# Tooling and Ecosystem Standards for Readability
This document outlines the coding standards specifically related to tooling and the ecosystem for Readability projects. It aims to guide developers in leveraging the available tools and libraries effectively to ensure maintainable, performant, and secure Readability applications. These standards consider the latest version of Readability and promote modern development practices.
## 1. Development Environment Setup
### 1.1 Integrated Development Environment (IDE)
**Do This:** Use a modern IDE like VS Code, IntelliJ IDEA, or Sublime Text with appropriate Readability plugins.
**Don't Do This:** Rely on basic text editors or outdated IDEs without Readability support.
**Why:** Modern IDEs provide syntax highlighting, code completion, linting, and debugging capabilities, significantly improving developer productivity and code quality.
**Example (VS Code with Readability Extension):**
1. Install VS Code.
2. Install the official Readability extension from the VS Code Marketplace.
3. Configure workspace settings for Readability-specific linting and formatting.
"""json
// .vscode/settings.json
{
"readability.linter.enable": true,
"readability.formatter.enable": true,
"editor.formatOnSave": true
}
"""
### 1.2 Dependency Management
**Do This:** Use a robust dependency management tool like Pip with "requirements.txt" or Poetry for managing project dependencies. For more complex Readability deployments, consider using Docker and Docker Compose.
**Don't Do This:** Manually manage dependencies or rely on system-wide installed packages.
**Why:** Dependency management ensures consistent and reproducible builds, simplifying collaboration and deployment.
**Example (Poetry):**
"""bash
poetry init # create project with poetry
poetry add readability-core # add the main readability library
poetry add readability-components # add a related component
poetry install # install the dependencies
poetry lock # Generate poetry.lock to ensure consistent deployments
"""
**Example (requirements.txt):**
"""
readability-core==1.2.3
readability-components==0.5.0
requests>=2.28
"""
"""bash
pip install -r requirements.txt
"""
### 1.3 Version Control
**Do This:** Use Git for version control and a collaborative platform like GitHub, GitLab, or Bitbucket.
**Don't Do This:** Rely on local backups or shared network drives for code management.
**Why:** Version control provides a complete history of changes, facilitates collaboration, and enables easy rollback in case of errors.
**Best Practices:**
* Follow a branching strategy (e.g., Gitflow)
* Write meaningful commit messages
* Use pull requests for code reviews
## 2. Code Analysis and Linting
### 2.1 Static Analysis
**Do This:** Use static analysis tools like "mypy" (if Readability components are typed) or similar tools to catch errors early in the development cycle.
**Don't Do This:** Ignore static analysis warnings or suppress them without understanding the underlying issue.
**Why:** Static analysis helps identify potential bugs, security vulnerabilities, and code style violations before runtime.
**Example (MyPy configuration if applicable):**
"""toml
# mypy.ini
[mypy]
python_version = 3.9
warn_unused_configs = True
ignore_missing_imports = True # if some readability components don't have types.
"""
### 2.2 Linting
**Do This:** Use a Readability-specific linter (if available within the ecosystem) or adapt existing linters (e.g., flake8 with Readability-specific rules) to enforce code style guidelines.
**Don't Do This:** Disable linting or ignore linting errors.
**Why:** Linting enforces consistent code style, improving readability and reducing potential errors.
**Example (flake8 setup, adapting for possible Readability needs):**
If standard flake8 rules are adapted for Readability:
"""bash
pip install flake8 flake8-comprehensions
flake8 --max-line-length=120 --select=E,W,F,C ."
"""
(Assuming "C" is a prefix for custom Readability style checks that can then be configured in flake8. Or, if a specific Readability linter plugin for flake8 is available, install that instead).
### 2.3 Code Formatting
**Do This:** Use an automated code formatter like "black" or "autopep8" to ensure consistent code formatting. Integrate code formatting into your IDE and pre-commit hooks.
**Don't Do This:** Rely on manual code formatting or inconsistent styling.
**Why:** Code formatting ensures a consistent and professional appearance, making the code easier to read and understand.
**Example ("black" integration):**
"""bash
pip install black
black .
"""
Add a pre-commit hook:
"""yaml
# .pre-commit-config.yaml
repos:
- repo: https://github.com/psf/black
rev: 24.2.0 # Use the latest version
hooks:
- id: black
"""
## 3. Testing Frameworks and Tools
### 3.1 Unit Testing
**Do This:** Use a comprehensive unit testing framework like "pytest" or "unittest" to thoroughly test individual Readability components.
**Don't Do This:** Skip unit tests or write insufficient tests that don't cover all edge cases.
**Why:** Unit tests ensure that individual components work as expected, reducing the risk of integration issues and bugs.
**Example ("pytest"):**
"""python
# test_readability_component.py
import pytest
from readability_core import analyze_text # Example - replace with your code
def test_analyze_text_basic():
text = "This is a sample sentence."
result = analyze_text(text)
assert isinstance(result, dict) # replace with more specific test
assert result["sentence_count"] == 1 # replace with more specific test
def test_analyze_text_empty():
text = ""
result = analyze_text(text)
assert result["sentence_count"] == 0 # replace with more specific test
"""
"""bash
pytest
"""
### 3.2 Integration Testing
**Do This:** Implement integration tests to verify the interaction between different Readability components and external systems.
**Don't Do This:** Neglect integration testing, assuming that individual components will work correctly together.
**Why:** Integration tests ensure that the different parts of the system work seamlessly together, catching potential integration issues.
**Example (Integration test - demonstrating connection to a data source):**
(Assuming "readability-core" somehow needs to interact with an external data source.)
"""python
# test_readability_integration.py
import pytest
from readability_core import analyze_text
import requests
def test_analyze_text_with_external_data():
# Mock external API
response = requests.get("https://example.com/data") # This should be replaced with your real external API
# Check external API's status code first
assert response.status_code == 200
text = response.text # Get example text
result = analyze_text(text)
assert isinstance(result, dict)
# add more specific assertion base on what analyze_text should do.
"""
### 3.3 Performance Testing
**Do This:** Use performance testing tools like "locust" or "k6" to evaluate the performance of Readability applications under different loads.
**Don't Do This:** Ignore performance testing or assume that the application will scale automatically
**Why:** Performance testing identifies bottlenecks and ensures that the application can handle the expected traffic volume.
**Example (basic "locustfile.py" - customize for Readability's specific APIs/functions):**
"""python
from locust import HttpUser, task, between
class ReadabilityUser(HttpUser):
wait_time = between(1, 2)
@task
def analyze_text(self):
self.client.post("/api/analyze", json={"text": "This is a sample text."}) # Customize to your Readability API
"""
## 4. Documentation Tools
### 4.1 API Documentation
**Do This:** Use tools like Sphinx and MkDocs, potentially with extensions for Readability, to generate clear and comprehensive API documentation from code comments and docstrings.
**Don't Do This:** Rely on outdated or incomplete documentation or force developers to dig through code to understand the API.
**Why:** API documentation facilitates the use of the Readability library and reduces the learning curve for new developers.
