I’m trying to write some dataframes to BigQuery, but stuck at authentication. I encoded my GCP service account key (json) to base64, but I keep getting

binascii.Error: Incorrect padding

error. When I decode it back to utf-8, I have the same json so I assume the format is correct. Do I need to add arguments in the gcp_credentials of the BigQueryResource or something?

When using the

BigQueryResource

or

BigQueryPandasIOManager

in Dagster, you can provide GCP authentication credentials by setting the

gcp_credentials

configuration value. According to the documentation, if you provide this configuration, Dagster will create a temporary file with the credentials and set the

GOOGLE_APPLICATION_CREDENTIALS

environment variable to point to this temporary file. To avoid issues with newlines in the keys, you must base64 encode the key. However, if you're encountering a

binascii.Error: Incorrect padding

error, this typically indicates that the base64 encoded string is not correctly padded. Base64 encoding divides the input bytes into groups of three and outputs four encoded characters for each group. If the number of input bytes is not divisible by three, the output is padded with one or two

=

characters to make the output divisible by four. Here are a few steps you can take to troubleshoot this issue: 1. Ensure that you are encoding the entire JSON key file, including any newlines that may be present within the file. 2. Check that the base64 encoded string ends with zero, one, or two

=

padding characters. If it does not, the encoding may be incorrect. 3. When you decode the base64 string, ensure that you are not inadvertently altering the string or stripping padding characters. 4. When providing the base64 encoded string to Dagster, make sure that it is passed correctly as a configuration value without any modifications or truncations. The documentation provides an example shell command to retrieve the base64 encoded key:

shell cat $GOOGLE_AUTH_CREDENTIALS | base64

And here is an example of how you might provide the encoded credentials to the

BigQueryResource

or `BigQueryPandasIOManager`:

python from dagster_gcp import BigQueryResource from dagster import resource  @resource def my_bigquery_resource(context):     return BigQueryResource(         project='my-gcp-project',         gcp_credentials=context.resource_config['gcp_credentials']     )  # In your Dagster repository YAML configuration, you would then provide the base64 encoded key: resources:   my_bigquery_resource:     config:       gcp_credentials: "base64-encoded-key"

Make sure that the base64-encoded key is provided as a string in the configuration without any additional characters or quotes that might alter the content. If you continue to experience issues, double-check the encoded credentials and consider regenerating and re-encoding the key to ensure it is correct.

@Scout how do I save the encoded base64 string to the .env file and call the EnvVar dagster function?

To use environment variables with Dagster, you can set them in a

.env

file and then access them in your Dagster code using the

EnvVar

function. Here's how you can save the base64-encoded GCP service account key to a

.env

file and use it with `EnvVar`: 1. Open your

.env

file and add the base64-encoded GCP service account key as an environment variable. For example:

GCP_CREDENTIALS_BASE64=your-base64-encoded-key-here

Make sure to replace

your-base64-encoded-key-here

with your actual base64-encoded service account key. 2. In your Dagster code, you can access this environment variable using the

EnvVar

function. Here's an example of how you might define a resource that uses this environment variable:

python from dagster import resource, Field, String from dagster_gcp import BigQueryResource  @resource({     "gcp_credentials": Field(String, is_required=False, description="The base64-encoded GCP credentials") }) def my_bigquery_resource(context):     gcp_credentials_base64 = context.resource_config.get('gcp_credentials')     if gcp_credentials_base64:         # Decode the base64-encoded credentials         import base64         gcp_credentials_json = base64.b64decode(gcp_credentials_base64).decode("utf-8")         # Use the credentials to create a BigQueryResource         return BigQueryResource(             project='my-gcp-project',             gcp_credentials=gcp_credentials_json         )     else:         # Handle the case where credentials are not provided         raise Exception("GCP credentials must be provided.")  # In your repository or pipeline configuration, you would use the EnvVar function: resources:   my_bigquery_resource:     config:       gcp_credentials:         env: GCP_CREDENTIALS_BASE64

In this example, the

my_bigquery_resource

function checks if the

gcp_credentials

configuration is provided. If it is, it decodes the base64-encoded credentials and uses them to create a

BigQueryResource

. The

EnvVar

function is used in the configuration to specify that the

gcp_credentials

should be sourced from the

GCP_CREDENTIALS_BASE64

environment variable set in the

.env

file. Make sure that the

.env

file is located in the same directory where you run your Dagster commands, as Dagster will automatically load the environment variables from the

.env

file in that location.

