Based on the research and available information, here's what I can tell you about dictionary variable handling in Prefect 3.x work pools: 1. There isn't a built-in mechanism to automatically merge dictionary variables in the same way that

env

variables are handled, with one exception: if you're using infrastructure-specific dictionary configurations (like in Kubernetes work pools), those may have special merging behavior. 2. For custom dictionary variables, when a deployment overrides a work pool variable, it will completely replace the original dictionary rather than merging with it. However, there are a few workarounds you could consider: 1. Split the Dictionary: Instead of using a single dictionary variable, you could split your configuration into separate variables that can be individually overridden. 2. Custom Merging Logic: You could implement the merging behavior in your flow code, by: - Reading both the work pool default dictionary and the deployment-specific dictionary - Merging them programmatically using Python's dictionary update methods Here's a simple example of how you might implement the second approach:

python
from prefect import flow
from prefect.context import get_run_context

@flow
def my_flow():
    context = get_run_context()
    
    # Get the base dictionary from work pool
    base_config = context.work_pool.base_job_template.job_configuration.get("my_dict", {})
    
    # Get the deployment-specific dictionary
    deployment_config = context.deployment.job_configuration.get("my_dict", {})
    
    # Merge them (base_config will be updated with deployment_config values)
    final_config = {*base_config, *deployment_config}
    
    # Use the merged configuration
    print(f"Final configuration: {final_config}")

If you need this behavior frequently, you might want to create a helper function to handle the merging consistently across your flows. Would you like me to research any specific aspect of this in more detail? For example, I could look into infrastructure-specific dictionary handling for particular work pool types if that would be helpful.

Here's how you would structure this using separate variables: 1. Work Pool Configuration (in your base job template JSON):

json
{
    "variables": {
        "db_host": "default-host",
        "db_port": 5432,
        "db_timeout": 30,
        "db_retry_attempts": 3,
        "db_ssl_mode": "prefer"
    }
}

2. Deployment Configuration (in your deployment code or prefect.yaml):

python
from prefect import flow
from prefect.deployments import Deployment

@flow
def my_flow():
    # Your flow code here
    pass

deployment = Deployment.build_from_flow(
    flow=my_flow,
    name="my-deployment",
    work_pool_name="my-pool",
    job_variables={
        "db_host": "production-host",  # Override just the host
        "db_port": 5433  # Override just the port
        # Other variables will keep work pool defaults
    }
)

Or in your `prefect.yaml`:

yaml
deployments:
  - name: my-deployment
    work_pool:
      name: my-pool
      job_variables:
        db_host: production-host
        db_port: 5433

3. Using the Variables in Your Flow:

python
from prefect import flow
from prefect.context import get_run_context

@flow
def my_flow():
    context = get_run_context()
    
    # Get all database-related variables
    db_config = {
        "host": context.deployment.job_variables.get("db_host"),
        "port": context.deployment.job_variables.get("db_port"),
        "timeout": context.deployment.job_variables.get("db_timeout"),
        "retry_attempts": context.deployment.job_variables.get("db_retry_attempts"),
        "ssl_mode": context.deployment.job_variables.get("db_ssl_mode")
    }
    
    print(f"Database configuration: {db_config}")
    # Use the configuration...

The advantages of this approach are: 1. Granular Control: You can override individual settings without affecting others 2. Clear Visibility: It's easier to see which specific variables are being overridden 3. Default Preservation: Work pool defaults are preserved unless explicitly overridden 4. Flexibility: You can group related variables by using consistent prefixes (like

db_

in this example) The main trade-off is that you need to handle reassembling the dictionary in your flow code if you need the complete configuration as a single dictionary. However, this gives you more control over how the values are combined and used. You could also create a helper function to make this more manageable across multiple flows:

python
from prefect import flow, task

@task
def get_db_config(prefix="db_"):
    context = get_run_context()
    variables = context.deployment.job_variables
    
    # Get all variables that start with the prefix
    return {
        key.replace(prefix, ""): value 
        for key, value in variables.items() 
        if key.startswith(prefix)
    }

@flow
def my_flow():
    db_config = get_db_config()
    print(f"Database configuration: {db_config}")
    # Use the configuration...

This approach gives you the flexibility of separate variables while maintaining the convenience of working with a dictionary in your flow code.

Based on the research, I understand that you want to replicate the merging behavior that Prefect uses for

env

variables, but for your own custom dictionary. Unfortunately, there isn't a built-in way to achieve this exact behavior for custom dictionaries in work pool variables. However, I can suggest a workaround that might meet your needs. You could implement this behavior in your flow code by creating a helper function that merges the dictionaries:

python
from prefect import flow, task
from prefect.context import get_run_context
from typing import Dict, Any

@task
def get_merged_config(variable_name: str) -> Dict[Any, Any]:
    """
    Get a merged configuration dictionary from work pool and deployment variables.
    Work pool values serve as defaults, and deployment values are merged on top.
    """
    context = get_run_context()
    
    # Get the base dictionary from work pool (default to empty dict if not present)
    base_config = context.work_pool.base_job_template.job_configuration.get(variable_name, {})
    
    # Get the deployment-specific dictionary (default to empty dict if not present)
    deployment_config = context.deployment.job_configuration.get(variable_name, {})
    
    # Merge the dictionaries (deployment values take precedence)
    return {*base_config, *deployment_config}

@flow
def my_flow():
    # This will contain all keys from both work pool and deployment
    config = get_merged_config("my_custom_dict")
    print(f"Merged configuration: {config}")

You would then set up your work pool with some default values:

json
{
    "variables": {
        "my_custom_dict": {
            "default_key1": "value1",
            "default_key2": "value2"
        }
    }
}

And in your deployment, you can add new keys without specifying the existing ones:

yaml
deployments:
  - name: my-deployment
    work_pool:
      name: my-pool
      job_variables:
        my_custom_dict:
          new_key3: "value3"
          new_key4: "value4"

When the flow runs,

config

would contain all keys from both sources:

python
{
    "default_key1": "value1",
    "default_key2": "value2",
    "new_key3": "value3",
    "new_key4": "value4"
}

This approach has a few characteristics to be aware of: 1. The merging happens at runtime in your flow code, not at the infrastructure level 2. If you override a key that exists in the work pool, the deployment value takes precedence 3. You can add any new keys in the deployment without affecting the existing work pool keys Would you like me to explain any part of this in more detail or explore alternative approaches?