**Example (Sphinx setup):**
1. Install Sphinx: "pip install sphinx sphinx-rtd-theme"
2. Generate Sphinx configuration: "sphinx-quickstart"
3. Configure "conf.py" for Readability modules and potentially add autodoc extensions:
"""python
# conf.py
import os
import sys
sys.path.insert(0, os.path.abspath('.'))
project = 'Readability'
copyright = '2024, Your Name'
extensions = [
'sphinx.ext.autodoc',
'sphinx.ext.napoleon',
'sphinx_rtd_theme'
]
html_theme = 'sphinx_rtd_theme'
"""
4. Add directives to rst files, such as index.rst. For example:
"""rst
.. automodule:: readability_core
:members:
"""
5. Build the documentation: "make html"
### 4.2 User Guides and Tutorials
**Do This:** Create detailed user guides, tutorials, and examples showcasing how to use Readability features effectively.
**Don't Do This:** Neglect user documentation, assuming that users will be able to figure out the library on their own.
**Why:** User guides and tutorials help users understand and adopt the Readability library quickly and effectively.
## 5. Containerization and Deployment
### 5.1 Docker
**Do This:** Use Docker to containerize Readability applications, ensuring consistent execution across different environments.
**Don't Do This:** Deploy applications directly to servers without containerization.
**Why:** Docker provides a portable and reproducible environment, simplifying deployment and reducing compatibility issues.
**Example (Dockerfile):**
"""dockerfile
FROM python:3.9-slim-buster
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "main.py"]
"""
### 5.2 Docker Compose
**Do This:** Use Docker Compose to define and manage multi-container Readability applications.
**Don't Do This:** Manually manage multiple Docker containers.
**Why:** Docker Compose simplifies the management of complex applications with multiple interacting containers.
**Example (docker-compose.yml):**
"""yaml
version: "3.9"
services:
readability_app:
build: .
ports:
- "8000:8000"
depends_on:
- database
database:
image: postgres:13
environment:
POSTGRES_USER: user
POSTGRES_PASSWORD: password
POSTGRES_DB: readability_db
"""
### 5.3 CI/CD Pipelines
**Do This:** Implement CI/CD pipelines using tools like Jenkins, GitLab CI, or GitHub Actions to automate the build, test, and deployment process.
**Don't Do This:** Manually deploy applications or rely on ad-hoc deployment scripts.
**Why:** CI/CD pipelines automate the software delivery process, reducing errors and improving efficiency.
**Example (GitHub Actions):**
"""yaml
# .github/workflows/main.yml
name: CI/CD Pipeline
on:
push:
branches: [ main ]
pull_request:
branches: [ main ]
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python 3.9
uses: actions/setup-python@v3
with:
python-version: 3.9
- name: Install dependencies
run: |
python -m pip install --upgrade pip
pip install -r requirements.txt
- name: Lint with flake8
run: |
flake8 . --max-line-length=120 --select=E,W,F,C
- name: Test with pytest
run: |
pytest
deploy:
needs: build
runs-on: ubuntu-latest
steps:
- name: Deploy to production
run: echo "Deploying..." # Replace with your deployment steps
"""
## 6. Monitoring and Logging
### 6.1 Logging Frameworks
**Do This:** Use a logging framework like "logging" to capture detailed application logs.
**Don't Do This:** Rely on "print" statements for debugging or ignore potential errors.
**Why:** Logging provides valuable insights into application behavior, facilitating debugging and performance monitoring. Use structured logging formats where possible for easier aggregation and analysis within monitoring systems.
**Example (logging):**
"""python
import logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def analyze_text(text):
logger.info(f"Analyzing text: {text}")
try:
# Analysis logic here...
result = {"sentence_count": len(text.split('.'))} # Example Result
logger.info(f"Analysis Result : {result}")
return result
except Exception as e:
logger.error(f"Error during analysis: {e}", exc_info=True)
raise
"""
### 6.2 Monitoring Tools
**Do This:** Integrate Readability applications with monitoring tools like Prometheus, Grafana, or DataDog to track performance metrics and detect anomalies.
**Don't Do This:** Neglect application monitoring or assume that the application will always run smoothly.
**Why:** Monitoring provides real-time visibility into application health, enabling proactive issue resolution.
**Example (Prometheus and Grafana):** This assumes your Readability app exposes metrics via an HTTP endpoint in Prometheus format.
1. Install and configure Prometheus to scrape the application's metrics endpoint.
2. Install and configure Grafana to visualize the metrics from Prometheus.
3. Create dashboards in Grafana to monitor key performance indicators (KPIs) for Readability components.
## 7. Security Best Practices
### 7.1 Input Validation
**Do This:** Thoroughly validate all external inputs to prevent injection attacks and other security vulnerabilities (especially if your Readability app exposes APIs to external users/systems).
**Don't Do This:** Trust user input without validation or rely on client-side validation alone.
**Why:** Input validation ensures that the application is protected against malicious inputs.
**Example:**
"""python
def analyze_text(text):
if not isinstance(text, str):
raise ValueError("Input must be a string")
if len(text) > 10000: # Limit string length
raise ValueError("Input string too long")
# Continue with analysis...
"""
### 7.2 Dependency Security
**Do This:** Regularly scan dependencies for known vulnerabilities using tools like "safety" (for Python). Keep dependencies up to date.
**Don't Do This:** Use outdated dependencies with known security vulnerabilities.
**Why:** Dependency scanning helps identify and mitigate potential security risks.
**Example ("safety"):**
"""bash
pip install safety
safety check
"""
### 7.3 Secure Configuration
**Do This:** Store sensitive configuration data (e.g., API keys, database passwords) securely using environment variables or dedicated secrets management tools.
**Don't Do This:** Hardcode sensitive data directly in the code or store it in plain text configuration files.
**Why:** Secure configuration protects sensitive data from unauthorized access
This document provides a comprehensive set of tooling and ecosystem standards for Readability development. By adhering to these standards, developers can ensure that Readability applications are maintainable, performant, secure, and easy to use. Remember to consult the official Readability documentation and community resources for the latest best practices and updates.
danielsogl
Created Mar 6, 2025
This guide explains how to effectively use .clinerules with Cline, the AI-powered coding assistant.
The .clinerules file is a powerful configuration file that helps Cline understand your project's requirements, coding standards, and constraints. When placed in your project's root directory, it automatically guides Cline's behavior and ensures consistency across your codebase.
Place the .clinerules file in your project's root directory. Cline automatically detects and follows these rules for all files within the project.
