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# ask-ai
p
@Scout why? dagster._core.errors.DagsterInvalidDefinitionError: Resource config error: gcp_credentials config for BigQuery resource cannot be used if GOOGLE_APPLICATION_CREDENTIALS environment variable is set.
s
The error message you're encountering indicates that there is a conflict in the way that Google Cloud Platform (GCP) credentials are being provided to the BigQuery resource within your Dagster setup. Specifically, the error is stating that the 
gcp_credentials
configuration for the BigQuery resource should not be used if the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is already set. According to the documentation, when you provide GCP authentication credentials directly to the BigQuery I/O manager, Dagster will create a temporary file with these credentials and set the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable to point to this temporary file. This is done to avoid issues with newline characters in the GCP credential key, which must be base64 encoded. However, if the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is already set in your environment (pointing to a file with the service account key), then you should not provide the 
gcp_credentials
configuration to the BigQuery resource. This is because the presence of the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable indicates to Google Cloud client libraries that default application credentials should be used, and it expects the value of this variable to be a file path, not the actual credentials. To resolve this error, you have two options: 1. Remove the 
gcp_credentials
configuration from your BigQuery resource and rely on the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable that you have already set. Ensure that this environment variable points to a valid JSON file containing your service account key. 2. If you need to provide the credentials directly (for example, if you are using Dagster Cloud Serverless and cannot upload a credential file), then ensure that the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is not set in your environment. Instead, provide the base64 encoded credentials using the 
gcp_credentials
configuration. Choose the approach that best fits your deployment scenario and authentication requirements. Usage of BigQuery Resouce + BigQuery IO ... button dagster-bigquery integration reference button resource with key 'bigquery' required by... button Credentials for GCS on Dagster Serverles... button Thank you for your feedback!
p
@Scout for production i use this definition now: "bigquery": BigQueryResource( project=project_id, location="europe-west3", gcp_credentials=EnvVar("GCP_CREDS_BASE64") but what can i do in local env? Whenever i try to provide gcp_credentials= it provides the same error, but when i dont it uses the creds from .env but loads the string as file name (so doesnt work)
s
When working in a local environment, you should ensure that your setup is consistent with how you're providing credentials to the BigQuery resource. The error you're encountering suggests that there's a conflict between the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable and the 
gcp_credentials
configuration you're trying to provide. Here's what you can do to resolve this issue in your local environment: 1. Using 
GOOGLE_APPLICATION_CREDENTIALS
Environment Variable: - Make sure the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is set to the path of the JSON file that contains your service account key. - Do not provide the 
gcp_credentials
configuration in your 
BigQueryResource
definition. This way, the BigQuery client will automatically use the credentials specified by the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable. 2. Using 
gcp_credentials
Configuration: - If you prefer to provide the credentials directly through the 
gcp_credentials
configuration, first ensure that the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is not set in your local environment. You can unset it by running 
unset GOOGLE_APPLICATION_CREDENTIALS
in your terminal or by removing it from any startup scripts or 
.env
files that might be setting it. - Provide the base64 encoded credentials using the 
gcp_credentials
configuration as you're currently doing. Ensure that the value of 
GCP_CREDS_BASE64
in your environment is the base64 encoded JSON key, not the file path. Here's an example of how you might configure the 
BigQueryResource
without setting the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable:
Copy code
python "bigquery": BigQueryResource(     project=project_id,     location="europe-west3",     gcp_credentials=EnvVar("GCP_CREDS_BASE64") )
In this case, 
GCP_CREDS_BASE64
should be an environment variable containing the base64 encoded contents of your GCP credentials JSON file. You can set this variable in your local environment by running:
Copy code
shell export GCP_CREDS_BASE64=$(cat path_to_your_service_account.json | base64)
Remember to replace 
path_to_your_service_account.json
with the actual path to your JSON credentials file. By following one of these approaches, you should be able to run your Dagster pipelines locally without encountering the 
DagsterInvalidDefinitionError
related to the BigQuery resource configuration.
