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Validation

Dynaconf allows the validation of settings parameters, in some cases you may want to validate the settings before starting the program.

Lets say you have settings.toml

[default]
version = "1.0.0"
age = 35
name = "Bruno"
DEV_SERVERS = ['127.0.0.1', 'localhost', 'development.com']
PORT = 8001

[production]
PROJECT = "This is not hello_world"

Validating in Python programmatically

At any point of your program you can do:

from dynaconf import Dynaconf, Validator


settings = Dynaconf(
    validators=[
        # Ensure some parameters exists (are required)
        Validator('VERSION', 'AGE', 'NAME', must_exist=True),

        # Ensure some password cannot exist
        Validator('PASSWORD', must_exist=False),

        # Ensure some parameter mets a condition
        # conditions: (eq, ne, lt, gt, lte, gte, identity, is_type_of, is_in, is_not_in)
        Validator('AGE', lte=30, gte=10),

        # validate a value is eq in specific env
        Validator('PROJECT', eq='hello_world', env='production'),

        # Ensure some parameter (string) meets a condition
        # conditions: (len_eq, len_ne, len_min, len_max, cont)
        # Determines the minimum and maximum length for the value
        Validator("NAME", len_min=3, len_max=125),

        # Signifies the presence of the value in a set, text or word
        Validator("DEV_SERVERS", cont='localhost'),

        # Checks whether the length is the same as defined.
        Validator("PORT", len_eq=4),
    ]
)

The above will raise dynaconf.validators.ValidationError("AGE must be lte=30 but it is 35 in env DEVELOPMENT") and dynaconf.validators.ValidationError("PROJECT must be eq='hello_world' but it is 'This is not hello_world' in env PRODUCTION")

Providing default or computed values

Validators can be used to provide default or computed values.

Default values

Validator("FOO", default="A default value for foo")

Then if not able to load the values from files or environment this default value will be set for that key.

Computed values

Sometimes you need some values to be computed by calling functions, just pass a callable to the default argument.

Validator("FOO", default=my_function)

then

def my_function(settings, validator):
    return "this is computed during validation time"

If you want to be lazy evaluated

from dynaconf.utils.parse_conf import empty, Lazy

Validator("FOO", default=Lazy(empty, formatter=my_function))

You can also use dot-delimited paths for registering validators on nested structures:

# Register validators
settings.validators.register(

    # Ensure the database.host field exists.
    Validator('DATABASE.HOST', must_exist=True),

    # Make the database.password field optional. This is a default behavior.
    Validator('DATABASE.PASSWORD', must_exist=None),
)

# Fire the validator
settings.validators.validate()

Combining validators

Validators can be combined using:

| or operator.

Validator('DATABASE.USER', must_exist=True) | Validator('DATABASE.KEY', must_exist=True)

& and operator.

Validator('DATABASE.HOST', must_exist=True) & Validator('DATABASE.CONN', must_exist=True)

CLI and dynaconf_validators.toml

It is possible to define validators in TOML file called dynaconf_validators.toml placed in the same folder as your settings files.

dynaconf_validators.toml equivalent to program above

[default]

version = {must_exist=true}
name = {must_exist=true}
password = {must_exist=false}

# dot notation is also supported
'a_big_dict.nested_1.nested_2.nested_3.nested_4' = {must_exist=true, eq=1}

  [default.age]
  must_exist = true
  lte = 30
  gte = 10

[production]
project = {eq="hello_world"}

Then to fire the validation use:

$ dynaconf validate

This returns code 0 (success) if validation is ok.

!!! info All values in dynaconf are parsed using toml format, TOML tries to be smart and infer the type of the settings variables, some variables will be automatically converted to integer:

FOO = "0x..."  # hexadecimal
FOO = "0o..."  # Octal
FOO = "0b..."  # Binary

All cases are on toml specs https://github.com/toml-lang/toml/blob/master/toml.abnf

If you need to force a specific type casting there are 2 options.

  1. Use double quoted for strings ex: `FOO = "'0x...'" will be string.
  2. Specify the type using @ ex: FOO = "@str 0x..." (available converters are @int, @float, @bool, @json)

Selective Validation

New in 3.1.6

You can also choose what sections of the settings you do or don't want to validate. This is useful when:

  • You want to add additional validators after the settings object is created.
  • You want settings validated only when certain sections of your project are loaded.
  • You want to offer incremental configuration levels, validating only what is needed.

Selective validation can be performed both when creating a settings object and when calling validate on a settings object's validators. The new arguments accept either a string representing a settings path or a list of strings representing settings paths.

A settings path starts at the top level element and can be specified down to the lowest component. For example: my_settings.server.user.password can have the following settings paths passed in server, server.user, server.user.password.

Note: Selective validation matches the passed in value(s) to settings paths that start with that value. This means that passing exclude="FOO" will exclude not only paths that start with FOO but also FOOBAR.

Examples:

-- config.py --

...
# create a settings object, validating only settings under settings.server
settings = Dynaconf(
    validators=[
        Validator(
            "server.hostname",
            "server.port",
            "server.auth",
            must_exist=True
        ),
        Validator(
            "module1.value1",
            "module1.value2",
            "module1.value3",
            must_exist=True
        ),
        Validator(
            "module2.value1",
            "module2.value2",
            "module2.bad",
            must_exist=True
        )
    ],
    validate_only="server"
)

-- module1.py --

...
# call validation on module1 settings
settings.validators.validate(only=["module1"])

-- module2.py --

...
# call validation on module2 settings
# ignore validation for a subsection of module2's settings
settings.validators.validate(
    only=["module2"],
    exclude=["module2.bad"]
)