# Project Overview project: name: 'Your Project Name' description: 'Brief project description' stack: - technology: 'Framework/Language' version: 'X.Y.Z' - technology: 'Database' version: 'X.Y.Z'
# Code Standards standards: style: - 'Use consistent indentation (2 spaces)' - 'Follow language-specific naming conventions' documentation: - 'Include JSDoc comments for all functions' - 'Maintain up-to-date README files' testing: - 'Write unit tests for all new features' - 'Maintain minimum 80% code coverage'
# Security Guidelines security: authentication: - 'Implement proper token validation' - 'Use environment variables for secrets' dataProtection: - 'Sanitize all user inputs' - 'Implement proper error handling'
Be Specific
Maintain Organization
Regular Updates
# Common Patterns Example patterns: components: - pattern: 'Use functional components by default' - pattern: 'Implement error boundaries for component trees' stateManagement: - pattern: 'Use React Query for server state' - pattern: 'Implement proper loading states'
Commit the Rules
.clinerules in version controlTeam Collaboration
Rules Not Being Applied
Conflicting Rules
Performance Considerations
# Basic .clinerules Example project: name: 'Web Application' type: 'Next.js Frontend' standards: - 'Use TypeScript for all new code' - 'Follow React best practices' - 'Implement proper error handling' testing: unit: - 'Jest for unit tests' - 'React Testing Library for components' e2e: - 'Cypress for end-to-end testing' documentation: required: - 'README.md in each major directory' - 'JSDoc comments for public APIs' - 'Changelog updates for all changes'
# Advanced .clinerules Example project: name: 'Enterprise Application' compliance: - 'GDPR requirements' - 'WCAG 2.1 AA accessibility' architecture: patterns: - 'Clean Architecture principles' - 'Domain-Driven Design concepts' security: requirements: - 'OAuth 2.0 authentication' - 'Rate limiting on all APIs' - 'Input validation with Zod'
--- alwaysApply: true description: Avoid 'else'; prefer guard clauses and polymorphism to keep a linear flow --- ## Do not use the `else` keyword Avoid `else` to reduce branching and nesting. This keeps a linear top-to-bottom reading flow and simplifies methods. ### Preferred alternatives - **Guard clauses (early return/exit)**: handle exceptional cases up front and return immediately. - **Polymorphism**: for complex conditional logic based on type/state, use Strategy/State instead of chained conditionals. ### Example (guard clause) ❌ Bad: ```ts function processPayment(payment: Payment): boolean { if (payment.isVerified()) { console.log("Processing payment..."); return true; } else { console.log("Payment not verified."); return false; } } ``` ✅ Good: ```ts function processPayment(payment: Payment): boolean { // Guard clause if (!payment.isVerified()) { console.log("Payment not verified."); return false; } // Happy path console.log("Processing payment..."); return true; } ```
# API Integration Standards for Readability This document outlines the coding standards for integrating Readability with backend services and external APIs. Adhering to these standards will ensure maintainable, performant, and secure code. ## 1. Architectural Patterns for API Integration ### 1.1 Separation of Concerns (SoC) **Standard:** Isolate API integration logic from the core Readability components. **Do This:** * Create dedicated modules or services responsible for API communication. * Use interfaces or abstract classes to define the interaction contracts between Readability and the API integration layer. **Don't Do This:** * Embed API calls directly within UI components or core business logic. * Mix API logic with data transformation or presentation logic. **Why:** SoC enhances maintainability, testability, and reusability. Changes to the API or Readability core will have minimal impact on other parts of the application. **Example:** """python # api_service.py import requests import json from typing import Dict, Any class ApiService: def __init__(self, base_url: str): self.base_url = base_url def get_data(self, endpoint: str, params: Dict[str, Any] = None) -> Dict[str, Any]: """ Fetches data from the API endpoint. """ url = f"{self.base_url}/{endpoint}" try: response = requests.get(url, params=params) response
# Core Architecture Standards for Readability This document outlines the core architecture standards for Readability projects. It provides guidelines for structuring and organizing code to ensure maintainability, scalability, and performance. These standards are designed to work with automated coding assistants such as GitHub Copilot and Cursor. ## 1. Fundamental Architectural Pattern: Modular Monolith ### Standard * **Do This:** Embrace a modular monolith architecture. The Readability application should be structured as a single deployable unit, logically divided into independent modules. * **Don't Do This:** Avoid strict microservices from the start unless demonstrably required by scalability needs. Avoid a tightly coupled monolithic application. ### Explanation A modular monolith offers a balanced approach, providing the benefits of a single codebase (easier deployment, simplified testing) while maintaining logical separation of concerns (improved maintainability, independent development). This pattern allows for future migration to microservices if needed, without premature complexity. ### Code Example (Conceptual) """python # readability/ # ├── core/ # Shared kernel and utilities # ├── article_extraction/ # Module: Article extraction logic # ├── summarization/ # Module: Summarization algorithms # ├── user_management/ # Module: User account management # ├── api/ # API layer exposing functionalities # └── main.py # Entry point, configuration, setup """ ### Anti-Patterns * **Spaghetti Code:** Code that's highly interconnected and difficult to understand. * **God Class:** A single class that handles too many responsibilities. * **Premature Optimization:** Optimizing code before identifying actual performance bottlenecks. ## 2. Project Structure and Organization ### Standard * **Do This:** Organize the project into meaningful modules or packages based on domain or functionality. Enforce clear boundaries between modules through well-defined interfaces/APIs. * **Don't Do This:** Avoid circular dependencies between modules. Avoid scattering related functionality across the project. ### Explanation A well-organized project structure improves code navigation, reduces complexity, and makes it easier for developers to understand and contribute. Clear module boundaries prevent accidental coupling. ### Code Example (Python - Package Structure) """python # readability/ # ├── core/ # │ ├── __init__.py # │ ├── utils.py # General utility functions # │ ├── models.py # Base data models # ├── article_extraction/ # │ ├── __init__.py # │ ├── extractor.py # Core extraction logic # │ ├── parsers.py # HTML parsing implementations # │ ├── schemas.py # Data validation and serialization # │ └── ... # ├── summarization/ # │ ├── __init__.py # │ ├── summarizer.py # Main summarization class # │ ├── algorithms/ # Different summarization algorithms # │ │ ├── lsa.py # │ │ └── ... # │ └── ... # └── ... """ ### Anti-Patterns * **Flat Structure:** Placing all files in a single directory. * **Feature Envy:** A module excessively accesses data or methods of another module. ## 3. Dependency Injection and Inversion of Control ### Standard * **Do This:** Use dependency injection (DI) to manage dependencies between modules. Prefer constructor injection where possible. Utilize an Inversion of Control (IoC) container for complex dependency graphs, if appropriate (e.g. "Flask-DI" for Flask projects). * **Don't Do This:** Avoid hardcoding dependencies within modules. Avoid using global state to share dependencies. ### Explanation DI promotes loose coupling, making modules more testable and reusable. It allows for easy swapping of implementations without modifying the dependent modules. IoC containers manage complex object graphs efficiently. ### Code Example (Python - Constructor Injection) """python # article_extraction/extractor.py class ArticleExtractor: def __init__(self, html_parser): # Dependency injected via constructor self.html_parser = html_parser def extract_content(self, url): html = self.html_parser.fetch_html(url) # ... extract content from HTML using the injected parser return content # core/utils.py class RequestsHTMLParser: def fetch_html(self, url): import requests response = requests.get(url) return response.text # main.py (Example using Flask-DI) from flask import Flask from flask_di import Di from article_extraction.extractor import ArticleExtractor from core.utils import RequestsHTMLParser app = Flask(__name__) di = Di(app) def configure(binder): binder.bind(ArticleExtractor, to=ArticleExtractor(RequestsHTMLParser())) # Binding with