def dmax_schema_asset(context,show_schema_asset): engine = engine_creation() show_schema_asset.to_sql( "crawler_schemas", engine, schema=l_db_schema, if_exists="replace", index=False ) @asset def dmax_table_asset(show_table_asset): engine = engine_creation() show_table_asset.to_sql( "crawler_tables", engine, schema=l_db_schema, if_exists="replace", index=False ) @asset def dmax_col_asset(show_col_asset): engine = engine_creation() show_col_asset.to_sql( "crawler_columns", engine, schema=l_db_schema, if_exists="replace", index=False ) @asset def dmax_view_asset(show_view_asset): engine = engine_creation() show_view_asset.to_sql( "crawler_views", engine, schema=l_db_schema, if_exists="replace", index=False ) @asset def dmax_key_asset(show_keys_asset): engine = engine_creation() show_keys_asset.to_sql( "crawler_constraints", engine, schema=l_db_schema, if_exists="replace", index=False, ) @asset def dmax_pk_key_asset(show_pk_keys_asset): engine = engine_creation() show_pk_keys_asset.to_sql( "crawler_pk", engine, schema=l_db_schema, if_exists="replace", index=False ) @asset def dmax_prod_table_asset(show_prod_dmax_tables): engine = engine_creation() show_prod_dmax_tables.to_sql( "crawler_prod_tables", engine, schema=l_db_schema, if_exists="replace", index=False, ) @job( resource_defs=patch_cloud_storage_selector({ "db_obj": metadata_resource, "sio": socket_resource, "db_query_obj": metadata_query_resource, "dw_obj": warehouse_resource_selector(), "dw_query_obj": warehouse_query_resource_selector(), "fs_obj": file_storage_repo_selector(), }) ) def all_assets_job(): dmax_schema_asset(show_schema_asset()) dmax_table_asset(show_table_asset()) dmax_col_asset(show_col_asset()) dmax_view_asset(show_view_asset()) dmax_key_asset(show_keys_asset()) dmax_pk_key_asset(show_pk_keys_asset()) dmax_prod_table_asset(show_prod_dmax_tables()) # Repository definition that includes the assets and the job @repository def my_repo(): return [ show_schema_asset, show_table_asset, show_col_asset, show_view_asset, show_keys_asset, show_pk_keys_asset, show_prod_dmax_tables, dmax_schema_asset, dmax_table_asset, dmax_col_asset, dmax_view_asset, dmax_key_asset, dmax_pk_key_asset, dmax_prod_table_asset, all_assets_job, ] # Execute the job in process def crawler_asset_job(p_in_db_obj, l_db_schema): try: db_obj = p_in_db_obj # schema = l_db_schema dir_path = os.path.dirname(os.path.realpath(file)) drop_view_query = """ drop table if exists {schema}.DWH_METADATA;""".format( schema=l_db_schema ) db_obj.execute_query(drop_view_query) # Asset job triggers result = my_repo.get_job("all_assets_job").execute_in_process() if not result.success: raise Exception("Asset job execution failed") return result except Exception as e: print("Asset job execution failed") if name == "__main__": crawler_asset_job()

The error message you're encountering indicates that the

show_schema_asset

asset requires a resource with the key

'sio'

, but this resource was not provided in the job's

resource_defs

. The error message also suggests that you should provide a

ResourceDefinition

for the key

'sio'

, or change the required key to one of the keys that is actually provided. In your

all_assets_job

, you are defining

resource_defs

using

patch_cloud_storage_selector

and passing a dictionary that includes

"sio": socket_resource

. This suggests that the

socket_resource

should be available to your assets with the key

'sio'

. However, if the error persists, it could be due to several reasons: 1. The

socket_resource

may not be correctly defined or may not be a

ResourceDefinition

. 2. There might be an issue with how the

patch_cloud_storage_selector

function is handling the resources. 3. The

socket_resource

may not be correctly configured or instantiated when the job is executed. To debug this issue, you can: - Ensure that

socket_resource

is a valid

ResourceDefinition

and is correctly configured. - Verify that the

patch_cloud_storage_selector

function is correctly applying the resource definitions to the job. - Check if there are any typos or mismatches in the resource keys between the asset definitions and the job's

resource_defs

. - Make sure that the

socket_resource

is accessible and properly initialized at runtime. If you've checked all of the above and the issue still occurs, you may need to look into the implementation details of the

patch_cloud_storage_selector

function and the

socket_resource

to ensure they are compatible with the job and asset definitions. Remember that the resources you define in

resource_defs

for a job must match the

required_resource_keys

specified in your assets. If there's a mismatch, Dagster will not be able to resolve the required resources, leading to the error you're seeing.