p
can i reference the json as file path from .env file? @Scout
s
Yes, you can reference the JSON file path from a 
.env
file for local development. The 
.env
file is a convenient way to manage environment variables without hardcoding them into your source code, which is especially useful for sensitive information like credentials. To use the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable with a 
.env
file, you would do the following: 1. Create a 
.env
file in the root directory of your project (or ensure it's in a location that will be loaded by your application). 2. Add the path to your GCP credentials JSON file to the 
.env
file like this:
Copy code
GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/service-account-file.json
3. Make sure that your local development environment loads the 
.env
file. If you're using a package like 
python-dotenv
, you can load the 
.env
file in your Python code like this:
Copy code
python from dotenv import load_dotenv  load_dotenv()  # This will load the .env file and set the environment variables
Alternatively, if you're using a tool that automatically loads 
.env
files (like 
dagit
), you may not need to explicitly load the 
.env
file in your code. 4. Once the 
.env
file is loaded and the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is set, you can instantiate your 
BigQueryResource
without specifying the 
gcp_credentials
configuration:
Copy code
python "bigquery": BigQueryResource(     project=project_id,     location="europe-west3" )
The BigQuery client will automatically use the credentials specified by the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable. Remember to replace 
/path/to/your/service-account-file.json
with the actual path to your JSON credentials file, and ensure that the 
.env
file is not checked into source control, especially if it contains sensitive information.
p
@Scout i get this error: google.auth.exceptions.DefaultCredentialsError: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information. File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster/_core/execution/plan/utils.py", line 54, in op_execution_error_boundary yield File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) ^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster/_core/execution/plan/execute_step.py", line 792, in _gen_fn gen_output = output_manager.handle_output(output_context, output.value) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster_gcp/gcs/io_manager.py", line 169, in handle_output self._internal_io_manager.handle_output(context, obj) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster/_utils/cached_method.py", line 66, in _cached_method_wrapper result = method(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster_gcp/gcs/io_manager.py", line 162, in _internal_io_manager bucket=self.gcs_bucket, client=self.gcs.get_client(), prefix=self.gcs_prefix ^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster_gcp/gcs/resources.py", line 31, in get_client return _gcs_client_from_config(project=self.project) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster_gcp/gcs/resources.py", line 88, in _gcs_client_from_config return storage.client.Client(project=project) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/google/cloud/storage/client.py", line 172, in init super(Client, self).__init__( File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/google/cloud/client/__init__.py", line 321, in init Client.__init__( File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/google/cloud/client/__init__.py", line 178, in init credentials, _ = google.auth.default(scopes=scopes) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/google/auth/_default.py", line 691, in default raise exceptions.DefaultCredentialsError(_CLOUD_SDK_MISSING_CREDENTIALS) im using these resources in init.py: "prod": { "io_manager": GCSPickleIOManager( gcs=GCSResource(project=f"{project_id}"), gcs_bucket=f"{project_id}-accuranker-orchestration-test", gcs_prefix="data_folder" ), "google_cloud_storage": GCSResource( project=project_id ), "gsc_api_client": GoogleSearchConsoleAPI, "bigquery": BigQueryResource( project=project_id, location="europe-west3", gcp_credentials=EnvVar("GCP_CREDS_BASE64") ) whats the issue here?
s
The error message you're seeing indicates that the Google Cloud client library is unable to find the default credentials. This can happen for a few reasons, but given the context and the code you've provided, let's go through some troubleshooting steps: 1. Check the Environment Variable: Ensure that the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is correctly set to the path of your service account JSON file. You can check this by running 
echo $GOOGLE_APPLICATION_CREDENTIALS
in your terminal to see if it outputs the correct path. 2. Verify the JSON File: Make sure that the JSON file exists at the path specified by the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable and that it contains the correct credentials. 3. Permissions: Ensure that the JSON file is readable by the user running the Dagster process. 4. Environment Variable Loading: If you're setting the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable in a 
.env
file, make sure that the file is being loaded correctly. If you're using 
python-dotenv
or a similar package, ensure that it's installed and being invoked to load the 
.env
file. 5. Conflicting Credentials: Since you're using 
gcp_credentials=EnvVar("GCP_CREDS_BASE64")
in your 
BigQueryResource
, make sure that you're not setting the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable at the same time, as this can cause a conflict. You should use one method or the other, but not both simultaneously. 6. Base64 Decoding: If you're using the base64 encoded credentials, ensure that the 
GCP_CREDS_BASE64
environment variable contains the base64 encoded JSON key and that your code is correctly decoding it before use. 7. Resource Configuration: It looks like you're using 
GCSResource
without specifying 
gcp_credentials
. If you're relying on the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable for GCS authentication, make sure it's set correctly. If you're using base64 encoded credentials, you'll need to provide the 
gcp_credentials
configuration for 
GCSResource
as well. 8. Dagster Configuration: Ensure that your Dagster setup (e.g., 
workspace.yaml
or equivalent) is correctly configured to load the resources and environment variables. If you've gone through these steps and the issue persists, you may want to isolate the problem by testing the Google Cloud client library outside of Dagster to see if it can authenticate using the credentials file. This can help determine if the issue is with the credentials themselves or with how they're being used in Dagster. Lastly, if you're running this in a containerized or cloud environment, make sure that the environment variables and file paths are correctly set up in that context as well.