app.app_context(): extractor: ArticleExtractor = di.get(ArticleExtractor) #Resolving Dependency """ ### Anti-Patterns * **Service Locator:** Modules explicitly ask a service locator for dependencies, which reduces testability. * **Singleton Abuse:** Using singletons to provide dependencies, which makes it difficult to mock dependencies in tests. ## 4. API Design and Communication ### Standard * **Do This:** Design clear, well-defined APIs for module communication. Use appropriate data serialization formats (e.g., JSON, Protocol Buffers) and API styles (e.g., REST, GraphQL). * **Don't Do This:** Expose internal data structures through APIs. Avoid overly chatty APIs. ### Explanation Well-designed APIs ensure loose coupling between modules and allow for independent evolution. Using appropriate data formats and API styles improves interoperability and performance. ### Code Example (Python - REST API with Flask) """python # api/routes.py from flask import Flask, request, jsonify from article_extraction.extractor import ArticleExtractor from core.utils import RequestsHTMLParser app = Flask(__name__) extractor = ArticleExtractor(RequestsHTMLParser()) @app.route('/extract', methods=['POST']) def extract_article(): url = request.json.get('url') if not url: return jsonify({'error': 'URL is required'}), 400 try: content = extractor.extract_content(url) return jsonify({'content': content}), 200 except Exception as e: return jsonify({'error': str(e)}), 500 if __name__ == '__main__': app.run(debug=True) """ ### Anti-Patterns * **Leaky Abstraction:** The API exposes implementation details. * **Remote Procedure Call (RPC) Abuse:** Using RPC-style APIs for everything, leading to tight coupling. ## 5. Data Management and Persistence ### Standard * **Do This:** Abstract data access logic using a Repository pattern. Use an ORM (e.g., SQLAlchemy) or ODM (e.g., MongoEngine) for database interaction. Define clear data models and schemas. * **Don't Do This:** Directly embed SQL queries within application logic. Expose database implementation details to other modules. ### Explanation The Repository pattern separates data access logic from the business logic, making the application more maintainable and testable. ORMs and ODMs simplify database interactions and provide object-oriented data access. Clear data models ensure data consistency and integrity. ### Code Example (Python - Repository Pattern with SQLAlchemy) """python # core/models.py from sqlalchemy import Column, Integer, String from sqlalchemy.ext.declarative import declarative_base from sqlalchemy.orm import sessionmaker Base = declarative_base() class Article(Base): __tablename__ = 'articles' id = Column(Integer, primary_key=True) url = Column(String) content = Column(String) def __repr__(self): return f"<Article(url='{self.url}', content='{self.content[:50]}...')>" # data/article_repository.py using repository pattern from sqlalchemy import create_engine from sqlalchemy.orm import sessionmaker from core.models import Article, Base class ArticleRepository: def __init__(self, db_url='sqlite:///:memory:'): self.engine = create_engine(db_url) Base.metadata.create_all(self.engine) self.Session = sessionmaker(bind=self.engine) def get_article(self, url): with self.Session() as session: return session.query(Article).filter_by(url=url).first() def add_article(self, url, content): with self.Session() as session: article = Article(url=url, content=content) session.add(article) session.commit() def delete_article(self, url): with self.Session() as session: article = session.query(Article).filter_by(url=url).first() if article: session.delete(article) session.commit() """ ### Anti-Patterns * **Active Record:** Data models directly handle persistence logic, leading to tight coupling. * **Database as API:** Exposing the database directly to client applications. ## 6. Error Handling and Logging ### Standard * **Do This:** Implement comprehensive error handling using exceptions. Log errors and warnings using a logging framework (e.g., "logging" in Python). Use structured logging (e.g. JSON formatted logs). Implement centralized exception handling. * **Don't Do This:** Ignore exceptions. Print error messages to the console without logging. Expose sensitive information in error messages. ### Explanation Robust error handling and logging are crucial for debugging, monitoring, and maintaining the application. Structured logging facilitates analysis and troubleshooting. ### Code Example (Python - Error Handling and Logging) """python import logging import json # Configure logging logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') def extract_article_info(url): """ Extracts information from a given URL and logs any errors encountered. """ try: # Simulate fetching the HTML content (replace with actual extraction logic) # For example: html_content = fetch_html(url) logging.info(json.dumps({'event': 'article_extraction_started', 'url': url})) # Simulate an error condition raise ValueError("Simulated error during article extraction") article_content = "Dummy article content" logging.info(json.dumps({'event':'article_extraction_success', 'url': url})) # Log extraction success return article_content except ValueError as ve: logging.error(json.dumps({'event': 'article_extraction_failed', 'url': url, 'error': str(ve)}), exc_info=True) return None # Or raise the exception depending on the use case except Exception as e: # Catch-all for any other unexpected errors logging.exception(json.dumps({'event': 'unexpected_error_during_extraction', 'url': url, 'error': str(e)})) #Using logging.exception to capture traceback return None """ ### Anti-Patterns * **Catch-All Exception Handling:** Catching all exceptions without handling them properly. * **Silent Failure:** The application continues to run without reporting an error. ## 7. Concurrency and Parallelism ### Standard * **Do This:** Use appropriate concurrency models (e.g., threads, asyncio) for I/O-bound and CPU-bound tasks. Use thread pools or process pools to manage concurrency. Handle race conditions and deadlocks carefully. * **Don't Do This:** Use global state in concurrent code without proper synchronization. Create too many threads or processes, leading to resource exhaustion. ### Explanation Concurrency and parallelism can significantly improve application performance, but they also introduce complexities. Choosing the right concurrency model and handling synchronization issues are crucial. ### Code Example (Python - Asyncio for Concurrent Web Requests) """python import asyncio import aiohttp async def fetch_url(session, url): try: async with session.get(url
# Component Design Standards for Readability This document outlines the component design standards for Readability, focusing on creating reusable, maintainable, and performant components within the Readability framework. These standards are designed to promote consistency across the codebase and ensure that all components adhere to best practices. ## 1. Component Architecture Principles ### 1.1. Single Responsibility Principle (SRP) * **Standard:** Each component should have one, and only one, reason to change. A component should encapsulate a specific functionality or logic. * **Why:** This principle ensures that components are modular and easier to understand, test, and maintain. It reduces the risk of unintended side effects when modifications are made. * **Do This:** Break down complex functionalities into smaller, independent components. Ensure that each component is responsible for a well-defined task. * **Don't Do This:** Create monolithic components that handle multiple unrelated tasks. Avoid components with high coupling and low cohesion. **Example:** """python # Good: Separate components for data fetching and UI rendering class DataFetcher: def fetch_data(self, url): # Fetch data from URL pass class DataRenderer: def render_data(self, data): # Render data in the UI pass # Bad: Single component handling both data fetching and rendering class DataComponent: def fetch_and_render(self, url): # Fetch data from URL # Render data in the UI pass """ ### 1.2. Open/Closed Principle (OCP) * **Standard:** Components should be open for extension but closed for modification. You should be able to add new functionality without altering the source code of existing components. * **Why:** This principle promotes stability and reduces the risk of introducing bugs when adding new features. It encourages the use of abstraction and polymorphism. * **Do This:** Use interfaces, abstract classes, or higher-order functions to allow for extension without modification. * **Don't Do This:** Directly modify existing component code to add new features. Avoid tightly coupled designs that require changes across multiple components. **Example:** """python # Good: Using interface for extension from abc import ABC, abstractmethod class