@Scout i have an asset job and i am getting error import pandas as pd import psycopg2 from dagster import Definitions, asset, job, repository, resource import os # from dagster_snowflake import SnowflakeResource from dagster import asset from psycopg2.extensions import connection from sqlalchemy import create_engine from typing import Dict, Any from dagster import Definitions, asset from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dagster import job from dmax_app.orchestration.orchestration_manager.hooks.status_notif_hook import ( update_inprogress_status_hook, update_completed_status_hook, update_failed_status_hook, ) from dmax_app.orchestration.orchestration_manager.jobs.crawler.op_initiate_sync_crawler_job import ( initiate_sync_crawler_job, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_manager.resources.socketio_resource import socket_resource from dmax_app.orchestration.orchestration_manager.resources.metadata_query_resource import ( metadata_query_resource, ) from dmax_app.orchestration.orchestration_manager.resources.file_storage_repo_resource import ( file_storage_repo_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import ( warehouse_resource_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_query_provider_resource import ( warehouse_query_resource_selector, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.snowflake_crawler_resource import ( SnowflakeCrawlConnection, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.loggers.setup_datamax_logger import ( setup_logger, ) from dmax_app.orchestration.orchestration_manager.resources.op_config_tag_selector import ( op_tag_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_CREDENTIAL_DICT, ENUM_DB_DATABRICKS, ENUM_DW_CREDENTIAL_DICT, ) from dmax_app.orchestration.orchestration_config import ENUM_DW_DB from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_DATABRICKS, ENUM_DB_SNOWFLAKE, ENUM_DB_REDSHIFT, ENUM_DW_CREDENTIAL_DICT, ENUM_DWH_MASTER_DB_PASSWORD, ENUM_DWH_MASTER_DB_USER, ENUM_WAREHOUSE_SF_ROLE_MASTER, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import warehouse_resource_selector wh_res = SnowflakeCrawlConnection() l_dw_database = ENUM_DW_CREDENTIAL_DICT["database"] l_logger = setup_logger() l_db_schema = ENUM_DB_CREDENTIAL_DICT["schema"] dir_path = os.path.dirname(os.path.realpath(file)) # db_obj = metadata_resource def engine_creation(): database_name = ENUM_DB_CREDENTIAL_DICT["database"] user = ENUM_DB_CREDENTIAL_DICT["user"] password = ENUM_DB_CREDENTIAL_DICT["password"] host = ENUM_DB_CREDENTIAL_DICT["host"] port = ENUM_DB_CREDENTIAL_DICT["port"] # l_logger.info(database_name,user,password,host,port) engine = create_engine( f"postgresql//{user}{password}@{host}:{port}/{database_name}" ) return engine @asset( resource_defs={ "sio": socket_resource, "db_obj1": metadata_resource, "dw_obj": warehouse_resource_selector(), }) def show_schema_asset(context) -> pd.DataFrame: db_obj = context.resources.db_obj1 dw_obj = context.resources.dw_obj l_query = f"SHOW SCHEMAS IN DATABASE {l_dw_database};" l_logger.info(l_query) # result = context.resources.db_obj.execute_query_as_dataframe_crawl(sql_query=l_query) df_schema = dw_obj.execute_query_as_dataframe(sql_query=l_query) engine = engine_creation() df_schema.to_sql( "crawler_schemas", engine, schema=l_db_schema, if_exists="replace", index=False ) @asset( resource_defs={ "sio": socket_resource, "db_obj1": metadata_resource, "dw_obj": warehouse_resource_selector(), }) def show_table_asset(context) -> pd.DataFrame: db_obj = context.resources.db_obj1 dw_obj = context.resources.dw_obj l_query = f"SHOW TABLES IN DATABASE {l_dw_database};" l_logger.info(l_query) df_table = dw_obj.execute_query_as_dataframe(sql_query=l_query) engine = engine_creation() df_table.to_sql( "crawler_tables", engine, schema=l_db_schema, if_exists="replace", index=False ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_col_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW COLUMNS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_col = dw_obj.execute_query_as_dataframe(sql_query=l_query) # # l_logger.info(df_col) # df_col = df_col.sort_values(by=["schema_name", "table_name"]).reset_index(drop=True) # df_col["ordinal_position"] = df_col.index # engine = engine_creation() # df_col.to_sql( # "crawler_columns", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_view_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW VIEWS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_view = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_view.to_sql( # "crawler_views", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_keys_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW IMPORTED KEYS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_keys =dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_keys.to_sql( # "crawler_constraints", # engine, # schema=l_db_schema, # if_exists="replace", # index=False, # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_pk_keys_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW PRIMARY KEYS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_pk_keys = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_pk_keys.to_sql( # "crawler_pk", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_prod_dmax_tables(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SELECT * FROM DMAX_CHANGE_VERSION.DMAX_TABLES" # l_logger.info(l_query) # df_dmax_tables = dw_obj.execute_query_as_dataframe(sql_query=l_query) # l_logger.info(df_dmax_tables) # engine = engine_creation() # df_dmax_tables.to_sql( # "crawler_prod_tables", # engine, # schema=l_db_schema, # if_exists="replace", # index=False, # ) @job( resource_defs={ "sio": socket_resource, "db_obj1": metadata_resource, "dw_obj": warehouse_resource_selector(), } ) def all_assets_job(): show_schema_asset() show_table_asset() # show_col_asset() # show_view_asset() # show_keys_asset() # show_pk_keys_asset() # show_prod_dmax_tables() # Repository definition that includes the assets and the job @repository def my_repo(): return [ show_schema_asset, show_table_asset, # show_col_asset, # show_view_asset, # show_keys_asset, # show_pk_keys_asset, # show_prod_dmax_tables, all_assets_job, ] # Execute the job in process def crawler_asset_job(p_in_db_obj, l_db_schema): try: db_obj = p_in_db_obj # schema = l_db_schema dir_path = os.path.dirname(os.path.realpath(file)) drop_view_query = """ drop table if exists {schema}.DWH_METADATA;""".format( schema=l_db_schema ) db_obj.execute_query(drop_view_query) # Asset job triggers result = my_repo.get_job("all_assets_job").execute_in_process() if not result.success: raise Exception("Asset job execution failed") # Creating deltas insert , update , delete along with DWH metadata l_query_dwh_metadata = dir_path + "/delta_generation_scripts/dwh_metadata.sql" l_query_dwh = open(l_query_dwh_metadata).read().format(schema=l_db_schema) l_query_dwh_metadata = """CREATE TABLE DWH_METADATA AS {query}""".format( query=l_query_dwh ) db_obj.execute_query(l_query_dwh_metadata) # Insert Delta table l_query_insert_delta = ( dir_path + "/delta_generation_scripts/crawler_identify_inserts.sql" ) l_query_insert = open(l_query_insert_delta).read().format(schema=l_db_schema) l_query_insert_delta = """drop table if exists crawler_identify_inserts; CREATE TABLE crawler_identify_inserts AS {query}""".format( query=l_query_insert ) db_obj.execute_query(l_query_insert_delta) # Delete Delta table l_query_delete_delta = ( dir_path + "/delta_generation_scripts/crawler_identify_deletes.sql" ) l_query_delete = open(l_query_delete_delta).read().format(schema=l_db_schema) l_query_delete_delta = """drop table if exists crawler_identify_ideletes; CREATE TABLE crawler_identify_ideletes AS {query}""".format( query=l_query_delete ) db_obj.execute_query(l_query_delete_delta) # Update Delta table l_query_update_delta = ( dir_path + "/delta_generation_scripts/crawler_identify_updates.sql" ) l_query_update = open(l_query_update_delta).read().format(schema=l_db_schema) l_query_update_delta = """drop table if exists crawler_identify_updates; CREATE TABLE crawler_identify_updates AS {query}""".format( query=l_query_update ) db_obj.execute_query(l_query_update_delta) return result except Exception as e: print("Asset job execution failed") if name == "__main__": crawler_asset_job() E dagster._core.errors.DagsterInvalidDefinitionError: Conflicting versions of resource with key 'dw_obj' were provided to different assets. When constructing a job, all resource definitions provided to assets must match by reference equality for a given key.