p
@Scout im using dagster cloud serverless. i have two env variables: • "GCP_CREDS_BASE64", which includes the base64 string wrapped in "
s
In Dagster Cloud Serverless, you cannot upload a credentials file directly, so you must provide your credentials as an environment variable. Since you're using the 
GCP_CREDS_BASE64
environment variable, you should ensure that it contains the base64-encoded JSON key for your GCP credentials. Here's what you need to do: 1. Base64 Decoding: The 
GCP_CREDS_BASE64
environment variable should contain the base64-encoded contents of your GCP credentials JSON file. Make sure that the value does not include any additional quotes or characters that are not part of the base64-encoded string. 2. Resource Configuration: When configuring your 
BigQueryResource
, you are correctly using the 
gcp_credentials=EnvVar("GCP_CREDS_BASE64")
parameter to specify the credentials. This should work as long as the 
GCP_CREDS_BASE64
environment variable is correctly set. 3. Avoid Conflicts: Ensure that you do not have the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable set in your environment, as this can conflict with the 
gcp_credentials
configuration you're providing. In Dagster Cloud Serverless, you should only use the 
gcp_credentials
parameter and not set 
GOOGLE_APPLICATION_CREDENTIALS
. 4. Check Environment Variable Syntax: If you're setting the 
GCP_CREDS_BASE64
environment variable in the Dagster Cloud UI, make sure that it is entered correctly without any additional wrapping quotes. The value should be a plain base64-encoded string. 5. Dagster Cloud Configuration: Ensure that your environment variables are correctly configured in the Dagster Cloud UI. You should have the necessary permissions to set these variables. 6. Debugging: If you continue to encounter issues, you may want to add logging to your code to print out the decoded credentials (be careful with sensitive information) to verify that they are being decoded correctly before being used by the 
BigQueryResource
. If you've verified all of the above and the issue persists, you may want to reach out to Dagster support for further assistance, as they may be able to provide more context-specific help or check if there's an issue with the Dagster Cloud Serverless environment handling of environment variables.
p
@Scout the second is "GCP_SERVICE_ACCOUNT_CREDENTIALS", which is a string which contains the json
s
Given that you have two environment variables, 
GCP_CREDS_BASE64
and 
GCP_SERVICE_ACCOUNT_CREDENTIALS
, and you are using Dagster Cloud Serverless, you should use only one method to provide your GCP credentials to avoid conflicts. Here's how you can handle each one: 1. Using 
GCP_CREDS_BASE64
: If you choose to use the base64-encoded credentials, ensure that the 
GCP_CREDS_BASE64
environment variable contains the base64-encoded JSON key and that you are decoding it correctly within your Dagster setup. You should not set the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable when using this method. 2. Using 
GCP_SERVICE_ACCOUNT_CREDENTIALS
: If you prefer to use the JSON string directly, you can set this environment variable with the JSON content of your service account credentials. However, you need to ensure that your Dagster resource configuration can accept and correctly handle this JSON string. As of my knowledge cutoff date, the typical usage pattern in Dagster involves either setting the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable to point to a file containing the credentials or using the base64-encoded credentials as described in the documentation. Since you are using Dagster Cloud Serverless, you cannot upload a credentials file, so the 
GCP_SERVICE_ACCOUNT_CREDENTIALS
variable should be used in a way that is compatible with your resource configuration. If there is no built-in support for directly passing the JSON string as an environment variable, you may need to write custom logic to handle this within your resource initialization code. To avoid the error you're encountering, you should choose one method of providing credentials and ensure that your environment variables and resource configurations are consistent with that method. If the documentation or the platform's capabilities have been updated beyond my knowledge cutoff date to support using a JSON string directly, you should follow the updated guidance provided by Dagster. If you continue to face issues, I recommend reaching out to Dagster support for assistance, as they can provide the most up-to-date information and help troubleshoot any platform-specific issues.
