@Marvin Could you produce a sample .yaml file to work in prefect 3.0 that sources the flow from a gitlab instance, uses a gitlab credentials block, and sets 4 parameters?

To create a

.yaml

file for Prefect 3.0 that sources a flow from a GitLab instance using a GitLab credentials block, and sets four parameters, follow the structure below. Note that you'll need to replace placeholders with your actual configuration values:

yaml
# prefect.yaml

version: 3.0
name: my-flow-deployment
description: Deployment for my flow sourced from GitLab

# Define parameters for the flow
parameters:
  param1: default_value1
  param2: default_value2
  param3: default_value3
  param4: default_value4

# Define the source of the flow
pull:
  - git_clone:
      repository: "<https://gitlab.com/your-repo.git>"
      branch: "main"
      credentials: "{{ prefect.blocks.gitlab-credentials.your-credentials-block }}"

# Define any other deployment specific settings
settings:
  some_setting: some_value

### Key Sections: - parameters: Define your flow parameters with default values. - pull: Use

git_clone

to source your flow from a GitLab repository. The

credentials

field should reference a GitLab credentials block stored in Prefect. - settings: Include any additional deployment settings as needed. For more details, you can refer to the Prefect YAML documentation.

@Marvin Great work. Can you list some of the most common settings? I'm looking for a structure that I can test with to find what works best for me. Include some schedule settings and job variable examples if you can

Here's a more detailed look at some common settings you can use in your

prefect.yaml

file, including scheduling and job variables: ### Common Settings - name: The name of your deployment. - description: A brief description of what the deployment does. - version: The version of the deployment. - parameters: Define parameters that your flow will accept. - tags: Tags to categorize or filter your deployments. ### Schedule Settings You can use RRule strings to define complex schedules:

yaml
schedule:
  rrule: "DTSTART:20231101T090000Z\nRRULE:FREQ=WEEKLY;BYDAY=MO,WE,FR"
  timezone: "UTC"

This example schedules a flow to run every Monday, Wednesday, and Friday at 9 AM UTC. ### Job Variables You can define job variables to customize the execution environment:

yaml
job_variables:
  environment:
    VAR1: "value1"
    VAR2: "value2"

These variables can be overridden when running flows, and are visible in the Prefect UI under the Configuration tab. For more detailed and tailored examples, you might want to check the Prefect YAML documentation and the scheduling documentation.

@Marvin What is the different use case for job variables compared to parameters? It seems like job variables would always be superior since they can be edited when running flows and are more flexible as a result.

The distinction between job variables and parameters in Prefect serves different purposes and use cases: ### Parameters - Purpose: Parameters are used to define inputs that your flow logic can use. They are part of the flow's signature and can influence the flow's behavior or output. - Use Case: Use parameters when you need to pass data into the flow that affects its logic or when different runs of the flow need to operate under different conditions or data inputs. - Flexibility: Parameters are defined at the flow level and are typically set when the flow is deployed or triggered. They are meant to be consistent inputs for the flow's operations. ### Job Variables - Purpose: Job variables are used to define environment-specific settings or configurations that can affect how the flow is executed, such as environment variables or resource limits. - Use Case: Use job variables when you need to configure the execution environment of the flow, such as setting environment variables or specifying resource constraints. They are particularly useful for managing deployment-specific settings. - Flexibility: Job variables can be overridden at runtime, allowing for dynamic adjustments to the execution environment without altering the flow's logic. ### Conclusion - Parameters are best for data-specific inputs that the flow logic will use. - Job Variables are ideal for configuring the execution environment and can be adjusted without changing the flow's logic. In essence, parameters and job variables serve complementary roles: parameters for flow logic and job variables for execution environment configuration.

@Marvin what are some common use cases for job variables / environment-specific settings?

Job variables are particularly useful for configuring the execution environment of a flow, and they can be tailored to suit various deployment scenarios. Here are some common use cases: ### Common Use Cases for Job Variables 1. Environment Configuration: - API Keys and Secrets: Store sensitive information like API keys, tokens, or database credentials as environment variables. This ensures they are not hard-coded in your flow logic. - Service URLs: Define service endpoints or database connection strings that can vary between development, staging, and production environments. 2. Resource Management: - Resource Limits: Set limits on CPU, memory, or disk usage for the flow's execution environment to ensure it runs within the constraints of your infrastructure. - Concurrency Controls: Manage the number of concurrent runs or threads to optimize resource usage and avoid overloading systems. 3. Dynamic Configuration: - Feature Flags: Use job variables to enable or disable features dynamically without changing the flow code. - Logging Levels: Adjust logging verbosity based on the environment (e.g., more detailed logs in development). 4. Deployment Flexibility: - Region-Specific Settings: Configure region-specific settings such as time zones, currency formats, or language preferences. - Environment-Specific Behavior: Modify flow behavior based on the environment, such as using mock services in testing environments. 5. Overriding Defaults: - Default Values: Override default values for certain configurations at runtime without redeploying or modifying the flow. By using job variables, you can create a flexible and adaptable flow deployment that can easily adjust to different environments and operational needs.

@Marvin Thanks for your help so far. I want to run a generic flow that always executes some sql, but I'd like to be able to select which sql query is run from the prefect ui if I'm doing a custom run. How can I do that with self hosted prefect 3?