A sophisticated, production-ready web scraping framework demonstrating advanced Python architecture patterns, comprehensive testing practices, and enterprise-level software engineering.
Portfolio Project: This repository showcases professional software development skills including clean architecture, test-driven development, CI/CD implementation, and modern Python best practices.
- Clean Architecture: Plugin-based system with SOLID principles
- Production Ready: Comprehensive error handling, logging, and monitoring
- Test-Driven Development: 26+ automated tests with CI/CD integration
- Type Safety: Full mypy compliance with strict type checking
- Modern Python: Async/await, Pydantic v2, dependency injection patterns
- DevOps Integration: GitHub Actions, automated quality checks, multi-environment testing
This project demonstrates enterprise-level software design patterns:
- Abstract Factory Pattern: Dynamic parser creation and registration
- Strategy Pattern: Runtime parser selection based on URL analysis
- Template Method Pattern: Extensible base parser with customizable hooks
- Dependency Injection: Registry-based component management
- Observer Pattern: Event-driven logging and monitoring
- AI/ML Framework: LangChain with prompt engineering and structured output parsing
- Database: Apache Cassandra for distributed data storage and deduplication
- Browser Automation: Crawlee + Playwright for sophisticated queue management
- Data Validation: Pydantic v2 with advanced type checking and serialization
- Content Extraction: Multi-method approach (Newspaper3k + Trafilatura + custom)
- Async Architecture: Modern Python async/await patterns throughout
- Structured Logging: Loguru with contextual error tracking
- Testing Framework: Pytest with comprehensive coverage strategies
- Containerization: Multi-stage Docker builds with production optimization
- CI/CD Pipeline: GitHub Actions with automated testing, security scanning, and deployment
- Plugin Architecture: Extensible parser system following Open/Closed Principle
- Dependency Injection: Registry-based component management
- Strategy Pattern: Dynamic parser selection based on URL analysis
- Abstract Base Classes: Template method pattern for consistent behavior
- Separation of Concerns: Clear boundaries between parsing, validation, and output
- Type Safety: Full mypy compliance with strict typing
- Async Programming: Efficient concurrent processing with proper error handling
- Modern Features: Context managers, decorators, dataclasses, and type hints
- Data Validation: Runtime type checking with Pydantic schemas
- Error Handling: Comprehensive exception management with graceful degradation
- Content Analysis: AI-powered article summarization and sentiment analysis
- Topic Classification: Automated topic extraction and entity recognition
- Quality Assessment: Intelligent content quality scoring and readability analysis
- Prompt Engineering: Sophisticated LangChain prompt templates and output parsing
- Fallback Systems: Graceful degradation when AI services are unavailable
- Mock Integration: Development-friendly mock LLM for testing and demonstration
- Batch Processing: Enterprise-grade job orchestration similar to AWS Step Functions + Batch
- Auto-scaling: Dynamic resource allocation with horizontal and vertical scaling
- Job Management: Sophisticated job lifecycle management with automatic retries
- Monitoring: Built-in metrics, structured logging, and health checks
- Security: Pod security standards, RBAC, and minimal privilege execution
- Reliability: Failure recovery, resource cleanup, and graceful degradation
- Cassandra Integration: High-performance, scalable NoSQL database for web scraping data
- Data Deduplication: Intelligent URL and content duplicate detection and prevention
- Dynamic Seed Management: Database-driven crawler seed URL management and prioritization
- Time-Series Analytics: Crawl statistics, performance metrics, and historical data tracking
- Content Versioning: Track article changes over time with automated change detection
- Horizontal Scaling: Distributed architecture supporting multi-node deployments
- Test-Driven Development: 26+ automated tests covering multiple scenarios