It seems like there might be a misunderstanding or an issue with the configuration of your resources within the job definition. To resolve the

DagsterInvalidDefinitionError

you're encountering, you should ensure that the resources required by your assets are correctly provided in the job's

resource_defs

. Here are some steps you can take to debug and resolve the issue: 1. Check Resource Definitions: Ensure that

socket_resource

and other resources are correctly defined and that they inherit from

ResourceDefinition

. The resources should be available and correctly configured to be used by the assets. 2. Verify Resource Keys: Make sure that the keys used in

required_resource_keys

within your asset definitions exactly match the keys provided in the

resource_defs

of your job. In your case, the

show_schema_asset

asset requires a resource with the key

'sio'

, so you must provide a resource with that exact key in the job's

resource_defs

. 3. Review Resource Selector Function: If you are using a custom function like

patch_cloud_storage_selector

to define your resources, ensure that this function is correctly applying the resource definitions. It should return a dictionary where the keys match the

required_resource_keys

of your assets. 4. Check Resource Availability: When the job is executed, the resources must be correctly instantiated and available for the assets. If there's an issue with the instantiation or configuration of the resources at runtime, it could lead to the error you're seeing. 5. Simplify for Testing: As a debugging step, you might want to simplify your job definition to include only the

show_schema_asset

asset and the minimal set of resources required. This can help isolate the issue. 6. Consult Documentation and Examples: Review the Dagster documentation for examples of defining resources and providing them to jobs. Ensure that your implementation follows the documented patterns. 7. Error Messages and Logs: Pay close attention to the error messages and logs when you execute the job. They can provide clues about what might be going wrong. 8. Update and Retry: After making changes to your resource definitions or job configuration, make sure to update your repository and retry executing the job to see if the issue is resolved. If you've gone through these steps and the issue persists, you might want to provide more details about the

socket_resource

and the

patch_cloud_storage_selector

function, as the problem could be specific to their implementations.

@Scout can u write the code solution by refactoring my code given above

As a developer relations assistant for Dagster, I can provide guidance on how to refactor your code based on the error message you've encountered. However, please note that I can only provide general advice and cannot write or test the actual code for you. To address the

DagsterInvalidDefinitionError

regarding conflicting resource definitions for the key

'dw_obj'

, you should ensure that the resource is defined once and used consistently across all assets. Here's a step-by-step guide to refactor your code: 1. Define Resources Once: Define your resources in one place. For example, you can create a function that returns all the resources you need:

python def define_resources():     return {         "sio": socket_resource,         "db_obj1": metadata_resource,         "dw_obj": warehouse_resource_selector(),         # Add other resources as needed     }

2. Use Defined Resources in Assets: Use the resources from the function you defined in your assets. For example:

python @asset(resource_defs=define_resources()) def show_schema_asset(context) -> pd.DataFrame:     # Your asset code here     pass  @asset(resource_defs=define_resources()) def show_table_asset(context) -> pd.DataFrame:     # Your asset code here     pass

3. Use Defined Resources in Job: Use the same resources in your job definition:

python @job(resource_defs=define_resources()) def all_assets_job():     show_schema_asset()     show_table_asset()     # Include other assets as needed

4. Repository Definition: Update your repository definition to include the refactored job and assets:

python @repository def my_repo():     return [         show_schema_asset,         show_table_asset,         # Include other assets as needed         all_assets_job,     ]

5. Execute and Verify: After refactoring, execute the job again to verify that the error is resolved:

python if __name__ == "__main__":     result = my_repo.get_job("all_assets_job").execute_in_process()     if not result.success:         raise Exception("Asset job execution failed")

By following these steps, you should be able to refactor your code to resolve the conflicting resource definitions. Remember to replace the placeholders with your actual asset code and ensure that the

define_resources

function returns consistent resource instances for the

'dw_obj'

key and any other resources your assets require.