ReportGenerator(ABC): @abstractmethod def generate_report(self, data): pass class PDFReportGenerator(ReportGenerator): def generate_report(self, data): # Generate PDF report pass class CSVReportGenerator(ReportGenerator): def generate_report(self, data): # Generate CSV report pass # Bad: Modifying existing class to add new report format class ReportGenerator: def generate_report(self, data, format): if format == "pdf": # Generate PDF report pass elif format == "csv": # Generate CSV report pass """ ### 1.3. Liskov Substitution Principle (LSP) * **Standard:** Subtypes must be substitutable for their base types without altering the correctness of the program. * **Why:** Ensures that inheritance maintains the expected behavior of base classes. This is especially important for maintaining the integrity of polymorphic code. * **Do This:** Ensure that derived classes fulfill the contract of their base classes. Avoid implementing methods that throw exceptions in derived classes if the base class does not specify that behavior. * **Don't Do This:** Create derived classes that violate the behavior of their base classes. Avoid forcing users to write type-checking code to handle different subtypes. **Example:** """python # Good: Subtype maintains base type behavior class Rectangle: def __init__(self, width, height): self.width = width self.height = height def set_width(self, width): self.width = width def set_height(self, height): self.height = height class Square(Rectangle): def __init__(self, size): super().__init__(size, size) def set_width(self, width): self.width = width self.height = width def set_height(self, height): self.width = height self.height = height # Bad: Subtype violates base type behavior class Dog: def bark(self): print("Woof!") class RobotDog(Dog): def bark(self): raise NotImplementedError("Robot dogs cannot bark.") """ ### 1.4. Interface Segregation Principle (ISP) * **Standard:** Clients should not be forced to depend on methods they do not use. * **Why:** Reduce unnecessary dependencies and promote loose coupling. Makes components easier to change and reuse. * **Do This:** Break down large interfaces into smaller, more specific interfaces tailored to the needs of different clients. * **Don't Do This:** Create large "fat" interfaces that force clients to implement methods they don't need. **Example:** """python # Good: Segregated interfaces from abc import ABC, abstractmethod class Document(ABC): @abstractmethod def open(self): pass @abstractmethod def close(self): pass class Printable(ABC): @abstractmethod def print_document(self): pass class Scanner(ABC): @abstractmethod def scan_document(self): pass class SimpleDocument(Document): def open(self): pass def close(self): pass class MultiFunctionPrinter(Document, Printable, Scanner): def open(self): pass def close(self): pass def print_document(self): pass def scan_document(self): pass # Bad: Single, monolithic interface class Machine(ABC): @abstractmethod def print_document(self, document): pass @abstractmethod def fax_document(self, document): pass @abstractmethod def scan_document(self, document): pass class OldFashionMachine(Machine): # must implement unrelated actions def print_document(self, document): pass # This method is not relevant but must be implemented def fax_document(self, document): pass def scan_document(self, document): pass """ ### 1.5. Dependency Inversion Principle (DIP) * **Standard:** High-level modules should not depend on low-level modules. Both should depend on abstractions. Abstractions should not depend on details. Details should depend on abstractions. * **Why:** Reduce coupling between components. Allows for easier testing, maintenance, and flexibility in changing implementations. * **Do This:** Use interfaces or abstract classes to define dependencies between components. Inject dependencies into components rather than creating them internally. * **Don't Do This:** Allow high-level modules to directly depend on low-level modules. Avoid hardcoding dependencies within components. **Example:** """python # Good: Using dependency injection through an interface from abc import ABC, abstractmethod class Switchable(ABC): @abstractmethod def turn_on(self): pass @abstractmethod def turn_off(self): pass class LightBulb(Switchable): def turn_on(self): print("LightBulb: ON") def turn_off(self): print("LightBulb: OFF") class ElectricPowerSwitch: def __init__(self, client: Switchable): self.client = client self.on = False def press(self): if self.on: self.client.turn_off() self.on = False else: self.client.turn_on() self.on = True # Bad: High-level module depends directly on low-level module. class LightBulb: def turn_on(self): print("LightBulb: ON") def turn_off(self): print("LightBulb: OFF") class ElectricPowerSwitch: def __init__(self, bulb: LightBulb): self.bulb = bulb self.on = False def press(self): if self.on: self.bulb.turn_off() self.on = False else: self.bulb.turn_on() self.on = True """ ## 2. Component Structure and Organization ### 2.1. Directory Structure * **Standard:** Organize components into logical directories based on their functionality or domain. Group related components together. * **Why:** Improves code discoverability and maintainability. Makes it easier to navigate and understand the codebase. * **Do This:** Create descriptive directory names that reflect the purpose of the components within them. Use a consistent directory structure across the project. * **Don't Do This:** Place all components in a single directory. Use ambiguous or non-descriptive directory names. **Example:** (Illustrative directory structure for a hypothetical Readability plugin) """ readability_plugin/ ├── core/ # Core components related to the central functionality │ ├── __init__.py │ ├── text_analyzer.py # Analyze text complexity │ ├── readability_scorer.py # Calculate the score. │ └── ... ├── ui/ # User interface components │ ├── __init__.py │ ├── settings_panel.py │ ├── display_panel.py │ └── ... ├── data/ # Data storage and processing │ ├── __init__.py │ ├── model.py # Data models of information │ ├── data_persistence.py # Load/Save function │ └── ... ├── tests/ # Automated tests │ ├── __init__.py │ ├── test_text_analyzer.py │ └── ... ├── README.md └── setup.py """ ### 2.2. Component Naming * **Standard:** Use descriptive and consistent names for components. Follow a naming convention that reflects the component's purpose and type. * **Why:** Improves code readability and maintainability. Makes it easier to understand the role of each component. * **Standard (Python):** Use CamelCase for Classes. Use snake_case for function names. **Example:** """python # Good class TextAnalyzer: # Class name descriptive and in CamelCase def calculate_flesch_reading_ease(self, text): # snake_case for function # Bad class TA: def calc(self, input): ... """ ### 2.3. Component Size * **Standard:** Keep components small and focused. Avoid creating large, complex components that are difficult to understand and maintain. * **Why:** Improves code readability and maintainability. Makes it easier to test and reuse components. * **Do This:** Break down large components into smaller, more manageable pieces. Limit the number of lines of code in a single component. * **Don't Do This:** Create monolithic components with hundreds or thousands of lines of code. ## 3. Component Implementation Details ### 3.1. Immutability * **Standard:** Favor immutable data structures and components. If a component's state is fixed after creation, it is easier to reason about and test. * **Why:** Simplifies debugging and testing. Reduces the risk of unexpected side effects. Improves concurrency. * **Do This:** Use immutable data structures (e.g., tuples, frozensets). Create components that do not modify their internal state after initialization. **Example:** """python # Good: Using immutable tuple def calculate_stats(data): # data should be kept immutable average = sum(data) / len(data) minimum = min(data) maximum = max(data) return (average, minimum, maximum) # Returning a tuple # Bad: Mutating data directly def update_data(data): data[0] = 100 # Mutating the input """ ### 3.2. Error Handling * **Standard:** Implement robust error handling within components. Catch exceptions and handle them gracefully. Provide meaningful error messages. * **Why:** Prevents unexpected crashes and provides helpful information for debugging. Improves the robustness of the application. * **Do This:** Use try-except blocks to catch exceptions. Raise custom exceptions with descriptive error messages. **Example:** """python def read_file(filename): try: with open(filename, 'r') as f: content = f.read() return content except FileNotFoundError: raise ValueError(f"File not found: {filename}") except IOError: raise IOError(f"Could not read file: {filename}") """ ### 3.3. Logging * **Standard:** Use logging to record important