@Scout in your documentation, you have this :

defs = Definitions(
    assets=[iris_data],
    resources={
        "io_manager": BigQueryPandasIOManager(
            project="my-gcp-project",
            location="us-east5",
            dataset="IRIS",
            timeout=15.0,
            gcp_credentials=EnvVar("GCP_CREDS"),
        )
    },
)

I did exactly the same thing, decoded the json, and passed it on as an EnvVar to the gcp_credentials argument. I have my resources defined in the init.py under the resources folder, and import it into the definitions init.py file.

Based on the documentation, using

EnvVar

to pass environment variables to your Dagster resources is a valid approach. The

EnvVar

function is used to reference an environment variable that will be resolved at runtime. When you use

EnvVar("GCP_CREDS")

, Dagster expects an environment variable named

GCP_CREDS

to be available in the environment where the Dagster process is running. Here's a step-by-step guide on how to set this up: 1. Ensure that your

.env

file contains the base64-encoded GCP service account key as an environment variable:

GCP_CREDS=your-base64-encoded-key-here

2. In your Dagster resource definition, use

EnvVar

to reference the

GCP_CREDS

environment variable:

python from dagster import Definitions from dagster_gcp_pandas import BigQueryPandasIOManager from dagster import EnvVar  defs = Definitions(     assets=[...],  # your assets here     resources={         "io_manager": BigQueryPandasIOManager(             project="my-gcp-project",             location="us-east5",             dataset="IRIS",             timeout=15.0,             gcp_credentials=EnvVar("GCP_CREDS"),         )     }, )

3. Make sure that the

.env

file is in the correct location. If you're running Dagster locally, the

.env

file should be in the root directory of your project or in the same directory from which you run Dagster commands. 4. When you run your Dagster process, it will load the environment variables from the

.env

file and use the

GCP_CREDS

variable to provide the base64-encoded credentials to the

BigQueryPandasIOManager

. If you've followed these steps and are still encountering issues, here are a few things to check: - Verify that the

.env

file is correctly formatted and that there are no typos in the environment variable name. - Ensure that the

.env

file is being loaded properly. If you're using a tool like

dotenv

or running Dagster with Dagit, it should automatically load the

.env

file. If you're running Dagster in a different environment (like a container or a cloud environment), you'll need to make sure the environment variables are set appropriately. - Check that the base64-encoded credentials are correct and that they have not been altered or truncated when saving them to the

.env

file. - Ensure that the

GCP_CREDS

environment variable is not being overwritten by another value in your environment. If you've checked all of these and the issue persists, you may want to add logging to your resource initialization code to confirm that the credentials are being loaded and decoded correctly.

@Scout yeah it doesn’t work still, I have no idea why

I'm sorry to hear that you're still experiencing issues. Debugging environment variable and authentication issues can be tricky, but here are some steps you can take to troubleshoot the problem: 1. Verify Environment Variable Loading: Ensure that the environment variable is being loaded correctly. You can do this by temporarily adding a print statement in your code to print out the value of the environment variable after it's supposed to be loaded by

EnvVar

. 2. Check Base64 Encoding: Double-check the base64 encoding of your GCP credentials. Ensure that you're encoding the file correctly and that no extra characters or line breaks have been introduced. You can also try decoding it manually using a command-line tool or an online base64 decoder to verify that it decodes back to the original JSON without any issues. 3. Inspect Decoded Credentials: After decoding the base64 string, inspect the JSON to ensure that it is a valid GCP service account key with all necessary fields. 4. Test Credentials Outside of Dagster: As a sanity check, try using the decoded credentials outside of Dagster to authenticate with GCP directly, for example using the

gcloud

command-line tool or a small Python script that uses the

google-cloud-bigquery

library. This can help determine if the issue is with the credentials themselves or with how they're being used in Dagster. 5. Check for Overwrites: Ensure that the