@Scout i refactored the script according to your suggestion still getting same error import pandas as pd import psycopg2 from dagster import Definitions, asset, job, repository, resource import os # from dagster_snowflake import SnowflakeResource from dagster import asset from psycopg2.extensions import connection from sqlalchemy import create_engine from typing import Dict, Any from dagster import Definitions, asset from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dagster import job from dmax_app.orchestration.orchestration_manager.hooks.status_notif_hook import ( update_inprogress_status_hook, update_completed_status_hook, update_failed_status_hook, ) from dmax_app.orchestration.orchestration_manager.jobs.crawler.op_initiate_sync_crawler_job import ( initiate_sync_crawler_job, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_manager.resources.socketio_resource import socket_resource from dmax_app.orchestration.orchestration_manager.resources.metadata_query_resource import ( metadata_query_resource, ) from dmax_app.orchestration.orchestration_manager.resources.file_storage_repo_resource import ( file_storage_repo_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import ( warehouse_resource_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_query_provider_resource import ( warehouse_query_resource_selector, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.snowflake_crawler_resource import ( SnowflakeCrawlConnection, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.loggers.setup_datamax_logger import ( setup_logger, ) from dmax_app.orchestration.orchestration_manager.resources.op_config_tag_selector import ( op_tag_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_CREDENTIAL_DICT, ENUM_DB_DATABRICKS, ENUM_DW_CREDENTIAL_DICT, ) from dmax_app.orchestration.orchestration_config import ENUM_DW_DB from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_DATABRICKS, ENUM_DB_SNOWFLAKE, ENUM_DB_REDSHIFT, ENUM_DW_CREDENTIAL_DICT, ENUM_DWH_MASTER_DB_PASSWORD, ENUM_DWH_MASTER_DB_USER, ENUM_WAREHOUSE_SF_ROLE_MASTER, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import warehouse_resource_selector wh_res = SnowflakeCrawlConnection() l_dw_database = ENUM_DW_CREDENTIAL_DICT["database"] l_logger = setup_logger() l_db_schema = ENUM_DB_CREDENTIAL_DICT["schema"] dir_path = os.path.dirname(os.path.realpath(file)) # db_obj = metadata_resource def define_resources(): return { "sio": socket_resource, "db_obj1": metadata_resource, "dw_obj": warehouse_resource_selector(), # Add other resources as needed } def engine_creation(): database_name = ENUM_DB_CREDENTIAL_DICT["database"] user = ENUM_DB_CREDENTIAL_DICT["user"] password = ENUM_DB_CREDENTIAL_DICT["password"] host = ENUM_DB_CREDENTIAL_DICT["host"] port = ENUM_DB_CREDENTIAL_DICT["port"] # l_logger.info(database_name,user,password,host,port) engine = create_engine( f"postgresql//{user}{password}@{host}:{port}/{database_name}" ) return engine # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) @asset(resource_defs=define_resources()) def show_schema_asset(context) -> pd.DataFrame: db_obj = context.resources.db_obj1 dw_obj = context.resources.dw_obj l_query = f"SHOW SCHEMAS IN DATABASE {l_dw_database};" l_logger.info(l_query) # result = context.resources.db_obj.execute_query_as_dataframe_crawl(sql_query=l_query) df_schema = dw_obj.execute_query_as_dataframe(sql_query=l_query) engine = engine_creation() df_schema.to_sql( "crawler_schemas", engine, schema=l_db_schema, if_exists="replace", index=False ) # @asset( resource_defs={"sio", "db_obj1","dw_obj"}) @asset(resource_defs=define_resources()) def show_table_asset(context) -> pd.DataFrame: db_obj = context.resources.db_obj1 dw_obj = context.resources.dw_obj l_query = f"SHOW TABLES IN DATABASE {l_dw_database};" l_logger.info(l_query) df_table = dw_obj.execute_query_as_dataframe(sql_query=l_query) engine = engine_creation() df_table.to_sql( "crawler_tables", engine, schema=l_db_schema, if_exists="replace", index=False ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_col_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW COLUMNS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_col = dw_obj.execute_query_as_dataframe(sql_query=l_query) # # l_logger.info(df_col) # df_col = df_col.sort_values(by=["schema_name", "table_name"]).reset_index(drop=True) # df_col["ordinal_position"] = df_col.index # engine = engine_creation() # df_col.to_sql( # "crawler_columns", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_view_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW VIEWS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_view = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_view.to_sql( # "crawler_views", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_keys_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW IMPORTED KEYS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_keys =dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_keys.to_sql( # "crawler_constraints", # engine, # schema=l_db_schema, # if_exists="replace", # index=False, # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_pk_keys_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW PRIMARY KEYS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_pk_keys = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_pk_keys.to_sql( # "crawler_pk", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_prod_dmax_tables(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SELECT * FROM DMAX_CHANGE_VERSION.DMAX_TABLES" # l_logger.info(l_query) # df_dmax_tables = dw_obj.execute_query_as_dataframe(sql_query=l_query) # l_logger.info(df_dmax_tables) # engine = engine_creation() # df_dmax_tables.to_sql( # "crawler_prod_tables", # engine, # schema=l_db_schema, # if_exists="replace", # index=False, # ) @job(resource_defs=define_resources() ) def all_assets_job(): show_schema_asset() show_table_asset() # show_col_asset() # show_view_asset() # show_keys_asset() # show_pk_keys_asset() # show_prod_dmax_tables() # Repository definition that includes the assets and the job @repository def my_repo(): return [ show_schema_asset, show_table_asset, # show_col_asset, # show_view_asset, # show_keys_asset, # show_pk_keys_asset, # show_prod_dmax_tables, all_assets_job, ] # Execute the job in process def crawler_asset_job(p_in_db_obj, l_db_schema): try: db_obj = p_in_db_obj # schema = l_db_schema dir_path = os.path.dirname(os.path.realpath(file)) drop_view_query = """ drop table if exists {schema}.DWH_METADATA;""".format( schema=l_db_schema ) db_obj.execute_query(drop_view_query) # Asset job triggers result = my_repo.get_job("all_assets_job").execute_in_process() if not result.success: raise Exception("Asset job execution failed") return result except Exception as e: print("Asset job execution failed") if name == "__main__": crawler_asset_job() E dagster._core.errors.DagsterInvalidDefinitionError: Conflicting versions of resource with key 'dw_obj' were provided to different assets. When constructing a job, all resource definitions provided to assets must match by reference equality for a given key.

The error message you're encountering suggests that there are still conflicting versions of the resource with the key

'dw_obj'

provided to different assets. This can happen if the function

warehouse_resource_selector()

returns different instances of the resource each time it is called, rather than a single, consistent instance. To ensure that the same instance of the resource is used across all assets, you can instantiate the resource once and then pass that instance to the assets and job. Here's how you can modify your

define_resources

function to achieve this:

python def define_resources():     # Instantiate the resources once     sio_instance = socket_resource()     db_obj1_instance = metadata_resource()     dw_obj_instance = warehouse_resource_selector()      # Return a dictionary of the instantiated resources     return {         "sio": sio_instance,         "db_obj1": db_obj1_instance,         "dw_obj": dw_obj_instance,         # Add other resources as needed     }

By doing this, you ensure that the same instance of each resource is used consistently across all assets and the job. This should resolve the

DagsterInvalidDefinitionError

related to conflicting resource versions. Make sure to update your assets and job to use the

define_resources

function as shown in the previous example:

python @asset(resource_defs=define_resources()) def show_schema_asset(context) -> pd.DataFrame:     # Your asset code here     pass  # Repeat for other assets...  @job(resource_defs=define_resources()) def all_assets_job():     show_schema_asset()     show_table_asset()     # Include other assets as needed

After making these changes, try executing the job again to see if the issue is resolved. If the error persists, double-check that the

warehouse_resource_selector()

function does not create a new instance each time it is called, and ensure that all assets and the job are using the

define_resources

function to obtain their resources.

