Managing configurations in your Python applications isn’t something you think about much often, until complexity starts to seep in and forces you to re-architect your initial approach. Ideally, your config management flow shouldn’t change across different applications or as your application begins to grow in size and complexity.

Even if you’re writing a library, there should be a consistent config management process that scales up properly. Since I primarily spend my time writing data-analytics, data-science applications and expose them using Flask1 or FastAPI2 framework, I’ll be tacking config management from an application development perspective.

Few ineffective approaches

In the past, while exposing APIs with Flask, I used to use .env, .flaskenv and Config class approach to manage configs which is pretty much a standard in the Flask realm. However, it quickly became cumbersome to maintain and juggle between configs depending on development, staging or production environments.

There were additional application specific global constants to deal with too. So I tried using *.json, *.yaml or *.toml based config management approaches but those too, quickly turned into a tangled mess. I was constantly accessing variables buried into 3-4 levels of nested toml data structure and it wasn’t pretty.

Then there are config management libraries like Dynaconf3 or environ-config4 that aim to ameliorate the issue. While these are all amazing tools but they also introduce their own custom workflow that can feel over-engineered while dealing with maintenance and extension.

A pragmatic wishlist

I wanted to take a declarative approach while designing a config management pipleline that’ll be modular, scalable and easy to maintain. To meet my requirements, the system should be able to:

  • Read configs from .env files and shell environment at the same time.
  • Handle dependency injection for introducing passwords or secrets.
  • Convert variable types automatically in the appropriate cases, e.g. string to integer conversion.
  • Keep development, staging and production configs separate.
  • Switch between the different environments e.g development, staging effortlessly.
  • Inspect the active config values
  • Create arbitrarily nested config structure if required (Not encouraged though).

Building the config management pipeline


The code block that appears in this section is self contained. It should run without any modifications. If you want to play along, then just spin up a Python virtual environment and install Pydantic and python-dotenv. The following commands works on any *nix based system:

python3.10 -m venv venv
source venv/bin/activate
pip install pydantic python-dotenv

Make sure you have fairly a recent version of Python 3 installed, preferably Python 3.10+. You might need to install python3.10 venv.

Introduction to Pydantic

To check off all the boxes of the wishlist above, I made a custom config management flow using Pydantic5, python-dotenv6 and the .env file. Pydantic is a fantastic data validation library that can be used for validating and implicitly converting data types using Python’s type hints. Type hinting is a formal solution to statically indicate the type of a value within your Python code. It was specified in PEP-4847 and introduced in Python 3.5. Let’s define and validate the attributes of a class named User:

from Pydantic import BaseModel

class User(BaseModel):
    name: str
    username: str
    password: int

user = User(name="Redowan Delowar", username="rednafi", password="123")


This will give you:

>>> User(name='Redowan Delowar', username='rednafi', password=123)

In the above example, I defined a simple class named User and used Pydantic for data validation. Pydantic will make sure that the data you assign to the class attributes conform with the types you’ve annotated. Notice, how I’ve assigned a string type data in the password field and Pydantic converted it to integer type without complaining. That’s because the corresponding type annotation suggests that the password attribute of the User class should be an integer. When implicit conversion is not possible or the hinted value of an attribute doesn’t conform to its assigned type, Pydantic will throw a ValidationError.

The orchestration

Now let’s see how you can orchestrate your config management flow with the tools mentioned above. For simplicity, let’s say you’ve 3 sets of configurations.

  1. Configs of your app’s internal logic
  2. Development environment configs
  3. Production environment configs

In this case, other than the first set of configs, all should go into the .env file.

I’ll be using this .env file for demonstration. If you’re following along, then go ahead, create an empty .env file there and copy the variables mentioned below:


ENV_STATE="dev" # or prod



Notice how I’ve used the DEV_ and PROD_ prefixes before the environment specific configs. These help you discern between the variables designated for different environments.

Configs related to your application’s internal logic should either be explicitly mentioned in the same or imported from a different file. You shouldn’t pollute your .env files with the internal global variables necessitated by your application’s core logic.