p
whats up with this error @Scout : dagster._core.errors.DagsterExecutionHandleOutputError: Error occurred while handling output "result" of step "check_if_db_is_up2date":: google.auth.exceptions.DefaultCredentialsError: Your default credentials were not found. To set up Application Default Credentials, see https://cloud.google.com/docs/authentication/external/set-up-adc for more information. Stack Trace: File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster/_core/execution/plan/utils.py", line 54, in op_execution_error_boundary yield File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) ^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster/_core/execution/plan/execute_step.py", line 792, in _gen_fn gen_output = output_manager.handle_output(output_context, output.value) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster_gcp/gcs/io_manager.py", line 169, in handle_output self._internal_io_manager.handle_output(context, obj) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster/_utils/cached_method.py", line 66, in _cached_method_wrapper result = method(self, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster_gcp/gcs/io_manager.py", line 162, in _internal_io_manager bucket=self.gcs_bucket, client=self.gcs.get_client(), prefix=self.gcs_prefix ^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster_gcp/gcs/resources.py", line 31, in get_client return _gcs_client_from_config(project=self.project) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/dagster_gcp/gcs/resources.py", line 88, in _gcs_client_from_config return storage.client.Client(project=project) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/google/cloud/storage/client.py", line 172, in init super(Client, self).__init__( File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/google/cloud/client/__init__.py", line 321, in init Client.__init__( File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/google/cloud/client/__init__.py", line 178, in init credentials, _ = google.auth.default(scopes=scopes) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/venvs/ec3619e4eb94/lib/python3.11/site-packages/google/auth/_default.py", line 691, in default raise exceptions.DefaultCredentialsError(_CLOUD_SDK_MISSING_CREDENTIALS)
s
The error you're encountering, 
google.auth.exceptions.DefaultCredentialsError
, indicates that the Google Cloud client library is unable to find the default credentials when trying to authenticate with Google Cloud services. This error is occurring within the context of handling an output with the GCS IO Manager in Dagster, which suggests that the credentials required to access Google Cloud Storage are not properly configured or accessible. Here are some steps to troubleshoot and resolve this issue: 1. Check Environment Variables: Ensure that the environment variable 
GOOGLE_APPLICATION_CREDENTIALS
is set and points to the correct path of the service account JSON file. This environment variable should contain the full file path, not the contents of the file. 2. Verify Service Account File: Make sure that the service account JSON file exists at the specified path and that it has the correct permissions set to allow the Dagster process to read it. 3. Base64 Encoded Credentials: If you are using base64 encoded credentials (as indicated by the 
GCP_CREDS_BASE64
environment variable in previous discussions), ensure that they are being correctly decoded and used to authenticate the GCS client. The credentials should be decoded from base64 and then used to create a credentials object that the Google Cloud client library can use. 4. Resource Configuration: Review the configuration of your GCS-related resources in Dagster to ensure that they are set up to use the correct method of authentication. If you are providing credentials directly through a Dagster resource configuration, make sure that the configuration is correct and that there are no conflicts with environment variables. 5. Permissions and Scopes: Verify that the service account has the necessary permissions and that the correct scopes are being used when initializing the Google Cloud client. 6. Dagster Cloud Serverless: Since you mentioned using Dagster Cloud Serverless, ensure that the environment variables are correctly configured in the Dagster Cloud UI and that they are being passed to the execution environment. 7. Debugging: Add logging to your code to check the values of environment variables and the status of credential loading to help pinpoint where the issue is occurring. If you've checked all the above and the issue persists, consider reaching out to Dagster support for further assistance. They can provide more detailed guidance and help you troubleshoot the issue in the context of Dagster Cloud Serverless.
p
@Scout in dagster UI under resources when i check "google_cloud_storage", it does not recognize the Credentials Variable. how can i fix this?
s
There was an error processing your request. Please try again.
p
@Scout in dagster UI under resources when i check "google_cloud_storage", it does not recognize the Credentials Variable. how can i fix this?