GCP_CREDS

environment variable is not being overwritten elsewhere in your environment or code. Environment variables can be set at various scopes (e.g., system, user, process), and a variable set at a higher scope could potentially overwrite the one you're setting in your

.env

file. 6. Review Dagster Resource Configuration: Review the configuration of your

BigQueryPandasIOManager

resource to ensure that all other parameters are correct and that the

gcp_credentials

parameter is the only one causing issues. 7. Check Dagster Logs: Look at the logs generated by Dagster when you run your pipeline. They might contain additional information or error messages that can help pinpoint the issue. 8. Consult Dagster Community: If you're still stuck, consider reaching out to the Dagster community. You can ask for help on the Dagster Slack channel or create an issue on the Dagster GitHub repository with details of the problem you're facing. Remember to remove or redact any sensitive information such as actual credential content before sharing your code or logs publicly. If you're able to share more specific error messages or logs (without sensitive data), that could also help in diagnosing the issue.

@Scout tested 1. - it loads the same string tested 2. - tried it with 3 different methods, I get the same string tested 3. - I decode the string, and get the same json file what seem to have sorted it temporarily is if I literally hard-code the decoded string instead of calling it with EnvVar. Something like:

bq_resource = BigQueryResource(
			project="project", 
            location="location",
            gcp_credentials="decoded-credentials"
	)

why would EnvVar affect the padding?

It's puzzling that hard-coding the decoded credentials works, but using

EnvVar

does not, especially since you've verified that the environment variable contains the correct base64 string. This suggests that the issue may not be with the credentials themselves but rather with how the environment variable is being interpreted or used within Dagster. Here are a few possibilities to consider: - Environment Variable Interpretation: When you use

EnvVar

, Dagster retrieves the environment variable's value at runtime. If there's any difference in how the environment variable is being interpreted (e.g., extra whitespace, encoding issues), it could potentially lead to the padding error you're seeing. - String Handling: It's possible that the way strings are handled when passed directly versus through

EnvVar

could differ, leading to subtle issues like padding errors. For example, if the environment variable is being retrieved with additional or missing characters, it could affect the base64 decoding. - Dagster Resource Configuration: There might be something specific about the

BigQueryResource

configuration that behaves differently when using

EnvVar

compared to a direct string. This could be a bug or an undocumented behavior. Given that hard-coding the decoded credentials works, you might want to try the following as a workaround: 1. Use a Python Environment Variable: Instead of using

EnvVar

, access the environment variable directly in Python using

os.getenv

and then pass the result to

gcp_credentials

. For example:

python import os import base64  # Assuming your environment variable is named 'GCP_CREDS_BASE64' encoded_creds = os.getenv('GCP_CREDS_BASE64') decoded_creds = base64.b64decode(encoded_creds).decode('utf-8')  bq_resource = BigQueryResource(     project="project",     location="location",     gcp_credentials=decoded_creds )

2. Check for Invisible Characters: Ensure there are no invisible characters (like Unicode BOM or non-printable characters) in the environment variable that could affect the base64 decoding. 3. File-based Credentials: As another workaround, you could write the decoded credentials to a temporary file and set the

GOOGLE_APPLICATION_CREDENTIALS

environment variable to point to that file before initializing the

BigQueryResource

. If none of these workarounds are satisfactory, it might be worth reaching out to the Dagster community or filing an issue on the Dagster GitHub repository, as this could be indicative of a deeper issue with how

EnvVar

is handling the credentials. When doing so, provide as much detail as possible about your setup and the steps you've taken to debug the issue.