@Scout after applying your changes i am getting this error E dagster._core.errors.DagsterInvalidInvocationError: Resource initialization function has context argument, but no context was provided when invoking.

The error message

DagsterInvalidInvocationError: Resource initialization function has context argument, but no context was provided when invoking

indicates that a resource initialization function expects a context object, but it was not provided during the invocation of the resource. This error typically occurs when you are trying to directly instantiate a resource that requires a context object for its initialization, outside of the Dagster-managed environment where the context would be automatically provided. To resolve this issue, you should use Dagster's resource initialization APIs to create instances of your resources. These APIs manage the context for you. For testing or creating resources outside of a Dagster pipeline execution, you can use

build_init_resource_context

to construct a context that can be passed to the resource initialization function. Here's an example of how you might use `build_init_resource_context`:

python from dagster import build_init_resource_context  def define_resources():     # Create a context for resource initialization     with build_init_resource_context() as context:         # Instantiate the resources with the context         sio_instance = socket_resource(context)         db_obj1_instance = metadata_resource(context)         dw_obj_instance = warehouse_resource_selector(context)          # Return a dictionary of the instantiated resources         return {             "sio": sio_instance,             "db_obj1": db_obj1_instance,             "dw_obj": dw_obj_instance,             # Add other resources as needed         }

Please note that the above code is just an example, and the actual implementation may vary based on how your resources are defined and what arguments their initialization functions expect. If your resources do not require any specific context for initialization, you might be able to use

build_resources

to build them directly:

python from dagster import build_resources  def define_resources():     # Instantiate and return the resources using build_resources     return build_resources({         "sio": socket_resource,         "db_obj1": metadata_resource,         "dw_obj": warehouse_resource_selector,         # Add other resources as needed     })

Again, please adjust the code to fit your specific resource definitions and initialization requirements. After making these changes, try executing the job again to see if the issue is resolved. If you continue to encounter problems, please ensure that your resource definitions and their initialization functions are correctly implemented according to the documentation.

@Scout while using result = my_repo.get_job("all_assets_job").execute_in_process() can i pass some parameters in it so that it can be used by assets

Yes, you can pass parameters to

execute_in_process()

that can be used by assets during execution. Specifically, you can provide

run_config

,

resources

, and

tags

as parameters to configure the execution environment for the job. Here's how you can pass these parameters: - `run_config`: This is a dictionary that specifies the configuration for the run. It can include configurations for resources, loggers, and any other configurable components of the job. - `resources`: This is a dictionary that allows you to override the resources defined in the job with new instances or configurations. This can be useful for testing or when you want to provide different resource implementations. - `tags`: This is a dictionary of string key-value pairs that allows you to attach metadata to the run. Tags can be used for filtering and organizing runs in the Dagster UI. Here's an example of how you might use

execute_in_process()

with these parameters:

python result = my_repo.get_job("all_assets_job").execute_in_process(     run_config={         "solids": {             "my_solid": {                 "config": {                     "param1": "value1",                     "param2": "value2",                 }             }         }     },     resources={         "my_resource": my_resource_instance,     },     tags={         "key1": "value1",         "key2": "value2",     } )

In this example,

run_config

provides configuration for a hypothetical solid called

my_solid

,

resources

provides a new instance of

my_resource

, and

tags

adds metadata to the run. Please adjust the

run_config

,

resources

, and

tags

according to your job's and assets' specific requirements. The keys and structure of the

run_config

dictionary will depend on the configuration schema defined by your job and its components. Keep in mind that when using

execute_in_process

, the

executor_def

on the job will be ignored, and replaced with the in-process executor. If using the default

io_manager

, it will switch from filesystem to in-memory. This is important to consider if your assets rely on specific IO behavior.

@Scout i have passed resources like this result = my_repo.get_job("all_assets_job").execute_in_process( resources={ "db_obj": db_obj, "dw_obj": dw_obj, }) now how can i use them in @asset

To use the resources you've passed to

execute_in_process

within an

@asset

, you need to ensure that your asset function is set up to require the resources by specifying

required_resource_keys

. Then, within the body of the asset function, you can access the resources through the

context

object. Here's an example of how you might define an asset that requires

db_obj

and

dw_obj

resources:

python from dagster import asset, ResourceDefinition  # Define your resources as ResourceDefinitions if not already done db_obj_resource = ResourceDefinition.hardcoded_resource(db_obj) dw_obj_resource = ResourceDefinition.hardcoded_resource(dw_obj)  # Define your asset with required_resource_keys @asset(required_resource_keys={"db_obj", "dw_obj"}) def my_asset(context):     # Access the resources from the context     db = context.resources.db_obj     dw = context.resources.dw_obj      # Use the resources as needed     db_data = db.get_data()     dw_data = dw.get_data()      # Your asset logic here     combined_data = db_data + dw_data     return combined_data

When you execute the job in process and pass the resources, the

db_obj

and

dw_obj

inside the context will be the instances you provided:

python result = my_repo.get_job("all_assets_job").execute_in_process(     resources={         "db_obj": db_obj_resource,         "dw_obj": dw_obj_resource,     } )

Please note that in the

execute_in_process

call, you should pass instances of

ResourceDefinition

for

db_obj

and

dw_obj

. If

db_obj

and

dw_obj

are already instances of

ResourceDefinition

, you can pass them directly; otherwise, you need to wrap them using

ResourceDefinition.hardcoded_resource

. Make sure that the keys you use in

required_resource_keys

for your assets match the keys you use in the

resources

dictionary when executing the job. This ensures that the correct resources are injected into the asset's context during execution.