Now let’s dump the entire config orchestration and go though the building blocks one by one:


from typing import Optional

from pydantic import BaseSettings, Field, BaseModel

class AppConfig(BaseModel):
    """Application configurations."""

    VAR_A: int = 33
    VAR_B: float = 22.0

class GlobalConfig(BaseSettings):
    """Global configurations."""

    # These variables will be loaded from the .env file. However, if
    # there is a shell environment variable having the same name,
    # that will take precedence.

    APP_CONFIG: AppConfig = AppConfig()

    # define global variables with the Field class
    ENV_STATE: Optional[str] = Field(None, env="ENV_STATE")

    # environment specific variables do not need the Field class
    REDIS_HOST: Optional[str] = None
    REDIS_PORT: Optional[int] = None
    REDIS_PASS: Optional[str] = None

    class Config:
        """Loads the dotenv file."""

        env_file: str = ".env"

class DevConfig(GlobalConfig):
    """Development configurations."""

    class Config:
        env_prefix: str = "DEV_"

class ProdConfig(GlobalConfig):
    """Production configurations."""

    class Config:
        env_prefix: str = "PROD_"

class FactoryConfig:
    """Returns a config instance dependending on the ENV_STATE variable."""

    def __init__(self, env_state: Optional[str]):
        self.env_state = env_state

    def __call__(self):
        if self.env_state == "dev":
            return DevConfig()

        elif self.env_state == "prod":
            return ProdConfig()

cnf = FactoryConfig(GlobalConfig().ENV_STATE)()

The print statement of the last line in the above code block is to inspect the active configuration class. You’ll soon learn what I meant by the term active configuration. You can comment out the last line while using the code in production. Let’s explain what’s going on with each of the classes defined above.


The AppConfig class defines the config variables required for you API’s internal logic. In this case I’m not loading the variables from the .env file, rather defining them directly in the class. You can also define and import them from another file if necessary but they shouldn’t be placed in the .env file. For data validation to work, you have to inherit from Pydantic’s BaseModel and annotate the attributes using type hints while constructing the AppConfig class. Later, this class is called from the GlobalConfig class to build a nested data structure.


GlobalConfig defines the variables that propagates through other environment classes and the attributes of this class are globally accessible from all other environments. In this class, the variables are loaded from the .env file. In the .env file, global variables don’t have any environment specific prefixes like DEV_ or PROD_ before them. The class GlobalConfig inherits from Pydantic’s BaseSettings which helps to load and read the variables from the .env file. The .env file itself is loaded in the nested Config class. Although the environment variables are loaded from the .env file, Pydantic also loads your actual shell environment variables at the same time. From Pydantic’s [documentation]:

Even when using a dotenv file, Pydantic will still read environment variables as well as the dotenv file, environment variables will always take priority over values loaded from a dotenv file.

This means you can keep the sensitive variables in your .bashrc or zshrc and Pydantic will inject them during runtime. It’s a powerful feature, as it implies that you can easily keep the insensitive variables in your .env file and include that to the version control system. Meanwhile the sensitive information should be injected as a shell environment variable. For example, although I’ve defined an attribute called REDIS_PASS in the GlobalConfig class, there is no mention of any REDIS_PASS variable in the .env file. So normally, it returns None but you can easily inject a password into the REDIS_PASS variable from the shell. Assuming that you’ve set up your venv and installed the dependencies, you can test it by copying the contents of the above code snippet in file called and running the commands below:

export DEV_REDIS_PASS=ubuntu

This should printout:

>>> DevConfig(
...     ENV_STATE='dev',
...     APP_CONFIG=AppConfig(VAR_A=33, VAR_B=22.0),
...     REDIS_PASS='ubuntu',
...     REDIS_HOST='', REDIS_PORT=4000)

Notice how your injected REDIS_PASS has appeared in the printed config class instance. Although I injected DEV_REDIS_PASS into the environment variable, it appeared as REDIS_PASS inside the DevConfig instance. This is convenient because you won’t need to change the name of the variables in your codebase when you change the environment. To understand why it printed an instance of the DevConfig class, refer to the FactoryConfig section.


