<@ULVA73B9P> I when I make changes to certain environment variables I am passing through a .env.prod...

Daniel

09/12/2024, 7:56 AM

@Marvin I when I make changes to certain environment variables I am passing through a .env.prod file to my prefect deployment, prefect doesn’t seem to pickup these changes even when I re-deploy the pipeline. why might this be?

Marvin

09/12/2024, 7:56 AM

Your environment variables are having an identity crisis – they aren't being detected even after redeployment. Could you let me know which version of Prefect you are using so I can help you better?

Daniel

09/12/2024, 7:57 AM

@Marvin prefect version 2.20.2

Marvin

09/12/2024, 7:57 AM

To help Prefect pick up the changes in your environment variables from the

.env.prod

file, ensure you follow these guidelines: 1. Define Job Variables in `prefect.yaml`: Add a

job_variables

section to your deployment definition in the

prefect.yaml

file to explicitly provide the environment variables. Example:

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yaml
   deployments:
   - name: demo-deployment
     entrypoint: demo_project/demo_flow.py:some_work
     work_pool:
       name: local
     job_variables:
       env:
         YOUR_ENV_VARIABLE: your_value
     schedule: null

2. Redeploy the Pipeline: After making changes to your deployment definition, redeploy the flow using:

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prefect deploy -n demo-deployment

3. Check Deployment Configuration: Verify the job variables in the "Configuration" tab of the deployment in the Prefect UI to ensure they are correctly set. For more details, refer to this guide.

Daniel

09/12/2024, 8:15 AM

@Marvin I am not passing individual env variables. I am passing an .env.prod file. this works fine on my ohter prefect project that is using prefect 2.19.1 but for my project using prefect 2.20.2 it seems to pickup the correct env file but then all the env variables seem to be from my dev env file rather than my prod env file

Marvin

09/12/2024, 8:16 AM

It seems the issue might be related to how Prefect 2.20.2 handles environment variables compared to 2.19.1. To ensure your .env file is being correctly picked up, follow these steps: 1. Update Deployment Configuration: Ensure your deployment configuration explicitly loads the

.env.prod

file. This might need to be defined in your

flow

script or setup. 2. Check For Known Issues: This GitHub Issue mentions some deployment inconsistencies in Prefect 2.x which might be affecting your setup. 3. Verify Job Variables: Cross-check that your

job_variables

in the

prefect.yaml

file are consistent and ensure the right environment file is specified:

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yaml
   deployments:
   - name: demo-deployment
     entrypoint: demo_project/demo_flow.py:some_work
     work_pool:
       name: local
     job_variables:
       env_file: .env.prod
     schedule: null

4. Configuration in Flow Script: Ensure your flow script correctly loads environment variables, e.g.,

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python
   from dotenv import load_dotenv
   load_dotenv(dotenv_path='.env.prod')

For further details on overriding job variables, consult this guide. If the issue persists, consider downgrading Prefect to 2.19.1 as a temporary workaround.

Daniel

09/12/2024, 8:19 AM

@Marvin I am passing the env file like this:

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work_pool:
    name: cloud-run-work-pool
    work_queue_name: default
    schedules: []
    job_variables:
      image: '{{ build_image.image }}'
      env:
        ENV_FILE: '.env.prod'
  schedules: []

Is that correct? I then load it in my python script like this:

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env_file = os.getenv('ENV_FILE', '.env')
load_dotenv(env_file)

But then when I log them in my flow like so:

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print("cwd: ", PROJ_ROOT)
    print("test_ip: ", test_ip)
    print("env_file: ", env_file)
    print("db_host: ", db_host)

They are all loading the dev env variables except for env_file which says it is using the prod env file

Marvin

09/12/2024, 8:20 AM

Let's troubleshoot step-by-step: 1. Confirm File Structure and Content: Ensure

.env.prod

contains the correct production variables. 2. Python Script Handling: - You correctly load the

.env.prod

file using

load_dotenv(env_file)

. Ensure this script runs before accessing any environment variables. - Verify that

.env.dev

is not being sourced implicitly elsewhere. 3. Deployment Configuration: Your

prefect.yaml

configuration looks mostly correct but ensure the structure exactly matches Prefect guidelines.

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yaml
   work_pool:
     name: cloud-run-work-pool
     work_queue_name: default
     schedules: []
     job_variables:
       image: '{{ build_image.image }}'
       env:
         ENV_FILE: '.env.prod'

Note: Ensure there is no extra

schedules: []

after

job_variables

. 4. Environment Variable Loading Order: Check that the environment variables are loaded correctly. Make sure the

load_dotenv

line in your Python script appears at the top, before using any environment variables. 5. Deploy and Verify: Re-deploy the flow to ensure the configuration is updated:

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bash
   prefect deploy -n demo-deployment

If the problem persists: - Debugging: Add debugging statements to ensure the correct file is being loaded:

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python
  print(f"Loading environment variables from: {env_file}")
  load_dotenv(env_file)
  print(f"DB_HOST: {os.getenv('DB_HOST')}")

- Check for Known Issues: Verify any recent issues reported in Prefect's GitHub repository that might match this behavior. For exact steps: 1. Double-check this Prefect guide. 2. Re-deploy to apply changes correctly. If everything checks out and the issue persists, consider raising an issue on Prefect's GitHub for a closer inspection.