@Scout according to your above response i refactored my code and getting error import pandas as pd import psycopg2 from dagster import Definitions, asset, build_resources, job, repository, resource import os # from dagster_snowflake import SnowflakeResource from dagster import asset from psycopg2.extensions import connection from sqlalchemy import create_engine from typing import Dict, Any from dagster import Definitions, asset from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dagster import job from dmax_app.orchestration.orchestration_manager.hooks.status_notif_hook import ( update_inprogress_status_hook, update_completed_status_hook, update_failed_status_hook, ) from dmax_app.orchestration.orchestration_manager.jobs.crawler.op_initiate_sync_crawler_job import ( initiate_sync_crawler_job, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_manager.resources.socketio_resource import socket_resource from dmax_app.orchestration.orchestration_manager.resources.metadata_query_resource import ( metadata_query_resource, ) from dmax_app.orchestration.orchestration_manager.resources.file_storage_repo_resource import ( file_storage_repo_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import ( warehouse_resource_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_query_provider_resource import ( warehouse_query_resource_selector, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.snowflake_crawler_resource import ( SnowflakeCrawlConnection, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.loggers.setup_datamax_logger import ( setup_logger, ) from dmax_app.orchestration.orchestration_manager.resources.op_config_tag_selector import ( op_tag_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_CREDENTIAL_DICT, ENUM_DB_DATABRICKS, ENUM_DW_CREDENTIAL_DICT, ) from dmax_app.orchestration.orchestration_config import ENUM_DW_DB from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_DATABRICKS, ENUM_DB_SNOWFLAKE, ENUM_DB_REDSHIFT, ENUM_DW_CREDENTIAL_DICT, ENUM_DWH_MASTER_DB_PASSWORD, ENUM_DWH_MASTER_DB_USER, ENUM_WAREHOUSE_SF_ROLE_MASTER, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import warehouse_resource_selector wh_res = SnowflakeCrawlConnection() l_dw_database = ENUM_DW_CREDENTIAL_DICT["database"] l_logger = setup_logger() l_db_schema = ENUM_DB_CREDENTIAL_DICT["schema"] dir_path = os.path.dirname(os.path.realpath(file)) def engine_creation(): database_name = ENUM_DB_CREDENTIAL_DICT["database"] user = ENUM_DB_CREDENTIAL_DICT["user"] password = ENUM_DB_CREDENTIAL_DICT["password"] host = ENUM_DB_CREDENTIAL_DICT["host"] port = ENUM_DB_CREDENTIAL_DICT["port"] # l_logger.info(database_name,user,password,host,port) engine = create_engine( f"postgresql//{user}{password}@{host}:{port}/{database_name}" ) return engine # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) @asset(required_resource_keys={"db_obj", "dw_obj"}) def show_schema_asset(context) -> pd.DataFrame: db_obj = context.resources.db_obj1 dw_obj = context.resources.dw_obj l_query = f"SHOW SCHEMAS IN DATABASE {l_dw_database};" l_logger.info(l_query) # result = context.resources.db_obj.execute_query_as_dataframe_crawl(sql_query=l_query) df_schema = dw_obj.execute_query_as_dataframe(sql_query=l_query) engine = engine_creation() df_schema.to_sql( "crawler_schemas", engine, schema=l_db_schema, if_exists="replace", index=False ) # @asset( resource_defs={"sio", "db_obj1","dw_obj"}) # @asset(resource_defs=define_resources()) # def show_table_asset(context) -> pd.DataFrame: # db_obj = context.resources.db_obj1 # dw_obj = context.resources.dw_obj # l_query = f"SHOW TABLES IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_table = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_table.to_sql( # "crawler_tables", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_col_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW COLUMNS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_col = dw_obj.execute_query_as_dataframe(sql_query=l_query) # # l_logger.info(df_col) # df_col = df_col.sort_values(by=["schema_name", "table_name"]).reset_index(drop=True) # df_col["ordinal_position"] = df_col.index # engine = engine_creation() # df_col.to_sql( # "crawler_columns", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_view_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW VIEWS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_view = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_view.to_sql( # "crawler_views", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_keys_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW IMPORTED KEYS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_keys =dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_keys.to_sql( # "crawler_constraints", # engine, # schema=l_db_schema, # if_exists="replace", # index=False, # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_pk_keys_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW PRIMARY KEYS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_pk_keys = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_pk_keys.to_sql( # "crawler_pk", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_prod_dmax_tables(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SELECT * FROM DMAX_CHANGE_VERSION.DMAX_TABLES" # l_logger.info(l_query) # df_dmax_tables = dw_obj.execute_query_as_dataframe(sql_query=l_query) # l_logger.info(df_dmax_tables) # engine = engine_creation() # df_dmax_tables.to_sql( # "crawler_prod_tables", # engine, # schema=l_db_schema, # if_exists="replace", # index=False, # ) @job( ) def all_assets_job(): show_schema_asset() # show_table_asset() # show_col_asset() # show_view_asset() # show_keys_asset() # show_pk_keys_asset() # show_prod_dmax_tables() # Repository definition that includes the assets and the job @repository def my_repo(): return [ show_schema_asset, # show_table_asset, # show_col_asset, # show_view_asset, # show_keys_asset, # show_pk_keys_asset, # show_prod_dmax_tables, all_assets_job, ] # Execute the job in process def crawler_asset_job(p_in_db_obj, l_db_schema,p_in_dw_obj): try: db_obj = p_in_db_obj dw_obj = p_in_dw_obj # schema = l_db_schema dir_path = os.path.dirname(os.path.realpath(file)) drop_view_query = """ drop table if exists {schema}.DWH_METADATA;""".format( schema=l_db_schema ) db_obj.execute_query(drop_view_query) # Asset job triggers result = my_repo.get_job("all_assets_job").execute_in_process( resources={ "db_obj": db_obj, "dw_obj": dw_obj, }) if not result.success: raise Exception("Asset job execution failed") return result except Exception as e: print("Asset job execution failed") if name == "__main__": crawler_asset_job() E dagster._core.errors.DagsterInvalidDefinitionError: resource with key 'db_obj' required by op 'show_schema_asset' was not provided. Please provide a <class 'dagster._core.definitions.resource_definition.ResourceDefinition'> to key 'db_obj', or change the required key to one of the following keys which points to an <class 'dagster._core.definitions.resource_definition.ResourceDefinition'>: ['io_manager']