DevConfig class inherits from the GlobalConfig class and it can define additional variables specific to the development environment. It inherits all the variables defined in the GlobalConfig class. In this case, the DevConfig class doesn’t define any new variable.

The nested Config class inside DevConfig defines an attribute env_prefix and assigns DEV_ prefix to it. This helps Pydantic to read your prefixed variables like DEV_REDIS_HOST, DEV_REDIS_PORT etc without you having to explicitly mention them.


ProdConfig class also inherits from the GlobalConfig class and it can define additional variables specific to the production environment. It inherits all the variables defined in the GlobalConfig class. In this case, like DevConfig this class doesn’t define any new variable.

The nested Config class inside ProdConfig defines an attribute env_prefix and assigns PROD_ prefix to it. This helps Pydantic to read your prefixed variables like PROD_REDIS_HOST, PROD_REDIS_PORT etc without you having to explicitly mention them.


FactoryConfig is the controller class that dictates which config class should be activated based on the environment state defined as ENV_STATE in the .env file. If it finds ENV_STATE="dev" then the control flow statements in the FactoryConfig class will activate the development configs (DevConfig). Similarly, if ENV_STATE="prod" is found then the control flow will activate the production configs (ProdConfig). Since the current environment state is ENV_STATE="dev", when you run the code, it prints an instance of the activated DevConfig class. This way, you can assign different values to the same variable based on different environment contexts.

You can also dynamically change the environment by changing the value of ENV_STATE on your shell. Run:


This time the config instance should change and print the following:

>>> ProdConfig(
...     ENV_STATE='prod',
...     APP_CONFIG=AppConfig(VAR_A=33, VAR_B=22.0),
...     REDIS_PASS='ubuntu', REDIS_HOST='',
...     REDIS_PORT=5000)

Accessing the configs

Using the config variables is easy. Suppose you want use the variables in file called You can easily do so as shown in the following code block:


from configs import cnf

VAR_A = APP_CONFIG.VAR_A  # this is a nested config
REDIS_HOST = cnf.REDIS_HOST  # this is a top-level config


This should print out:

>>> ProdConfig(
...     ENV_STATE='prod',
...     APP_CONFIG=AppConfig(VAR_A=33, VAR_B=22.0),
...     REDIS_PASS='ubuntu',
...     REDIS_HOST='',
...     REDIS_PORT=5000)

VAR_A=33 VAR_B=22.0

Extending the pipeline

The modular design demonstrated above is easy to maintain and extend in my opinion. Previously, for simplicity, I’ve defined only two environment scopes; development and production. Let’s say you want to add the configs for your staging environment.

  • First you’ll need to add those staging variables to the .env file.


  • Then you’ve to create a class named StageConfig that inherits from the GlobalConfig class. The architecture of the class is similar to that of the DevConfig or ProdConfig class.

class StageConfig(GlobalConfig):
    """Staging configurations."""

    class Config:
        env_prefix: str = "STAGE_"

  • Finally, you’ll need to insert an ENV_STATE logic into the control flow of the FactoryConfig class. See how I’ve appended another if-else block to the previous (prod) block.

class FactoryConfig:
    """Returns a config instance depending on the ENV_STATE variable."""

    def __init__(self, env_state: Optional[str]):
        self.env_state = env_state

    def __call__(self):
        if self.env_state == "dev":
            return DevConfig()

        elif self.env_state == "prod":
            return ProdConfig()

        elif self.env_state == "stage"
            return StageConfig()

To see your new addition in action just change the ENV_STATE to “stage” in the .env file or export it to your shell environment.

export ENV_STATE="stage"

This will print out an instance of the class StageConfig.


The above workflow works perfectly for my usage scenario. So subjectively, I feel like it’s an elegant solution to a very icky problem. Your mileage will definitely vary.

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