events and errors within components. Provide sufficient context to diagnose issues. Make sure log configuration is abstracted via central configuration file * **Why:** Aids in debugging and monitoring the application. Provides valuable insights into component behavior. * **Do This:** Use a logging framework to record events. Include relevant information such as timestamps, component names, and error messages. "logging.basicConfig(filename='example.log', encoding='utf-8', level=logging.DEBUG)" should only be called from a central configuration point. **Example:** """python import logging # Create a logger logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) # Create handlers stream_handler = logging.StreamHandler() # Output to console formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') stream_handler.setFormatter(formatter) logger.addHandler(stream_handler) def process_data(data): try: result = 10 / len(data) logger.debug(f"Processed data: {result}") return result except ZeroDivisionError: logger.error("Cannot divide by zero", exc_info=True) # Include exception info return None """ ### 3.4. Testing * **Standard:** Write unit tests for all components. Ensure that each component is thoroughly tested in isolation. Apply Test Driven Design (TDD) where tests are defined upfront * **Why:** Validates the correctness of components. Prevents regressions and ensures that components continue to function as expected after modifications. * **Do This:** Use a unit testing framework. Write tests for all possible scenarios, including edge cases and error conditions. **Example:** """python # example_component.py def add(x, y): return x + y # test_example_component.py import unittest from example_component import add class TestExampleComponent(unittest.TestCase): def test_add_positive_numbers(self): self.assertEqual(add(2, 3), 5) def test_add_negative_numbers(self): self.assertEqual(add(-2, -3), -5) def test_add_mixed_numbers(self): self.assertEqual(add(2, -3), -1) """ ### 3.5. Documentation * **Standard:** Document all components with clear and concise descriptions. Explain the purpose, inputs, outputs, and any dependencies of each component. * **Why:** Improves code understanding and maintainability. Makes it easier for other developers to use and contribute to the codebase. * **Do This:** Use docstrings to document components. Provide examples of how to use components. * **Standard (Python):** Follow PEP 257 for docstring conventions. **Example:** """python def calculate_reading_time(text, words_per_minute=200): """ Calculates the estimated reading time for a given text. Args: text (str): The text to analyze. words_per_minute (int): The average reading speed in words per minute. Default is 200. Returns: float: The estimated reading time in minutes. Example: >>> calculate_reading_time("This is a short sentence.") 0.025 """ word_count = len(text.split()) return word_count / words_per_minute """ ### 3.6. Performance Optimization * **Standard:** Optimize components for performance. Minimize unnecessary computations and allocations. * **Why:** Improves the responsiveness and efficiency of the application. Reduces resource consumption. * **Do This:** Use efficient algorithms and data structures. Avoid unnecessary loops and function calls. Profile components to identify performance bottlenecks. * **Don't Do This:** Premature optimization. Optimize only when necessary and based on profiling results. **Example:** """python # Avoid string concatenation in loops def build_string(items): result = "" # Inefficient for item in items: result += str(item) return result def build_string(items): return "".join(map(str,items)) # Efficient """ ### 3.7 Security Considerations * **Standard:** Build components with security in mind. Sanitize inputs and outputs. Avoid vulnerabilities such as SQL injection and cross-site scripting (XSS). * **Why:** Protects the application and its users from malicious attacks. Ensures data integrity and confidentiality. * **Do This:** Use secure coding practices. Follow security guidelines and recommendations. Perform security reviews and penetration testing. * **Don't Do This:** Trust user inputs. Store sensitive data in plain text. **Example:** """python import html import bleach def sanitize_input(user_input): # Escape HTML entities to prevent XSS escaped_input = html.escape(user_input) # Use bleach to allow only specific tags and attributes allowed_tags = ['p', 'a', 'b', 'i', 'strong', 'em'] allowed_attributes = {'a': ['href', 'title']} sanitized_input = bleach.clean(escaped_input, tags=allowed_tags, attributes=allowed_attributes) return sanitized_input # Usage Example: user_supplied_html = "<script>alert('XSS Attack!')</script><p>This is some text with a <a href='http://example.com'>link</a></p>" safe_html = sanitize_input(user_supplied_html) print(safe_html) # Output: <p>This is some text with a <a href='http://example.com'>link</a></p> """ ### 3.8. Code Reviews * **Standard:** Always conduct thorough code reviews for all component changes. * **Why:** Catches errors and ensures standards adherence early in the development cycle. Promotes knowledge sharing among team members. * **Do This:** Use a code review tool. Provide constructive feedback. Focus on code quality, maintainability, and security. Document review discussions. ## 4. Readability-Specific Considerations ### 4.1. Linguistic Analysis Components * **Standard**: Separate the components for each type of analysis and its level needed for readability * **Why**: Readability needs to be scalable from the analysis. **Example**: """python # Good class SentenceAnalyzer: def get_words(sentence): pass def get_length(self, sentence): pass class TextAnalyzer: def get_sentences(text): pass def get_average_words(text): pass # Bad class Analyzer: def get_words_from_sentence(sentence): pass def get_sentences_from_text(text): pass def get_average_words_from_text(text): pass """ ### 4.2. Scoring Components * **Standard**: Clearly define scores and use cases. * **Why**: Readability needs to specify the range of score and what each score means **Example**: """python class FleschKincaid: def getScoreMeaning(score): if (score > 100): return "Easy to read. " # keep it simple, link to documentation for details if (score <= 100 and score >=60): return "Can be read by 6th graders" # keep it simple, link to documentation for details def calculate(text): # specific implementation pass """ ## 5. Modern Approaches and Patterns ### 5.1. Functional Components * **Standard:** Favor functional components where appropriate. This is especially relevant for data transformation or analysis logic where state management is minimal. * **Why:** Functional components are often easier to test, reason about, and compose. They can lead to more concise and maintainable code. * **Do This:** Use pure functions (no side effects), immutability, and higher-order functions. * **Don't Do This:** Force a functional approach when an object-oriented approach would be more natural. **Example:** """python # Functional component for calculating readability score (simplified) def calculate_fog_index(average_words_per_sentence, percentage_complex_words): return 0.4 * (average_words_per_sentence + percentage_complex_words) # Example usage: avg_words = 15.5 complex_words_percent = 10.0 fog_index = calculate_fog_index(avg_words, complex_words_percent) print(f"Fog Index: {fog_index}") """ ### 5.2. Hooks and Composition * **Standard:** Utilize modern composition techniques such as hooks (if applicable or available in the Readability context) to share logic between components. * **Why:** Promotes code reuse and avoids duplication. Leads to more flexible and maintainable components. * **Do This:** Identify common patterns in components and extract them into reusable hooks or functions. **Example:** Demonstrative hooks style: """python # Example hook-like function for reusability def use_fetch_data(url): """ Simulates a hook for fetching data from a URL. In React/similar frameworks, this would be a true hook. """ try: # Simulate fetching data (replace with actual API call) import time time.sleep(1) #Simulate network latency data = f"Data from {url}" # Placeholder return data, None # Return data and no error except Exception as e: return None, str(e) # Return no data and an error message def display_component(url): """Example display component using our hook-like function""" data, error = use_fetch_data(url) if error: return f"Error: {error}" else: return f"Displaying: {data}" # Usage result = display_component("https://example.com/readability_data") print(result) """ By adhering to these component design standards, developers can create a more maintainable, testable, and performant Readability codebase. These standards should be reviewed and updated regularly to reflect the latest best practices and advancements in the field.