Daniel

09/12/2024, 8:21 AM

@Marvin Again this works as expected on prefect version 2.19.1 but not version 2.20.2. Can you check prefect releases to show me what is different between these releases?

Marvin

09/12/2024, 8:22 AM

I couldn't find the exact release notes between Prefect versions 2.19.1 and 2.20.2. However, there are some relevant changes based on issues reported and typical versioning practices. Here are some pointers: - Environment Variable Handling: Changes in how environment variables are processed, especially across different deployment configurations, could have been introduced. - Job Variables & Deployment: Given that other users had deployment issues with newer versions, such as needing all dependencies at deploy time, this might also affect how

.env

files are loaded. ### Recommendations: 1. Explicit Loading in Python: Make sure you explicitly load the environment file at the top of your flow script:

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python
   from dotenv import load_dotenv
   import os

   env_file = os.getenv('ENV_FILE', '.env')
   load_dotenv(env_file)

2. Inspect Deployment: Confirm there's no implicit loading of

.env.dev

in your deployment scripts or environments. 3. Consistency Checks: Ensure consistency in

.env

file content and paths across different projects. ### Additional Resources: - Review the Prefect docs for details. - You can also keep an eye on current discussions and issues tracked in the Prefect GitHub repository. If the problem persists, you might consider rolling back to Prefect 2.19.1 temporarily or creating a bug report on Prefect's GitHub.

Daniel

09/12/2024, 8:24 AM

@Marvin should i load the env file inside the flow function itself or is it ok to load it at the top of the python file just after the python imports?

Marvin

09/12/2024, 8:25 AM

Loading the

.env

file at the top of your Python script, just after the imports, is generally a good practice. This ensures that all environment variables are set before any code that relies on them is executed. Here's a concise example:

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python
from dotenv import load_dotenv
import os

# Load environment variables
env_file = os.getenv('ENV_FILE', '.env')
load_dotenv(env_file)

# Import other dependencies
from prefect import flow

@flow
def my_flow():
    # Environment variables should now be available
    db_host = os.getenv('DB_HOST')
    print(f"DB_HOST: {db_host}")

if __name__ == "__main__":
    my_flow()

By loading the environment variables at the top, you ensure they are available wherever needed in your script, including within your flow functions. This avoids any confusion or issues that might arise from variables not being set when required.

Daniel

09/12/2024, 8:26 AM

@Marvin yes this is what I am doing and it’s still reading eh .env file rather than the .env.prod file. What is confusing is that it says the env file is set to .env.prod but then the actual variables being read are from the .env file (should be from the .env.prod file!)

Marvin

09/12/2024, 8:26 AM

Let's run through a few additional checks to troubleshoot this further: 1. Verify File Paths: Ensure the

.env.prod

file's path is correct and accessible from your script’s location.

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python
   import os
   print(f"Environment file: {env_file}")
   print(f"File Exists: {os.path.exists(env_file)}")

2. Contents of
.env
and `.env.prod`: Ensure there’s no overlap or conflicting variables between

.env

and

.env.prod

. Comments and unintended white spaces could affect parsing. 3. Order of Loading: Check if

.env

is being implicitly loaded before

.env.prod

anywhere in your deployment configuration or scripts. 4. Explicit
.env.prod
Loading: Make sure to explicitly load

.env.prod

without a fallback to

.env

for testing:

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python
   from dotenv import load_dotenv
   import os

   env_file = '.env.prod'  # Hardcode for testing
   if not os.path.exists(env_file):
       raise FileNotFoundError(f"{env_file} not found")

   load_dotenv(env_file)
   print(f"Loaded environment file: {env_file}")

   # Now check if variables are correctly loaded
   db_host = os.getenv('DB_HOST')
   print(f"DB_HOST: {db_host}")

If this confirms the issue lies in how Prefect reads configurations: 5. Prefect Deployment Config: Double-check that your Prefect deployment script and configuration files (

prefect.yaml

, etc.) are not altering the environment loading. - Ensure no

.env

file is silently being loaded in these configs. - Verify with:

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yaml
   work_pool:
     name: cloud-run-work-pool
     work_queue_name: default
     job_variables:
       image: '{{ build_image.image }}'
       env:
         ENV_FILE: '.env.prod'

6. Consistency Across Versions: Given the difference in versions, you might have found a regression or behavior change in Prefect 2.20.2. Check Prefect’s release notes for undocumented changes and consider reporting this discrepancy. If none of these resolve the issue, it would be beneficial to raise it with Prefect’s support or on their GitHub. Temporary downgrading to 2.19.1 for production-critical deployments could be a short-term mitigation.

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