@Marvin using prefect_dbt, if I pass a path to profiles.yml and have env variables pulled like this:

analytics:
  target: dev
  outputs:
    local:
      type: clickhouse
      schema: dbt_dev
      host: localhost
      port: 8123
      user: default
      password: ''
      secure: False
      timeout: 6000
      send_receive_timeout: 6000
    dev:
      type: clickhouse
      schema: dbt_dev
      host:  "{{ env_var('CLICKHOUSE_HOST') }}"
      port: 80
      user: "{{ env_var('CLICKHOUSE_USER') }}"
      password: "{{ env_var('CLICKHOUSE_PW') }}"
      secure: False
      timeout: 6000
      send_receive_timeout: 6000
      use_lw_deletes: True

How can I change the env var of CLICKHOUSE_HOST to a prefect secret

To use Prefect secrets for setting environment variables for your

dbt

profiles.yml

, you would need to retrieve the secret and then dynamically generate the

profiles.yml

content with the secrets filled in. However, from the documentation search, there isn't a direct way to inject Prefect secrets into

profiles.yml

covered in the material provided. Nonetheless, typically, this could be achieved by using Prefect tasks to pull secrets and then write a

profiles.yml

with the secret values inserted into the appropriate places. For instance, using Prefect's

Secret

task to get the values and then a custom task to create or overwrite

profiles.yml

. You'd do something like this:

python
from prefect import Flow, task
from prefect.tasks.secrets import PrefectSecret

@task
def generate_profiles_yml(clickhouse_host):
    profiles_template = """
    analytics:
      target: dev
      outputs:
        local:
          ...
        dev:
          type: clickhouse
          schema: dbt_dev
          host:  "{}"
          port: 80
          user: "{{ env_var('CLICKHOUSE_USER') }}"
          password: "{{ env_var('CLICKHOUSE_PW') }}"
          secure: False
          ...
    """.format(clickhouse_host)
    with open("path/to/profiles.yml", "w") as f:
        f.write(profiles_template)

with Flow("DBT Flow") as flow:
    clickhouse_host = PrefectSecret("CLICKHOUSE_HOST")
    generate_profiles_yml(clickhouse_host)

You'd need to set the

CLICKHOUSE_HOST

secret in the Prefect UI, and the task will retrieve it and use it to generate

profiles.yml

. Remember to customize the

profiles.yml

template to match your exact requirements, and to securely manage and store this file, especially since it contains sensitive connection details. If you are looking for a more seamless integration with Prefect dbt, and if the documentation doesn't already cover it, I would recommend checking on the Prefect community Slack or Forums for user-contributed patterns, or raise an issue on the Prefect GitHub to ask for this feature to be documented or enhanced.

@Marvin How can I create a

DbtCliProfile

but with the following settings:

prod:
      type: clickhouse
      schema: dbt
      host:  "SOME_CLICKHOUSE_HOST"
      port: 80
      user: "SOME_CLICKHOUSE_USER"
      password: "SOME_PW"
      secure: False
      timeout: 6000
      send_receive_timeout: 6000

To create a

DbtCliProfile

with the specified settings using Prefect, you need to configure a

DbtCliProfile

object and provide the necessary target configurations. Since you're using ClickHouse, we assume you have some ClickHouse connector class or configuration available. Here's the basic outline of how you could set this up, assuming you have a ClickHouse connector:

python
from prefect_dbt import DbtCoreOperation, DbtCliProfile
from prefect_dbt.cli.configs import ClickHouseTargetConfigs

# Replace these values with your actual settings
clickhouse_settings = {
    "host": "SOME_CLICKHOUSE_HOST",
    "port": 80,
    "user": "SOME_CLICKHOUSE_USER",
    "password": "SOME_PW",
    "schema": "dbt",
    "timeout": 6000,
    "send_receive_timeout": 6000,
    "secure": False
}

target_configs = ClickHouseTargetConfigs(
    type="clickhouse",
    schema=clickhouse_settings['schema'],
    host=clickhouse_settings['host'],
    port=clickhouse_settings['port'],
    user=clickhouse_settings['user'],
    password=clickhouse_settings['password'],
    secure=clickhouse_settings['secure'],
    timeout=clickhouse_settings['timeout'],
    send_receive_timeout=clickhouse_settings['send_receive_timeout']
)

dbt_cli_profile = DbtCliProfile(
    name="jaffle_shop",
    target="prod",
    target_configs=target_configs
)

dbt_run = DbtCoreOperation(
    commands=["dbt run"],
    dbt_cli_profile=dbt_cli_profile,
    overwrite_profiles=True
)

dbt_run.run()

### Breakdown 1. ClickHouseTargetConfigs Initialization: This initializes the target configurations specifically for ClickHouse with the given settings. 2. DbtCliProfile Creation: This creates the DBT CLI profile with the name, target, and the configurations provided. 3. DbtCoreOperation Initialization: This sets up the DBT operation with the profile and the necessary commands. Make sure the

ClickHouseTargetConfigs

class supports all the parameters you need to set (you might need to adjust the parameters based on the exact implementation of

ClickHouseTargetConfigs

). If the

ClickHouseTargetConfigs

doesn't perfectly match your requirements, you may need to adapt the class or set additional parameters manually. ### Documentation and Further Reading For further customization and information, please refer to Prefect's dbt documentation and the Prefect dbt GitHub repository. If you encounter any issues or need more specific features, consider reaching out to the Prefect community for support.