@Scout it’s the oddest thing, running

base64.b64decode(credentials_base64).decode('utf-8')

works in isolation, but not in my Dagster environment. I add a simple print statement at the beginning to my init.py file in my resources folder like this:

from dagster_gcp import BigQueryResource
from dagster import EnvVar
import json
import os
import base64

encoded_creds = os.getenv('GCP_BQ_KEY')
print(encoded_creds)

bq_resource = BigQueryResource...

when I materialise my asset, in the terminal I have the decoded JSON printed out first, twice, and then the encoded string (the one I have saved as environment variable). when I remove the print statement, nothing gets printed out. how does it decode the credentials first and then print them?

The behavior you're describing is indeed unusual. If you're seeing the decoded JSON printed out before the encoded string, it suggests that there might be some code executing before your print statement that decodes and prints the credentials. This could be due to several reasons: 1. Code Execution Order: There might be other parts of your codebase (or imported modules) that are executed before the print statement in your

__init__.py

file. Python modules can have side effects when imported, so if there's an import that triggers code execution, it could lead to the credentials being printed out. 2. Environment Variable Preprocessing: It's possible that the environment variable

GCP_BQ_KEY

is being preprocessed or manipulated by some other part of your system or by a tool that you're using to manage environment variables. This could inadvertently decode and print the credentials before your code runs. 3. Logging Configuration: Check if there's any logging configuration in your Dagster setup that could be capturing and printing out the credentials. This might happen if the credentials are used or logged by a resource or system that initializes before your print statement. 4. Dagster Resource Initialization: Resources in Dagster can have initialization code that runs when the resource is instantiated. If

BigQueryResource

or another resource is somehow printing the credentials during its initialization, this could explain the behavior. To debug this issue, you can try the following steps: - Isolate the Print Statement: Move the print statement to the very top of the

__init__.py

file, before any imports or other code, to see if the credentials are still being printed out decoded. - Search the Codebase: Perform a search across your entire codebase for any print statements or logging that might output the credentials. This includes looking through any custom modules or third-party libraries you're using. - Review Initialization Code: Review the initialization code for all resources and any other code that runs at startup to see if anything could be printing the credentials. - Check Environment Variable Management: If you're using a tool like

dotenv

to manage your environment variables, ensure that it's not doing any preprocessing that could affect the value of

GCP_BQ_KEY

. - Check for Multiple Environment Variable Sources: Ensure that there aren't multiple sources setting the

GCP_BQ_KEY

environment variable, which could lead to unexpected behavior. - Use a Debugger: Run your Dagster environment with a debugger attached and set breakpoints around the area where the credentials are printed. This can help you trace back to where the print is being triggered. If you're unable to resolve the issue wi

@Scout alright I’m not gonna get into this, this is really really weird…. anyways, another issue. when I materialise my asset, I get this error:

Not found: Dataset my-project:dataset_test

I just created the dataset in GCP with the name “dataset_test”. I configured my project in the BigQueryResource class with

bq_resource = BigQueryResource(
			project="my-project",....

and the dataset in my asset like

job = client.load_table_from_dataframe(
					dataframe=df,
					destination=f"dataset_test.{table_name}",
				)

in BigQuery I can see that I have the dataset with the ID

my-project.dataset_test

, which means Dagster replaces a “.” with a “:”. How can i overwrite this?

The error message

Not found: Dataset my-project:dataset_test

indicates that the BigQuery service is unable to find the specified dataset. In BigQuery, the standard SQL notation for referring to datasets and tables is to use a colon (

:

) to separate the project ID from the dataset ID, and a period (

.