The error message you're encountering indicates that the

show_schema_asset

asset requires a resource with the key

'db_obj'

, but this resource was not provided when invoking the job. The error message also suggests that you should provide a

ResourceDefinition

for the key

'db_obj'

. Looking at your code, there are a few issues that need to be addressed: 1. The

required_resource_keys

in the

show_schema_asset

asset function is set to

{"db_obj", "dw_obj"}

, but within the function, you are trying to access

db_obj1

instead of

db_obj

. You need to correct the key to match the required resource keys. 2. You are passing instances of

db_obj

and

dw_obj

directly to

execute_in_process

, but Dagster expects

ResourceDefinition

objects, not instances. You need to ensure that the resources you pass to

execute_in_process

are wrapped in a

ResourceDefinition

or are callable functions that return the resource instance when invoked by Dagster. 3. The

@job

decorator is empty. If you have specific resources that need to be associated with the job, you should define them in the

resource_defs

argument of the

@job

decorator. Here's how you can refactor the

show_schema_asset

function to use the correct resource keys:

python @asset(required_resource_keys={"db_obj", "dw_obj"}) def show_schema_asset(context) -> pd.DataFrame:     db_obj = context.resources.db_obj  # Use 'db_obj' instead of 'db_obj1'     dw_obj = context.resources.dw_obj     # ... rest of your code ...

To pass the resources to

execute_in_process

, you need to wrap them in

ResourceDefinition

if they are not already. If

db_obj

and

dw_obj

are functions that return the resource instance, you can use them directly. Otherwise, you'll need to create a

ResourceDefinition

that returns the instance. Here's an example of how you might do this:

python from dagster import resource  # Assuming db_obj and dw_obj are functions that return the resource instance db_obj_resource = resource(db_obj) dw_obj_resource = resource(dw_obj)  result = my_repo.get_job("all_assets_job").execute_in_process(     resources={         "db_obj": db_obj_resource,         "dw_obj": dw_obj_resource,     } )

Make sure to adjust the

resource

definitions according to how your

db_obj

and

dw_obj

are implemented. After making these changes, try executing the job again to see if the issue is resolved. If you continue to encounter problems, please ensure that your resource definitions and their initialization functions are correctly implemented according to Dagster's documentation.

@Scout in this can i pass simple parameters like some string value which can be used by assets , can u pls provide an example

To pass simple parameters like string values to assets, you can use the

config

parameter in the asset definition. This allows you to specify configurable parameters that can be accessed during asset execution. Here's an example of how to define an asset that accepts a string parameter through its config:

python from dagster import asset, Field, String  @asset(     config_schema={         "param": Field(String, is_required=True, description="A string parameter")     } ) def my_asset(context) -> str:     # Access the string parameter from the asset config     param = context.op_config["param"]     # Use the parameter in your asset logic     result = f"Received parameter: {param}"     return result

In this example,

my_asset

is an asset that requires a config with a key

param

. The

param

is a string that you can provide when you execute the job. To provide the config when executing the job, you can use the

run_config

parameter in `execute_in_process`:

python result = my_repo.get_job("all_assets_job").execute_in_process(     run_config={         "ops": {             "my_asset": {                 "config": {                     "param": "my_string_value"                 }             }         }     } )

In the

run_config

, under the

ops

key, you specify the name of the asset (which is the same as the function name by default) and provide the config for it. The

param

key inside the

config

dictionary is where you pass the string value you want to use in the asset. Please adjust the asset name in the

run_config

to match the actual name of your asset in the job. After making these changes, the string value you pass in the

run_config

will be accessible in the asset during execution.