s
In Dagster Cloud Serverless, you cannot upload a credentials file directly. Instead, you must provide your credentials as an environment variable. According to the documentation, if you cannot authenticate with GCP via a standard method, you can provide a service account key as the 
gcp_credentials
configuration. Dagster will store this key in a temporary file and set 
GOOGLE_APPLICATION_CREDENTIALS
to point to the file. After the run completes, the file will be deleted, and 
GOOGLE_APPLICATION_CREDENTIALS
will be unset. The key must be base64 encoded to avoid issues with newlines in the keys. Here's what you need to do to ensure that the 
google_cloud_storage
resource recognizes the credentials variable: 1. Base64 Encode Your Credentials: If you haven't already, you need to base64 encode your GCP service account JSON file. You can do this with the following shell command:
Copy code
shell
   cat $GOOGLE_APPLICATION_CREDENTIALS | base64
Replace 
$GOOGLE_APPLICATION_CREDENTIALS
with the path to your service account JSON file. 2. Set the Encoded Credentials as an Environment Variable: In the Dagster Cloud UI, set the encoded credentials as an environment variable. The name of this variable should match the one you are using in your resource configuration (e.g., 
GCP_CREDS_BASE64
). 3. Configure Your Resource: In your Dagster resource configuration, reference the environment variable using 
EnvVar
. For example:
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python
   from dagster import resource, Field, String
   from dagster_gcp.gcs.resources import GCSResource

   @resource({
       "gcp_credentials": Field(String, is_required=False)
   })
   def google_cloud_storage(init_context):
       return GCSResource(
           project="my-gcp-project",
           gcp_credentials=init_context.resource_config.get("gcp_credentials"),
       )
In the above example, 
gcp_credentials
would be the base64 encoded service account key that you have set as an environment variable. 4. Update Your Dagster Cloud Configuration: Make sure that your Dagster Cloud configuration is updated to include the new environment variable. This may involve updating your 
workspace.yaml
or other relevant configuration files. 5. Check Permissions: Ensure that the service account associated with the credentials has the necessary permissions to access the GCS resources. 6. Debugging: If the credentials are still not recognized, you may need to add logging or error handling in your resource definition to help debug the issue. If you continue to encounter issues, you may need to reach out to Dagster support for assistance, as they can provide more context-specific help and ensure that your Dagster Cloud Serverless environment is correctly configured to use the provided creden
tials.
p
where can i update the resource configuration for GCSResource? @Scout
s
In Dagster, you can update the resource configuration for 
GCSResource
in a few different places, depending on how you are using the resource. Here are the common places where you might update the configuration: 1. Dagster Repository Code: If you define your resources in your repository code, you can update the configuration directly in the Python code where the resource is defined. For example:
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python from dagster_gcp import GCSResource  my_gcs_resource = GCSResource(     project="my-gcp-project",     gcp_credentials=EnvVar("GCP_CREDS_BASE64"),  # Assuming you have this environment variable set )
2. Dagster YAML Configuration Files: If you are using YAML configuration files to define your resources, you can update the 
resources
section of your YAML file. For example, in a 
workspace.yaml
or an asset/job-specific YAML file:
Copy code
yaml resources:   google_cloud_storage:     config:       project: "my-gcp-project"       gcp_credentials: { env: "GCP_CREDS_BASE64" }
3. Dagster Cloud UI: If you are using Dagster Cloud, you can configure environment variables directly in the UI. You would navigate to the environment variables section of your Dagster Cloud instance and set the 
GCP_CREDS_BASE64
variable with the base64-encoded credentials. 4. Run Configuration: When launching a run, you can provide resource configurations in the run config. This can be done through the Dagit UI or programmatically when using the 
execute_in_process
method or submitting a run through the Dagster Python API. Here's an example of how you might specify the resource configuration in a run config:
Copy code
yaml resources:   google_cloud_storage:     config:       project: "my-gcp-project"       gcp_credentials: { env: "GCP_CREDS_BASE64" }
Remember to replace 
"my-gcp-project"
with your actual GCP project ID and ensure that the 
GCP_CREDS_BASE64
environment variable is set with the correct base64-encoded credentials. After updating the resource configuration, you'll need to reload or restart your Dagster instance for the changes to take effect. If you're using Dagster Cloud, the changes should be picked up automatically when you launch a new run.