# State Management Standards for Readability This document outlines the coding standards for state management in Readability applications. It provides guidelines for structuring state, handling data flow, and ensuring reactivity, while emphasizing maintainability, performance, and security. These standards are designed to be used by developers and AI coding assistants. ## Architecture and General Principles ### Standard 1: Embrace Unidirectional Data Flow * **Do This:** Implement a unidirectional data flow architecture. Changes to the state should originate from actions/events, flow through reducers/updaters, and propagate to the UI. * **Don't Do This:** Avoid directly mutating state values from UI components. This creates unpredictable behavior and makes debugging difficult. * **Why:** Unidirectional data flow ensures predictability and simplifies debugging. It provides a clear audit trail of state changes. """javascript // Example: Actions const READ_ARTICLE = 'READ_ARTICLE'; function readArticle(articleId) { return { type: READ_ARTICLE, payload: articleId }; } // Example: Reducer function articleReducer(state = {}, action) { switch (action.type) { case READ_ARTICLE: return { ...state, currentlyReading: action.payload }; default: return state; } } // Example: Component function ArticleList({ articles, readArticle }) { return ( <ul> {articles.map(article => ( <li key={article.id} onClick={() => readArticle(article.id)}> {article.title} </li> ))} </ul> ); } """ ### Standard 2: Centralize Application State * **Do This:** Use a centralized state container (e.g., using Readability's built-in state management or integrating with external libraries like Zustand or Jotai). * **Don't Do This:** Avoid scattering state across multiple components, especially using prop drilling for deeply nested components. * **Why:** Centralization makes it easier to track and manage state, reduce redundancy, and improve component decoupling. """javascript // Example: Zustand (using Readability compatible syntax) import create from 'zustand' const useStore = create(set => ({ articles: [], currentlyReading: null, fetchArticles: async () => { const response = await fetch('/api/articles'); // Example API endpoint const data = await response.json(); set({ articles: data }); }, setCurrentlyReading: (articleId) => set({ currentlyReading: articleId }) })) function ArticleList() { const { articles, fetchArticles, setCurrentlyReading } = useStore(); useEffect(() => { fetchArticles(); }, [fetchArticles]); return ( <ul> {articles.map(article => ( <li key={article.id} onClick={() => setCurrentlyReading(article.id)}> {article.title} </li> ))} </ul> ); } """ ### Standard 3: Favor Immutability * **Do This:** Treat state as immutable. Use methods that return new objects or arrays instead of modifying existing ones. * **Don't Do This:** Directly mutate state objects or arrays (e.g., using "push", "splice", or direct assignment). * **Why:** Immutability makes it easier to track changes, detect updates, and implement features like time-travel debugging. It is also crucial for performance optimization in Readability's rendering engine. """javascript // Example: Immutable update using spread operator function reducer(state, action) { switch (action.type) { case 'ADD_ARTICLE': return { ...state, articles: [...state.articles, action.payload] }; case 'UPDATE_ARTICLE': return { ...state, articles: state.articles.map(article => article.id === action.payload.id ? { ...article, ...action.payload } : article ) }; default: return state; } } // Example: Immutable update using libraries like Immer import { produce } from "immer" const articleReducer = (state, action) => { return produce(state, draft => { switch (action.type) { case "ADD_ARTICLE": draft.articles.push(action.payload) break case "REMOVE_ARTICLE": draft.articles = draft.articles.filter(article => article.id !== action.payload) break; default: return; //Important for Immer } }) } """ ### Standard 4: Selectors for Data Retrieval * **Do This:** Use selectors to derive data from the global state. Selectors should be pure functions. * **Don't Do This:** Access state directly in components or perform complex data transformations inline. * **Why:** Selectors help encapsulate the structure of the state, improve performance by memoizing derived data, and simplify component logic. """javascript // Example: Selector function const getPublishedArticles = (state) => { return state.articles.filter(article => article.isPublished); }; // Example: Using selector in a component function PublishedArticleList({ articles }) { const publishedArticles = useSelector(getPublishedArticles); // Assuming a useSelector hook return ( <ul> {publishedArticles.map(article => ( <li key={article.id}>{article.title}</li> ))} </ul> ); } """ ### Standard 5: Asynchronous Actions and Side Effects * **Do This:** Manage asynchronous actions (e.g., API calls) using middleware (e.g., Readability's effect handlers, or a library like Redux Thunk or Redux Saga). * **Don't Do This:** Perform asynchronous operations directly in components or reducers. * **Why:** Middleware keeps components and reducers pure and testable, and provides a structured way to handle side effects. """javascript // Example: Readability effect function fetchArticlesEffect(dispatch) { return async () => { try { const response = await fetch('/api/articles'); const data = await response.json(); dispatch({ type: 'FETCH_ARTICLES_SUCCESS', payload: data }); } catch (error) { dispatch({ type: 'FETCH_ARTICLES_ERROR', payload: error }); } }; } // Example: Dispatching the effect function ArticleList({ dispatch, isLoading, error, articles }) { useEffect(() => { dispatch(fetchArticlesEffect); // Assuming dispatch comes from context }, [dispatch]); // ... rendering logic } """ ## Technology-Specific Standards within Readability Readability, depending on the chosen ecosystem (e.g., tight integration with a custom framework or leveraging established JavaScript libraries), might have distinct approaches to state management. The following considerations are crucial : ### Readability Built-in State Management * **Leverage Readability's Context API effectively:** For simple state management needs, Readability's Context API offers a straightforward solution. When using Context, ensure that value updates trigger re-renders only when necessary. """javascript // Example: Using Context API import React, { createContext, useState, useContext } from 'react'; const ArticleContext = createContext(); export function ArticleProvider({ children }) { const [articles, setArticles] = useState([]); const fetchArticles = async () => { const response = await fetch('/api/articles'); const data = await response.json(); setArticles(data); }; const value = { articles, fetchArticles, }; return ( <ArticleContext.Provider value={value}> {children} </ArticleContext.Provider> ); } export function useArticles() { return useContext(ArticleContext); } // Usage in a component: function ArticleList() { const { articles, fetchArticles } = useArticles(); useEffect(() => { fetchArticles(); }, [fetchArticles]); return ( <ul> {articles.map(article => ( <li key={article.id}>{article.title}</li> ))} </ul> ); } """ * **Reducer Hooks for Complex State:** For component-specific state logic involving multiple sub-values with interdependencies or complex update logic, favor "useReducer" over multiple "useState" calls. """javascript // Example: useReducer Hook import React, { useReducer } from 'react'; const initialState = { articles: [], loading: false, error: null, }; function reducer(state, action) { switch (action.type) { case 'FETCH_ARTICLES_START': return { ...state, loading: true, error: null }; case 'FETCH_ARTICLES_SUCCESS': return { ...state, loading: false, articles: action.payload }; case 'FETCH_ARTICLES_ERROR': return { ...state, loading: false, error: action.payload }; default: return state; } } function ArticleList() { const [state, dispatch] = useReducer(reducer, initialState); useEffect(() => { const fetchArticles = async () => { dispatch({ type: 'FETCH_ARTICLES_START' }); try { const response = await fetch('/api/articles'); const data = await response.json(); dispatch({ type: 'FETCH_ARTICLES_SUCCESS', payload: data }); } catch (error) { dispatch({ type: 'FETCH_ARTICLES_ERROR', payload: error }); } }; fetchArticles(); }, []); // ... rendering with state.articles, state.loading, state.error } """ ### Using External State Management Libraries * **Choose the Right Library:** Select a state management library that aligns with the application's complexity and team's expertise. Options include Zustand, Jotai, Redux. * **Zustand and Jotai:** These are often preferable for simpler applications due to their ease of use and smaller bundle size compared to Redux. """javascript //Example with Jotai (using Readability friendly syntax) import { atom, useAtom } from 'jotai' const articlesAtom = atom([]) const ArticleList = () => { const [articles, setArticles] = useAtom(articlesAtom) useEffect(() => { const fetchArticles = async () => { const response = await fetch('/api/articles'); const data = await response.json(); setArticles(data); }; fetchArticles(); }, [setArticles]); return ( <ul> {articles.map(article => ( <li key={article.id}>{article.title}</li> ))} </ul> ); } """ * **Redux (If Needed):** For large, complex applications, Redux might be necessary. Use Redux Toolkit to streamline Redux development """javascript //Example using Redux Toolkit import { configureStore, createSlice } from '@reduxjs/toolkit'; import { useDispatch, useSelector } from 'react-redux'; const articlesSlice = createSlice({ name: 'articles', initialState: { articles: [], loading: false, error: null, }, reducers: { fetchArticlesStart: (state) => { state.loading = true; state.error = null; }, fetchArticlesSuccess: (state, action) => { state.loading = false; state.articles = action.payload; }, fetchArticlesError: (state, action) => { state.loading = false; state.error = action.payload; }, }, }); export const { fetchArticlesStart, fetchArticlesSuccess, fetchArticlesError } = articlesSlice.actions; export const store = configureStore({ reducer: { articles: articlesSlice.reducer, }, }); //Async Thunk Example export const fetchArticles = () => async (dispatch) => { dispatch(fetchArticlesStart()); try { const response = await fetch('/api/articles'); const data = await response.json(); dispatch(fetchArticlesSuccess(data)); } catch (error) { dispatch(fetchArticlesError(error.message)); } }; function ArticleList() { const dispatch = useDispatch(); const { articles, loading, error } = useSelector((state) => state.articles); useEffect(() => { dispatch(fetchArticles()); }, [dispatch]); // ... rendering logic using articles, loading, error } """ * **Middleware for Side Effects (Redux):** When using Redux, leverage middleware like "redux-thunk" or "redux-saga" to handle asynchronous operations and side effects cleanly. * **Selectors (Redux):** Always use selectors to access data from the Redux store. This allows components to remain decoupled from the store's precise state structure. ### Standard 6: Optimistic Updates * **Do This:** Implement optimistic updates to provide a more responsive user experience. Assume the operation will succeed and update the UI immediately, reverting the update only if an error occurs. * **Don't Do This:** Wait for the server response before updating the UI, leading to perceived lag. * **Why:** Optimistic updates make the application feel faster and more interactive. """javascript // Example: Optimistic update with Zustand import create from 'zustand' const useArticleStore = create((set, get) => ({ articles: [], addArticle: async (newArticle) => { // Optimistically update set(state => ({ articles: [...state.articles, { ...newArticle, tempId: Date.now(), isOptimistic: true }] })); try { const response = await fetch('/api/articles', { method: 'POST', headers: { 'Content-Type': 'application/json' }, body: JSON.stringify(newArticle), }); if (response.ok) { const savedArticle = await response.json(); // Replace temp article with the real one set(state => ({ articles: state.articles.map(article => article.tempId === newArticle.tempId ? savedArticle : article ) })); } else { // Revert on error set(state => ({ articles: state.articles.filter(article => article.tempId !== newArticle.tempId) })); } } catch (error) { // Revert on error set(state => ({ articles: state.articles.filter(article => article.tempId !== newArticle.tempId) })); } }, })) function ArticleForm() { const addArticle = useArticleStore(state => state.addArticle); const handleSubmit = (event) => { event.preventDefault(); const title = event.target.title.value; const content = event.target.content.value; addArticle({ title, content }); }; return ( <form onSubmit={handleSubmit}> <input type="text" name="title" placeholder="Title" /> <textarea name="content" placeholder="Content"></textarea> <button type="submit">Add Article</button> </form> ); } """ ### Standard 7: Local Storage and Persistence * **Do This:** Use local storage or other persistence mechanisms sparingly and carefully, primarily for user preferences or caching data. * **Don't Do This:** Store sensitive information in local storage without proper encryption. Avoid excessively large data storage, as it can impact performance. * **Why:** Local storage can improve the user experience by preserving state across sessions. However, it introduces security risks if not handled properly and can degrade performance if overused. """javascript // Example: Storing user preference in local storage function useThemePreference() { const [theme, setTheme] = useState(() => { return localStorage.getItem('theme') || 'light'; }); useEffect(() => { localStorage.setItem('theme', theme); }, [theme]); return [theme, setTheme]; } function ThemeSwitcher() { const [theme, setTheme] = useThemePreference(); const toggleTheme = () => { setTheme(theme === 'light' ? 'dark' : 'light'); }; return ( <button onClick={toggleTheme}> Switch to {theme === 'light' ? 'Dark' : 'Light'} Theme </button> ); } """ ### Standard 8: Server-Side State Management * **Do This:** When using Readability in server-side rendered or statically generated applications, consider carefully which state needs to be persisted and how it should be managed (e.g., using techniques like rehydration). * **Don't Do This:** Assume client-side state management strategies translate directly to server-side environments. * **Why:** Proper server-side state management impacts initial render performance, SEO, and overall application architecture. """javascript // Example: Rehydrating Zustand state from server (Next.js example) import { useStore } from './store'; // Assuming Zustand store import { useSnapshot } from 'valtio'; //Valtio for simple state sharing function MyApp({ Component, pageProps }) { if (pageProps.initialZustandState) { useStore.setState(pageProps.initialZustandState); } return <Component {...pageProps} />; } export default MyApp; export async function getStaticProps() { // Fetch data from external API const res = await fetch('https://.../articles') const data = await res.json() return { props: { initialZustandState: useStore.getState(), // Pass the store snapshot to the page articles: data }, revalidate: 10, } } //Example Component function ArticleList({articles}) { const store = useSnapshot(useStore); return ( <ul> {store.articles.map(article => ( <li key={article.id}>{article.title}</li> ))} </ul> ); } """ ## Common Anti-Patterns * **Prop Drilling:** Passing props through multiple layers of components that don't need them. Use context or a centralized state management solution instead. * **Global Mutable State:** Relying on global variables or objects directly modified throughout the application. This makes it difficult to track state changes and causes unpredictable behavior. * **Over-reliance on "useState":** Using multiple "useState" hooks in a component with complex state logic. This can lead to verbose and difficult-to-manage code. Consider using "useReducer" or a custom hook instead. * **Ignoring Memoization:** Failing to memoize derived data or component outputs, leading to unnecessary re-renders and performance issues. Utilize "useMemo", "useCallback", and "React.memo" effectively. * **Direct DOM Manipulation:** Directly manipulating the DOM outside of component lifecycles or effect hooks. This bypasses Readability's rendering engine and causes inconsistencies. By adhering to these state management standards, developers can build robust, maintainable, and performant Readability applications. These guidelines promote predictability, simplify debugging, and ensure a consistent and scalable architecture. They also facilitate collaboration and code reuse within development teams.