I. Background
In actual production projects, different environments (e.g., development, testing, and production environments) often have different configuration requirements, such as database links. We expect a code without change, only through a single configuration variable adjustment can be adapted and used in multiple environments, to achieve "a code, multiple deployments" needs, in order to enhance the flexibility of the system deployment and configuration management capabilities. Specifically, the configuration management framework (class library) that supports "multi-environment configuration" should support the following features:
(1) Must support loading different configurations for different environments, and multiple environments can load common configurations.
(2) Complex data structures such as lists, dictionaries, objects, etc. must be supported.
(3) Must support field data type conversion, e.g., directly output the value True for a Boolean type field instead of the string 'True'.
(4) Variable references must be supported, e.g. DOMAIN = "",ADMIN_EMAIL = "admin@{DOMAIN}".
(5) It is desirable to support dynamic modification of configuration at runtime, and variable values can be updated by modifying system environment variables.
(6) It is better to support mainstream configuration file formats, such as yml, json, toml, etc., especially the common toml format in Python.
II. Technical selection
The most common configuration management frameworks (libraries) in the Python technology stack are ConfigParser, pydantic, dynaconf, dotenv, and configobj. Of these, only ConfigParser is built into the Python standard library. The following is a comparison of the features, advantages, and disadvantages of each library, which will lead to the final selection.
1. Functional comparison
| Functions/Libraries | dynaconf | pydantic | dotenv | configobj | ConfigParser |
|---|---|---|---|---|---|
| Different environments load different configurations, and multiple environments can load common configurations. | be in favor of | Partial support | Partial support | Partial support | Partial support |
| Support for complex data structures such as lists, dictionaries, objects, etc. | be in favor of | be in favor of | unsupported | be in favor of | unsupported |
| Support for field data type conversion | be in favor of | be in favor of | unsupported | Partial support | Partial support |
| Support for variable references | be in favor of | unsupported | be in favor of | be in favor of | Partial support |
| Dynamically modifying configuration configurations at runtime | be in favor of | be in favor of | be in favor of | be in favor of | be in favor of |
| Support mainstream configuration file formats, such as yml, json, toml, etc. | be in favor of | be in favor of | unsupported | unsupported | unsupported |
Partial support Refers to the need for additional extensions or secondary development.
2. Summary of advantages and disadvantages
| storehouse | vantage | drawbacks |
|---|---|---|
| dynaconf | Support a variety of configuration file formats (YAML, JSON, TOML, etc.); support for complex data structures; support for environment switching and general configuration; powerful and flexible. | Additional installation required; relatively steep learning curve. |
| pydantic | Powerful data validation and type conversion; supports complex data structures; integrates well with frameworks such as FastAPI. | Requires additional installation; does not directly support multiple profile formats, but can be used in conjunction with other libraries. |
| dotenv | Easy to use, good for loading environment variables; good compatibility with other libraries. | Limited functionality, only supported.env files; complex data structures and multiple profile formats are not supported. |
| configobj | Supports nested structures and basic data type conversions; easy to use and suitable for processing.ini format configuration file. |
Multiple profile formats are not supported (only the.ini); partial support for advanced configuration management features such as environment switching and generic configuration. |
| ConfigParser | Built into the Python standard library, no additional installation required; easy to use, good for dealing with basic.ini Configuration file. |
Does not support complex data structures; does not support multiple configuration file formats; more limited functionality, does not support advanced configuration management features. |
Hopefully this tweaked form will better meet your needs.
III. Final selection
After comparing the above features and summarizing the advantages and disadvantages, dotenv and ConfigParser are the first to be ruled out because they do not support complex data structures; configobj does not support a variety of configuration file formats, and only partially supports general configuration and data conversion, which is also unsuitable; among the remaining two options, pydantic is rarely used for configuration management, does not support variable references, and only partially supports the loading of generic configurations. Among the remaining two options, pydantic is rarely used for configuration management, does not support variable references, and only partially supports loading generic configurations. dynaconf is the best choice because of its comprehensive features and professional configuration management library.