p
@Scout why ? dagster._core.errors.DagsterInvalidDefinitionError: Resource config error: gcp_credentials config for BigQuery resource cannot be used if GOOGLE_APPLICATION_CREDENTIALS environment variable is set. File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_plan.py", line 286, in dagster_event_sequence_for_step for step_event in check.generator(step_events): File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 519, in core_dagster_event_sequence_for_step for user_event in _step_output_error_checked_user_event_sequence( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 201, in _step_output_error_checked_user_event_sequence for user_event in user_event_sequence: File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 99, in _process_asset_results_to_events for user_event in user_event_sequence: File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 214, in execute_core_compute for step_output in _yield_compute_results(step_context, inputs, compute_fn, compute_context): File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 183, in _yield_compute_results for event in iterate_with_context( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/Users/paulherzog/Documents/GitHub/job_traveler_data/data_pipelines/jt_assets/gsc/gsc_check_dates_in_db_gsc.py", line 21, in check_if_db_is_up2date with bigquery.get_client() as client: File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/contextlib.py", line 135, in enter return next(self.gen) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster_gcp/bigquery/resources.py", line 79, in get_client with setup_gcp_creds(self.gcp_credentials): File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/contextlib.py", line 135, in enter return next(self.gen) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster_gcp/bigquery/utils.py", line 13, in setup_gcp_creds raise DagsterInvalidDefinitionError(
s
The error message you're encountering, 
DagsterInvalidDefinitionError
, is indicating that there is a conflict between the 
gcp_credentials
configuration provided to the BigQuery resource and the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable being set in your environment. In Dagster, when you provide 
gcp_credentials
directly to a resource, Dagster expects to manage the credentials internally, typically by creating a temporary file with these credentials and setting the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable to point to this file for the duration of the run. Once the run is complete, the temporary file is deleted, and the environment variable is unset. However, if the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is already set in your environment, it suggests to the Google Cloud client libraries that default application credentials should be used, and it expects the value of this variable to be a file path to the service account JSON file, not the actual credentials. To resolve this error, you have two options: 1. Use the Environment Variable: Remove the 
gcp_credentials
configuration from your BigQuery resource and rely solely on the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable. Ensure that this environment variable points to a valid JSON file containing your service account key. 2. Use the 
gcp_credentials
Configuration: If you need to provide the credentials directly (for example, if you are using Dagster Cloud Serverless and cannot upload a credential file), then ensure that the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is not set in your environment. Instead, provide the base64 encoded credentials using the 
gcp_credentials
configuration. Here's an example of how you might configure the 
BigQueryResource
using the 
gcp_credentials
configuration:
Copy code
python from dagster_gcp import BigQueryResource  bigquery_resource = BigQueryResource(     project="my-gcp-project",     location="us-central1",     gcp_credentials=base64_encoded_credentials,  # Your base64 encoded credentials )
Choose the approach that best fits your deployment scenario and authentication requirements. If you continue to face issues, you may need to reach out to Dagster support for assistance.
p
i dont understand like how exactly is there a different one set already? @Scout i got the base64 one and NO ONE ELSE dagster._core.errors.DagsterInvalidDefinitionError: Resource config error: gcp_credentials config for BigQuery resource cannot be used if GOOGLE_APPLICATION_CREDENTIALS environment variable is set. File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_plan.py", line 286, in dagster_event_sequence_for_step for step_event in check.generator(step_events): File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 519, in core_dagster_event_sequence_for_step for user_event in _step_output_error_checked_user_event_sequence( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 201, in _step_output_error_checked_user_event_sequence for user_event in user_event_sequence: File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/execute_step.py", line 99, in _process_asset_results_to_events for user_event in user_event_sequence: File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 214, in execute_core_compute for step_output in _yield_compute_results(step_context, inputs, compute_fn, compute_context): File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/compute.py", line 183, in _yield_compute_results for event in iterate_with_context( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/Users/paulherzog/Documents/GitHub/job_traveler_data/data_pipelines/jt_assets/gsc/gsc_check_dates_in_db_gsc.py", line 21, in check_if_db_is_up2date with bigquery.get_client() as client: File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/contextlib.py", line 135, in enter return next(self.gen) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster_gcp/bigquery/resources.py", line 79, in get_client with setup_gcp_creds(self.gcp_credentials): File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/contextlib.py", line 135, in enter return next(self.gen) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster_gcp/bigquery/utils.py", line 13, in setup_gcp_creds raise DagsterInvalidDefinitionError(
s
The error message you're seeing suggests that the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is set in your environment, which is conflicting with the 
gcp_credentials
configuration you're trying to provide directly to the BigQuery resource in your Dagster setup. Even though you have the base64 encoded credentials and you're not aware of setting the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable yourself, it's possible that it could have been set by another process or script, or it could be part of your shell's startup configuration. To troubleshoot this issue, you can take the following steps: 1. Check if the Environment Variable is Set: Open a terminal and run the following command to check if the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is set:
Copy code
shell    echo $GOOGLE_APPLICATION_CREDENTIALS
If this command outputs a path, then the environment variable is set. 2. Unset the Environment Variable: If the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable is set, you can unset it in your current terminal session with the following command:
Copy code
shell    unset GOOGLE_APPLICATION_CREDENTIALS
After unsetting the environment variable, try running your Dagster pipeline again. 3. Check Your Shell Configuration: If the environment variable is being set automatically, check your shell configuration files (e.g., 
.bashrc
, 
.bash_profile
, 
.zshrc
, etc.) for any lines that might be setting 
GOOGLE_APPLICATION_CREDENTIALS
. If you find such a line, you can comment it out or remove it. 4. Check for Other Scripts: If you're running your Dagster pipeline through a script or a tool that sets environment variables, check the configuration of that script or tool to ensure it's not setting 
GOOGLE_APPLICATION_CREDENTIALS
. 5. Check Dagster Configuration: Ensure that your Dagster configuration (e.g., 
workspace.yaml
, 
repository.yaml
, or the Python code where you define your resources) does not inadvertently set the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable. By following these steps, you should be able to identify and resolve the conflict between the environment variable and the 
gcp_credentials
configuration. If the issue persists after taking these steps, you may need to reach out to Dagster support for further assistance.