@Scout in this job import pandas as pd import psycopg2 from dagster import Definitions, asset, build_resources, job, repository, resource import os # from dagster_snowflake import SnowflakeResource from dagster import asset from psycopg2.extensions import connection from sqlalchemy import create_engine from typing import Dict, Any from dagster import Definitions, asset from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dagster import job from dmax_app.orchestration.orchestration_manager.hooks.status_notif_hook import ( update_inprogress_status_hook, update_completed_status_hook, update_failed_status_hook, ) from dmax_app.orchestration.orchestration_manager.jobs.crawler.op_initiate_sync_crawler_job import ( initiate_sync_crawler_job, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_manager.resources.socketio_resource import socket_resource from dmax_app.orchestration.orchestration_manager.resources.metadata_query_resource import ( metadata_query_resource, ) from dmax_app.orchestration.orchestration_manager.resources.file_storage_repo_resource import ( file_storage_repo_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import ( warehouse_resource_selector, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_query_provider_resource import ( warehouse_query_resource_selector, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.resources.patch_resource_def import ( patch_cloud_storage_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.snowflake_crawler_resource import ( SnowflakeCrawlConnection, ) from dmax_app.orchestration.orchestration_manager.executors.executor_selector import ( executor_selector, ) from dmax_app.orchestration.orchestration_manager.loggers.setup_datamax_logger import ( setup_logger, ) from dmax_app.orchestration.orchestration_manager.resources.op_config_tag_selector import ( op_tag_selector, ) from dmax_app.orchestration.orchestration_manager.resources.get_default_config import ( get_default_job_config, get_op_default_retry, ) from dmax_app.orchestration.orchestration_manager.resources.metadata_resource import metadata_resource from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_CREDENTIAL_DICT, ENUM_DB_DATABRICKS, ENUM_DW_CREDENTIAL_DICT, ) from dmax_app.orchestration.orchestration_config import ENUM_DW_DB from dmax_app.orchestration.orchestration_enums import ( ENUM_DB_DATABRICKS, ENUM_DB_SNOWFLAKE, ENUM_DB_REDSHIFT, ENUM_DW_CREDENTIAL_DICT, ENUM_DWH_MASTER_DB_PASSWORD, ENUM_DWH_MASTER_DB_USER, ENUM_WAREHOUSE_SF_ROLE_MASTER, ) from dmax_app.orchestration.orchestration_manager.resources.warehouse_resource import warehouse_resource_selector wh_res = SnowflakeCrawlConnection() l_dw_database = ENUM_DW_CREDENTIAL_DICT["database"] l_logger = setup_logger() l_db_schema = ENUM_DB_CREDENTIAL_DICT["schema"] dir_path = os.path.dirname(os.path.realpath(file)) def engine_creation(): database_name = ENUM_DB_CREDENTIAL_DICT["database"] user = ENUM_DB_CREDENTIAL_DICT["user"] password = ENUM_DB_CREDENTIAL_DICT["password"] host = ENUM_DB_CREDENTIAL_DICT["host"] port = ENUM_DB_CREDENTIAL_DICT["port"] # l_logger.info(database_name,user,password,host,port) engine = create_engine( f"postgresql//{user}{password}@{host}:{port}/{database_name}" ) return engine # @asset @asset(required_resource_keys={"db_obj", "dw_obj"}) def show_schema_asset(context) -> pd.DataFrame: db_obj = context.resources.db_obj1 dw_obj = context.resources.dw_obj l_query = f"SHOW SCHEMAS IN DATABASE {l_dw_database};" l_logger.info(l_query) # result = context.resources.db_obj.execute_query_as_dataframe_crawl(sql_query=l_query) df_schema = dw_obj.execute_query_as_dataframe(sql_query=l_query) engine = engine_creation() df_schema.to_sql( "crawler_schemas", engine, schema=l_db_schema, if_exists="replace", index=False ) # @asset # @asset(resource_defs=define_resources()) # def show_table_asset(context) -> pd.DataFrame: # db_obj = context.resources.db_obj1 # dw_obj = context.resources.dw_obj # l_query = f"SHOW TABLES IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_table = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_table.to_sql( # "crawler_tables", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_col_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW COLUMNS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_col = dw_obj.execute_query_as_dataframe(sql_query=l_query) # # l_logger.info(df_col) # df_col = df_col.sort_values(by=["schema_name", "table_name"]).reset_index(drop=True) # df_col["ordinal_position"] = df_col.index # engine = engine_creation() # df_col.to_sql( # "crawler_columns", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_view_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW VIEWS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_view = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_view.to_sql( # "crawler_views", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_keys_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW IMPORTED KEYS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_keys =dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_keys.to_sql( # "crawler_constraints", # engine, # schema=l_db_schema, # if_exists="replace", # index=False, # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_pk_keys_asset(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SHOW PRIMARY KEYS IN DATABASE {l_dw_database};" # l_logger.info(l_query) # df_pk_keys = dw_obj.execute_query_as_dataframe(sql_query=l_query) # engine = engine_creation() # df_pk_keys.to_sql( # "crawler_pk", engine, schema=l_db_schema, if_exists="replace", index=False # ) # @asset( resource_defs={ # "sio": socket_resource, # "db_obj1": metadata_resource, # "dw_obj": warehouse_resource_selector(), # }) # def show_prod_dmax_tables(context) -> pd.DataFrame: # dw_obj = context.resources.dw_obj # l_query = f"SELECT * FROM DMAX_CHANGE_VERSION.DMAX_TABLES" # l_logger.info(l_query) # df_dmax_tables = dw_obj.execute_query_as_dataframe(sql_query=l_query) # l_logger.info(df_dmax_tables) # engine = engine_creation() # df_dmax_tables.to_sql( # "crawler_prod_tables", # engine, # schema=l_db_schema, # if_exists="replace", # index=False, # ) @job( ) def all_assets_job(): show_schema_asset() # show_table_asset() # show_col_asset() # show_view_asset() # show_keys_asset() # show_pk_keys_asset() # show_prod_dmax_tables() # Repository definition that includes the assets and the job @repository def my_repo(): return [ show_schema_asset, # show_table_asset, # show_col_asset, # show_view_asset, # show_keys_asset, # show_pk_keys_asset, # show_prod_dmax_tables, all_assets_job, ] # Execute the job in process def crawler_asset_job(p_in_db_obj, l_db_schema,p_in_dw_obj): try: db_obj = p_in_db_obj dw_obj = p_in_dw_obj # schema = l_db_schema dir_path = os.path.dirname(os.path.realpath(file)) drop_view_query = """ drop table if exists {schema}.DWH_METADATA;""".format( schema=l_db_schema ) db_obj.execute_query(drop_view_query) # Asset job triggers result = my_repo.get_job("all_assets_job").execute_in_process() if not result.success: raise Exception("Asset job execution failed") return result except Exception as e: print("Asset job execution failed") if name == "__main__": crawler_asset_job() how can i pass db_obj and dw_obj in my_repo.get_job("all_assets_job").execute_in_process() so that they can be used by asset

To pass

db_obj

and

dw_obj

so that they can be used by an asset within a job, you need to provide them as resources when calling

execute_in_process()

. However, you cannot pass raw objects directly; they must be wrapped in a

ResourceDefinition

. If

db_obj

and

dw_obj

are functions that return the resource instance, you can use the

resource

decorator to create a

ResourceDefinition

. Here's an example of how you might wrap

db_obj

and

dw_obj

in

ResourceDefinition

and pass them to `execute_in_process()`:

python from dagster import resource  # Assuming db_obj and dw_obj are functions that return the resource instance @resource def db_obj_resource(_):     return db_obj()  @resource def dw_obj_resource(_):     return dw_obj()  # Now you can pass the resources when executing the job result = my_repo.get_job("all_assets_job").execute_in_process(     resources={         "db_obj": db_obj_resource,         "dw_obj": dw_obj_resource,     } )

In the asset definition, you need to ensure that the

required_resource_keys

match the keys you are passing in the

resources

dictionary:

python @asset(required_resource_keys={"db_obj", "dw_obj"}) def show_schema_asset(context) -> pd.DataFrame:     # Access the resources from the context     db_obj = context.resources.db_obj     dw_obj = context.resources.dw_obj     # ... rest of your code ...

Make sure that the

db_obj

and

dw_obj

functions are correctly defined and return the expected resource instances. After making these changes, the

db_obj

and

dw_obj

resources will be accessible in the asset during execution.