@Marvin Im getting the following error ImportError: cannot import name 'ClickHouseTargetConfigs' from 'prefect_dbt.cli.configs' (/Users/quinnalfaro/code/analytics-app/.venv/lib/python3.11/site-packages/prefect_dbt/cli/configs/__init__.py)

The error you're encountering is because there isn't a

ClickHouseTargetConfigs

class within the

prefect_dbt.cli.configs

module. It seems your attempt was based on an example likely tailored for a different database like Snowflake. For databases like ClickHouse, if

prefect-dbt

doesn't provide a built-in configuration class, you can create a custom target configuration class inheriting from Prefect's base target config class. Here's an example of how you might implement it: 1. Create a Custom Target Config Class:

python
   from prefect_dbt.cli.configs import BaseTargetConfigs

   class ClickHouseTargetConfigs(BaseTargetConfigs):
       def __init__(self, type, schema, host, port, user, password, secure, timeout, send_receive_timeout):
           super().__init__()
           self.type = type
           self.schema = schema
           self.host = host
           self.port = port
           self.user = user
           self.password = password
           self.secure = secure
           self.timeout = timeout
           self.send_receive_timeout = send_receive_timeout

2. Use the Custom Configuration in Your Flow:

python
   from prefect_dbt import DbtCoreOperation, DbtCliProfile

   clickhouse_settings = {
       "host": "SOME_CLICKHOUSE_HOST",
       "port": 80,
       "user": "SOME_CLICKHOUSE_USER",
       "password": "SOME_PW",
       "schema": "dbt",
       "timeout": 6000,
       "send_receive_timeout": 6000,
       "secure": False
   }

   target_configs = ClickHouseTargetConfigs(
       type="clickhouse",
       schema=clickhouse_settings['schema'],
       host=clickhouse_settings['host'],
       port=clickhouse_settings['port'],
       user=clickhouse_settings['user'],
       password=clickhouse_settings['password'],
       secure=clickhouse_settings['secure'],
       timeout=clickhouse_settings['timeout'],
       send_receive_timeout=clickhouse_settings['send_receive_timeout']
   )

   dbt_cli_profile = DbtCliProfile(
       name="jaffle_shop",
       target="prod",
       target_configs=target_configs
   )

   dbt_run = DbtCoreOperation(
       commands=["dbt run"],
       dbt_cli_profile=dbt_cli_profile,
       overwrite_profiles=True
   )

   dbt_run.run()

### Breakdown: 1. Custom Class: I created a custom

ClickHouseTargetConfigs

class inheriting from

BaseTargetConfigs

. 2. Customization: Populated the class with your specific settings. 3. Implementation: Integrated this custom configuration into the Prefect flow. ### Note Make sure you have relevant

prefect-dbt

dependencies installed and up to date. For any further assistance or troubleshooting, it might be beneficial to engage with the Prefect community on Slack or refer to the Prefect documentation.

@Marvin it doesn't seem to want to allow me to extend BaseTargetConfigs:

ImportError: cannot import name 'BaseTargetConfigs' from 'prefect_dbt.cli.configs' (/Users/quinnalfaro/code/analytics-app/.venv/lib/python3.11/site-packages/prefect_dbt/cli/configs/__init__.py)

It appears that there might be some confusion or lack of clarity regarding the

BaseTargetConfigs

class. Here are some key takeaways from the search results: - From Prefect's Documentation: The

BaseTargetConfigs

and

TargetConfigs

are mentioned as containing credentials and settings specific to the warehouse you're connecting to. Unfortunately, there's no direct evidence of

ClickHouseTargetConfigs

or a clear guide on extending

BaseTargetConfigs

in Prefect's documentation or GitHub issues related to

prefect-dbt

. ### Alternate Approach A practical workaround is to create a custom DBT profile programmatically without extending

BaseTargetConfigs

. Here's how you might proceed: 1. Define a Custom ClickHouse Profile: Define your custom configuration using a standard dictionary and inject it into

DbtCliProfile

. 2. Integrate into Prefect Flow: Integrate the custom configuration within a Prefect flow. Here’s a practical example of this approach:

python
from prefect import task, Flow
from prefect_dbt import DbtCoreOperation, DbtCliProfile
import yaml

# Define your custom profile configuration for ClickHouse
def create_clickhouse_profile():
    return {
        'prod': {
            'type': 'clickhouse',
            'schema': 'dbt',
            'host': "SOME_CLICKHOUSE_HOST",
            'port': 80,
            'user': "SOME_CLICKHOUSE_USER",
            'password': "SOME_PW",
            'secure': False,
            'timeout': 6000,
            'send_receive_timeout': 6000
        }
    }

@task
def generate_profiles_yml(profile):
    profiles_content = {
        'default': profile
    }
    with open("path/to/profiles.yml", "w") as f:
        yaml.dump(profiles_content, f, default_flow_style=False)

# Use the custom profile within a Prefect flow
with Flow("DBT Flow") as flow:
    profile = create_clickhouse_profile()
    generate_profiles_yml(profile)

    dbt_cli_profile = DbtCliProfile(
        name="jaffle_shop",
        target="prod",
        profile_dir="path/to"
    )

    dbt_run = DbtCoreOperation(
        commands=["dbt run"],
        dbt_cli_profile=dbt_cli_profile,
        overwrite_profiles=True
    )
    dbt_run.run()

if __name__ == "__main__":
    flow.run()

### Breakdown: 1. Custom Profile Function:

create_clickhouse_profile()

defines your ClickHouse profile settings. 2. YAML Generation Task:

generate_profiles_yml(profile)

converts this profile into the appropriate

profiles.yml

format. 3. Integration with Prefect Flow: Uses Prefect's flow and tasks framework to manage the data flow. This approach circumvents the need for inheriting from

BaseTargetConfigs

and directly injects the necessary configurations into your DBT profile. If Prefect's existing classes do not meet your needs, you might consider contributing to the Prefect community or extending Prefect’s CLI tools to better support ClickHouse out-of-the-box.