p
how can i update bigquery credentials point? @Scout for dagster cloud serverless
s
To update the BigQuery credentials for Dagster Cloud Serverless, you should provide your credentials as an environment variable because you cannot upload a credentials file directly. Here's how you can do it: 1. Base64 Encode Your Service Account Key: If you haven't already, you need to base64 encode your GCP service account JSON file. This is necessary to avoid issues with newline characters in the GCP credential key. You can do this with the following shell command:
Copy code
shell    cat path_to_your_service_account.json | base64
Replace 
path_to_your_service_account.json
with the actual path to your service account JSON file. 2. Set the Encoded Credentials as an Environment Variable: In the Dagster Cloud UI, navigate to the settings for your Dagster Cloud Serverless instance. Under the environment variables section, add a new environment variable with the name you will reference in your resource configuration (e.g., 
GCP_CREDS_BASE64
) and set its value to the base64-encoded credentials you obtained in the previous step. 3. Configure Your BigQuery Resource: In your Dagster code, configure the 
BigQueryResource
to use the 
gcp_credentials
parameter, referencing the environment variable you set in the Dagster Cloud UI. For example:
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python    from dagster import Field, String, resource    from dagster_gcp.bigquery.resources import BigQueryResource     @resource({        "gcp_credentials": Field(String, is_required=False)    })    def bigquery_resource(init_context):        return BigQueryResource(            project="my-gcp-project",            gcp_credentials=init_context.resource_config.get("gcp_credentials"),        )
In this example, the 
gcp_credentials
field is optional, and if provided, it should be the base64-encoded service account key. 4. Update Your Dagster Repository: Make sure your repository code reflects the use of the 
bigquery_resource
you defined. If you're using a 
workspace.yaml
file, ensure it's correctly pointing to your repository definitions. 5. Deploy Your Changes: After updating the environment variables and resource configuration, deploy your changes to Dagster Cloud Serverless. Your BigQuery resource should now use the updated credentials. Remember to replace 
"my-gcp-project"
with your actual GCP project ID. Also, ensure that you do not have the 
GOOGLE_APPLICATION_CREDENTIALS
environment variable set in your environment, as this can conflict with the 
gcp_credentials
configuration you're providing. If you encounter any issues, you may need to reach out to Dagster support for assistance, as they can provide more detailed guidance and help you troubleshoot any platform-specific issues.
p
@Scout with bigquery.get_client() as client: table_ref = f"adroit-medium-379911.{bq_data_set}.{bq_table_name}" load_job = client.load_table_from_dataframe( dataframe=dataframe, destination=table_ref, location="europe-west3" ) load_job.result() Whats the issue here: The above exception was caused by the following exception: pyarrow.lib.ArrowTypeError: object of type <class 'str'> cannot be converted to int File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/utils.py", line 54, in op_execution_error_boundary yield File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_utils/__init__.py", line 467, in iterate_with_context next_output = next(iterator) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 131, in _coerce_op_compute_fn_to_iterator result = invoke_compute_fn( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/dagster/_core/execution/plan/compute_generator.py", line 125, in invoke_compute_fn return fn(context, **args_to_pass) if context_arg_provided else fn(**args_to_pass) File "/Users/paulherzog/Documents/GitHub/job_traveler_data/data_pipelines/jt_assets/gsc/gsc_upload_from_storage_to_bigquery.py", line 81, in by_prop_data_to_bq dataframe = gcs_to_bq(bucket_name, File "/Users/paulherzog/Documents/GitHub/job_traveler_data/data_pipelines/jt_assets/gsc/gsc_upload_from_storage_to_bigquery.py", line 26, in gcs_to_bq load_job = client.load_table_from_dataframe( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/google/cloud/bigquery/client.py", line 2702, in load_table_from_dataframe _pandas_helpers.dataframe_to_parquet( File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/google/cloud/bigquery/_pandas_helpers.py", line 719, in dataframe_to_parquet arrow_table = dataframe_to_arrow(dataframe, bq_schema) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/google/cloud/bigquery/_pandas_helpers.py", line 662, in dataframe_to_arrow bq_to_arrow_array(get_column_or_index(dataframe, bq_field.name), bq_field) File "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/google/cloud/bigquery/_pandas_helpers.py", line 380, in bq_to_arrow_array return pyarrow.Array.from_pandas(series, type=arrow_type) File "pyarrow/array.pxi", line 1099, in pyarrow.lib.Array.from_pandas File "pyarrow/array.pxi", line 323, in pyarrow.lib.array File "pyarrow/array.pxi", line 83, in pyarrow.lib._ndarray_to_array File "pyarrow/error.pxi", line 123, in pyarrow.lib.check_status