) to separate the dataset ID from the table ID. So the full notation for a table would be

project_id:dataset_id.table_id

. In your case, it seems like you have correctly named your dataset

dataset_test

within your project

my-project

. When you are using the BigQuery client in Python to specify the destination for loading a table from a dataframe, you should use the format

project_id.dataset_id.table_id

. Here's how you can specify the destination correctly in your Dagster asset:

python job = client.load_table_from_dataframe(     dataframe=df,     destination=f"{bq_resource.project}.dataset_test.{table_name}", )

In this line,

bq_resource.project

should resolve to

my-project

, and

table_name

should be the name of the table you want to create or overwrite within the

dataset_test

dataset. Make sure that: 1. The

BigQueryResource

is properly configured with the correct project ID. 2. The dataset

dataset_test

exists within the

my-project

project in BigQuery. 3. The account or service account used for authentication has the necessary permissions to access the dataset and create tables within it. If you continue to encounter issues, double-check the dataset's existence and permissions in the Google Cloud Console, and ensure that your Dagster resource is configured with the correct credentials and project ID. If the problem persists, you may want to verify that the dataset name does not include any leading or trailing spaces or other hidden characters that might cause it to not match the name in BigQuery exactly.

@Scout I renamed it in my asset, but still get the same error.

If you've renamed the dataset in your asset code and are still receiving the

Not found: Dataset my-project:dataset_test

error, there are a few additional steps you can take to troubleshoot the issue: 1. Verify Dataset Existence: Double-check that the dataset

dataset_test

exists in the BigQuery web UI under the project

my-project

. Ensure that there are no typos in the dataset name. 2. Permissions: Confirm that the service account or credentials you are using have the necessary permissions to access the dataset and perform operations on it. 3. Project Configuration: Make sure that the

project

parameter in your

BigQueryResource

is set to the correct project ID where the dataset exists. 4. Dataset Location: If your dataset is in a multi-regional or different location than the default, ensure that the location is specified correctly in your BigQuery client configuration. 5. Resource Initialization: Verify that the

BigQueryResource

is being initialized correctly with the right parameters and that it's being used in the asset where you're trying to load the dataframe. 6. Check for Caching: If you're running this in a development environment like Dagit, sometimes restarting the environment can help if there's any caching of old configuration values. 7. Logging: Add logging to your asset code to print out the full destination string before the

load_table_from_dataframe

call to ensure it's being constructed as expected. 8. BigQuery Client Errors: Check if there are more detailed error messages from the BigQuery client that might give additional clues as to why the dataset is not being found. 9. Dagster Asset Definition: Ensure that the asset definition in Dagster is correct and that the resource is being passed correctly to the asset. 10. Hard Refresh: Sometimes, changes in the cloud console can take a moment to propagate. Try a hard refresh in the BigQuery console to ensure the dataset is listed. If you've gone through these steps and the issue still persists, you might want to isolate the problem by running a simple Python script outside of Dagster using the same credentials and BigQuery client to attempt to access the dataset. If this script succeeds, the issue is likely with the Dagster setup; if it fails, the problem is more likely with the BigQuery setup or credentials. If the problem continues to be unresolved, consider reaching out to the Dagster community for support, as they may have encountered similar issues or can provide additional debugging tips. Remember to redact any sensitive information before sharing your code or logs.

@Scout how do I define “multi” as a region in the BigQuery resource?

In the context of the BigQueryResource in Dagster, if you want to specify a "multi" region, it's important to clarify what you mean by "multi" as a region. Google BigQuery does not have a "multi" region per se; instead, it has the concept of multi-regional locations for storing your data. Common multi-regional locations include "US" and "EU". If you're referring to using a multi-regional location for your BigQuery dataset, you can specify the location when you instantiate the

BigQueryResource

. Here's an example of how you might define a resource with a multi-regional location:

python from dagster_gcp import BigQueryResource  bq_resource = BigQueryResource(     project="my-project",     location="US"  # This sets the location to the "US" multi-regional location )

In this example, the

location

parameter is set to

"US"

, which is a valid multi-regional location in BigQuery. You would replace

"US"

with

"EU"

or any other valid location according to your requirements. If you're trying to specify something different with "multi" as a region, please provide additional context so that we can give you a more accurate answer. If you're looking to work with datasets across multiple regions, you would typically handle that within your application logic, as the BigQueryResource itself is tied to a specific project and location.