Towards Effortless Python Configuration Files Version 1
Introduction
As noted in the previous article the simplistic version is rife with problems including scalability, maintainability and extensibility.
A simple extension from Version Ø is to try and hide the Python configuration details behind a property class. That is implement a pseudo data class that exposes a set of properties that allow a developer to simply do property set and get calls to retrieve and persist property values.
From the maintainers point of view this implementation should support the following capabilities.
- Allow auto creation of configuration sections if they are missing
- Allow auto creation of property values if they are missing
- The properties should be implemented as both read-through and write-through.
- In order to avoid the startup costs for the above as an application instantiates this class throughout the application this class should be a Singleton.
Class Representation
The following UML Class diagram describes a class that would meet the requirements in the introduction. The ConfiguratonProperties class meets requirements 1 & 2 with the protected methods .createMissingSections and .createMissingKeys
Create Implementations
Create Missing Sections
The following code shows the implementation. Notice that additional sections require code updates to this method
SECTION_GENERAL: str = 'General' SECTION_DATABASE: str = 'Database' def _createMissingSections(self): """ Create missing sections. Add additional calls for each defined section """ self._createMissingSection(SECTION_GENERAL) self._createMissingSection(SECTION_DATABASE)
The missing section code is as follows.
def _createMissingSection(self, sectionName: str): """ Only gets created if it is missing Args: sectionName: The potential section to create """ hasSection: bool = self._configParser.has_section(sectionName) self.logger.info(f'hasSection: {hasSection} - {sectionName}') if hasSection is False: self._configParser.add_section(sectionName)
Create Missing Keys
The following code shows the implementation. Again note if we add an additional section the developer must add an additional loop for the new section.
GENERAL_PREFERENCES: Dict[str, str] = { 'debug': 'False', 'logLevel': 'Info' } DATABASE_PREFERENCES: Dict[str, str] = { 'dbName': 'example_db', 'dbHost': 'localhost', 'dbPort': '5432' } def _createMissingKeys(self): """ Create missing keys and their values. Add additional calls for each defined section. """ for keyName, keyValue in GENERAL_PREFERENCES.items(): self._createMissingKey(sectionName=SECTION_GENERAL, keyName=keyName, defaultValue=keyValue) for keyName, keyValue in DATABASE_PREFERENCES.items(): self._createMissingKey(sectionName=SECTION_DATABASE, keyName=keyName, defaultValue=keyValue)
The missing key code is as follows. Notice any missing keys are immediately persisted.
def _createMissingKey(self, sectionName: str, keyName: str, defaultValue: str): """ Only gets created if it is missing. The configuration file is updated immediately for each missing key and its value Args: sectionName: The section name where the key resides keyName: The key name defaultValue: Itsß value """ if self._configParser.has_option(sectionName, keyName) is False: self._configParser.set(sectionName, keyName, defaultValue) self._saveConfiguration()
Class Properties
Sample implementations for requirement 3 follow.
String Properties
Notice that setting a property writes-through to the configuration file by setting the property and immediately persisting it. Reading properties are effectively read-through because of how we immediately write set properties.
@property def dbName(self) -> str: return self._configParser.get(SECTION_DATABASE, 'dbName') @dbName.setter def dbName(self, newValue: str): self._configParser.set(SECTION_DATABASE, 'dbName', newValue) self._saveConfiguration()
Integer Properties
Integer properties use the .getint method to retrieve the value. When setting the property the developer must manually convert it to a string.
@property def dbPort(self) -> int: return self._configParser.getint(SECTION_DATABASE, 'dbPort') @dbPort.setter def dbPort(self, newValue: int): self._configParser.set(SECTION_DATABASE, 'dbPort', str(newValue)) self._saveConfiguration()
Boolean Properties
Boolean properties use the .getboolean method to retrieve their value. When setting the property the developer must manually convert it to a string.
SECTION_GENERAL: str = 'General' SECTION_DATABASE: str = 'Database' def _createMissingSections(self): """ Create missing sections. Add additional calls for each defined section """ self._createMissingSection(SECTION_GENERAL) self._createMissingSection(SECTION_DATABASE)
Enumeration Properties
I will not cover enumeration properties in this article. There are two ways to persist them, by their name or by their value. Each mechanism requires a slightly different way to deserialize the values back to an enumeration type.
Accessing and Modifying Properties
The following code snippet demonstrates how to access and modify the properties.
def _createMissingSection(self, sectionName: str): """ Only gets created if it is missing Args: sectionName: The potential section to create """ hasSection: bool = self._configParser.has_section(sectionName) self.logger.info(f'hasSection: {hasSection} - {sectionName}') if hasSection is False: self._configParser.add_section(sectionName)
The above snippet produces the following output
GENERAL_PREFERENCES: Dict[str, str] = { 'debug': 'False', 'logLevel': 'Info' } DATABASE_PREFERENCES: Dict[str, str] = { 'dbName': 'example_db', 'dbHost': 'localhost', 'dbPort': '5432' } def _createMissingKeys(self): """ Create missing keys and their values. Add additional calls for each defined section. """ for keyName, keyValue in GENERAL_PREFERENCES.items(): self._createMissingKey(sectionName=SECTION_GENERAL, keyName=keyName, defaultValue=keyValue) for keyName, keyValue in DATABASE_PREFERENCES.items(): self._createMissingKey(sectionName=SECTION_DATABASE, keyName=keyName, defaultValue=keyValue)
Conclusion
The source code for this article is here. The support class SingletonV3 is here
The result of the implementation initially left me satisfied as a consumer of the code. I was able to get and set typed properties. However, as the maintainer of the code I had to manually update code data structures and code loops whenever I added new sections and new properties. Additionally, all I really got from this is a mechanism/pattern to use whenever I needed new configuration properties in different applications.
Advantages
- Easy type safe access to application properties
- Invoking the singleton in different parts of my application provided consistent and reliable access to properties regardless which part of the application modified values
Disadvantages
- Updates to add new properties was tedious
- Lots of boiler plate code
- No reusability across various applications. Essentially, I just had a template
See my next post that documents an alternate implementation to address the disadvantages I listed, while keeping the advantages.
The above is the detailed content of Towards Effortless Python Configuration Files Version 1. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics











Python is suitable for data science, web development and automation tasks, while C is suitable for system programming, game development and embedded systems. Python is known for its simplicity and powerful ecosystem, while C is known for its high performance and underlying control capabilities.

You can learn basic programming concepts and skills of Python within 2 hours. 1. Learn variables and data types, 2. Master control flow (conditional statements and loops), 3. Understand the definition and use of functions, 4. Quickly get started with Python programming through simple examples and code snippets.

Python excels in gaming and GUI development. 1) Game development uses Pygame, providing drawing, audio and other functions, which are suitable for creating 2D games. 2) GUI development can choose Tkinter or PyQt. Tkinter is simple and easy to use, PyQt has rich functions and is suitable for professional development.

You can learn the basics of Python within two hours. 1. Learn variables and data types, 2. Master control structures such as if statements and loops, 3. Understand the definition and use of functions. These will help you start writing simple Python programs.

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

To maximize the efficiency of learning Python in a limited time, you can use Python's datetime, time, and schedule modules. 1. The datetime module is used to record and plan learning time. 2. The time module helps to set study and rest time. 3. The schedule module automatically arranges weekly learning tasks.

Python is widely used in the fields of web development, data science, machine learning, automation and scripting. 1) In web development, Django and Flask frameworks simplify the development process. 2) In the fields of data science and machine learning, NumPy, Pandas, Scikit-learn and TensorFlow libraries provide strong support. 3) In terms of automation and scripting, Python is suitable for tasks such as automated testing and system management.

Python excels in automation, scripting, and task management. 1) Automation: File backup is realized through standard libraries such as os and shutil. 2) Script writing: Use the psutil library to monitor system resources. 3) Task management: Use the schedule library to schedule tasks. Python's ease of use and rich library support makes it the preferred tool in these areas.