s
The error message 
pyarrow.lib.ArrowTypeError: object of type <class 'str'> cannot be converted to int
suggests that there is a mismatch between the data types in your Pandas DataFrame and the expected schema in BigQuery. Specifically, it appears that a column in your DataFrame that is supposed to be of type 
int
(integer) is being represented as a 
str
(string). Here are some steps to troubleshoot and resolve this issue: 1. Check DataFrame Schema: Review the schema of your Pandas DataFrame to ensure that the data types match what is expected by BigQuery. You can use 
dataframe.dtypes
to print out the data types of each column in your DataFrame. 2. Correct Data Types: If you find any columns that are strings but should be integers, you can convert them using the 
pd.to_numeric()
function or by casting the column to the correct type with 
dataframe['column_name'] = dataframe['column_name'].astype(int)
. 3. BigQuery Schema: Ensure that the schema defined in BigQuery for the destination table matches the schema of the DataFrame you are trying to load. The field types must align correctly. 4. Intermediate Conversion: Sometimes, it may be helpful to convert the DataFrame to an Apache Arrow Table before loading it into BigQuery. This can provide more control over the data types and allow you to catch any conversion issues earlier in the process. 5. Validate Data: Check the actual data in the DataFrame to ensure there are no unexpected non-numeric values in columns that are supposed to be integers. 6. Use Schema Auto-Detection: If the schema is not rigidly defined, you could consider using BigQuery's schema auto-detection feature by setting the 
job_config.autodetect
to 
True
when creating the load job. However, this should be used with caution and may not always produce the desired schema. Here's an example of how you might convert a string column to an integer in your DataFrame before loading it into BigQuery:
Copy code
python import pandas as pd  # Assuming 'dataframe' is your Pandas DataFrame # and 'int_column' is the name of the column that should be an integer dataframe['int_column'] = pd.to_numeric(dataframe['int_column'], errors='coerce')  # Now 'int_column' will be converted to numeric values, with any non-convertible values set to NaN # You may want to handle NaN values or rows with errors as appropriate for your use case
After making the necessary adjustments to your DataFrame, you can attempt to load it into BigQuery again. If the issue persists, you may need to further investigate the specific values in the DataFrame that are causing the conversion error.
p
@Scout Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 clicks 1 non-null int64 1 impressions 1 non-null int64 2 ctr 1 non-null float64 3 position 1 non-null float64 4 date 1 non-null object where is there an issue? schema in bigquery: clicks: integer, impressions: integer, ctr: float, position: float, date: date
s
Based on the schema information you've provided for both the Pandas DataFrame and the BigQuery table, the data types seem to align correctly with one exception: the 
date
column. In your DataFrame, the 
date
column is of type 
object
, which typically means it's a string or mixed types in Pandas. However, in your BigQuery schema, the 
date
column is of type 
date
. This discrepancy could potentially cause issues when loading the DataFrame into BigQuery, as BigQuery expects the 
date
column to be in a date format, not a string. To resolve this, you should convert the 
date
column in your DataFrame to a Pandas 
datetime
type before loading it into BigQuery. Here's how you can convert the 
date
column to datetime in Pandas:
Copy code
python import pandas as pd  # Assuming 'dataframe' is your Pandas DataFrame # Convert the 'date' column to datetime dataframe['date'] = pd.to_datetime(dataframe['date'])  # Now the 'date' column in your DataFrame is in datetime format
After converting the 
date
column to the correct type, you should be able to load the DataFrame into BigQuery without encountering the 
ArrowTypeError
. If the error persists, it may be related to another column or a different issue, and you might need to inspect the DataFrame and the BigQuery table schema more closely to identify any other discrepancies.
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