- Integration Testing: End-to-end workflow validation
- Continuous Integration: GitHub Actions with multi-Python version testing
- Code Quality: Automated linting, formatting, and security scanning
- Performance Optimization: uv integration for 10-100x faster dependency installation
- Documentation: Comprehensive inline documentation and usage examples
- CI/CD Pipeline: Automated testing, quality checks, and deployment
- Containerization: Multi-stage Docker builds with security hardening
- Environment Management: Pixi + uv for fast, reliable dependency management
- Logging & Monitoring: Structured logging with contextual error tracking
- Configuration Management: Flexible configuration with environment support
- Security: Dependency scanning and vulnerability assessment
- Container Registry: Automated builds with GitHub Container Registry
- Multi-platform Support: ARM64 and AMD64 container builds
- AI-Enhanced Content Analysis: LangChain-powered summarization, sentiment analysis, and topic classification
- Distributed Database Storage: Cassandra integration with deduplication and analytics
- Multi-Parser Architecture: Automatic parser selection based on URL fingerprinting
- Advanced Kubernetes Orchestration: Enterprise-grade batch processing with auto-scaling
- Production Pipelines: Complete CI/CD with automated testing, building, and deployment
- Container Orchestration: Kubernetes-ready with sophisticated job management
- Type-Safe Data Models: Pydantic v2 schemas with comprehensive validation
- Async Content Extraction: High-performance processing with Playwright automation
- Enterprise Monitoring: Structured logging with contextual error tracking
# Automatic parser discovery and registration
@dataclass
class BaseParser:
"""Abstract base implementing Template Method pattern"""
async def can_parse(self, url: str) -> bool:
"""Strategy pattern for runtime parser selection"""
async def parse(self, page: Page, context: dict) -> BaseModel:
"""Core extraction logic with type safety"""# LangChain integration for intelligent content analysis
from langchain_core.prompts import PromptTemplate
from langchain_community.llms import FakeListLLM
class AdvancedContentAnalyzer:
def __init__(self):
self.summary_prompt = PromptTemplate(
input_variables=["title", "content"],
template="Analyze this article and provide a concise summary..."
)
async def analyze_article(self, article: NewsArticle) -> ContentAnalysis:
"""AI-powered analysis with sentiment, topics, and quality scoring"""
analysis = await self.llm.ainvoke({
"title": article.title,
"content": article.content
})
return ContentAnalysis(
summary=analysis.summary,
sentiment=analysis.sentiment,
topics=analysis.topics,
quality_score=self.calculate_quality_score(article)
)# Pydantic v2 schema with advanced validation
class NewsArticle(BaseModel):
title: str = Field(..., min_length=1, max_length=500)
content: str = Field(..., min_length=10)
url: HttpUrl
published_date: Optional[datetime] = None
author: Optional[str] = Field(None, max_length=200)
ai_analysis: Optional[Dict[str, Any]] = None # AI insights
@field_validator('content')
@classmethod
def validate_content_quality(cls, v: str) -> str:
# Custom business logic validation
return v.strip()Advanced batch processing system using pure Kubernetes primitives:
┌─────────────────────────────────────────────────────────────────┐
│ Kubernetes Orchestration │
├─────────────────────────────────────────────────────────────────┤
│ ┌─────────────────┐ ┌──────────────────┐ ┌─────────────┐ │
│ │ Batch │ │ CronJob │ │ Manual │ │
│ │ Orchestrator │───▶│ Schedulers │ │ Jobs │ │
│ └─────────────────┘ └──────────────────┘ └─────────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ Kubernetes Job Execution Layer │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────┐ │ │
│ │ │ Scraper │ │ Scraper │ │ Scraper │ │ ... │ │ │
│ │ │ Job Pod 1 │ │ Job Pod 2 │ │ Job Pod 3 │ │ │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────┘ │ │
│ └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
- Batch Processing: URL batching with configurable chunk sizes
- Job Lifecycle Management: Automatic creation, monitoring, and cleanup
- Failure Recovery: Intelligent retry logic with exponential backoff
- Resource Management: Dynamic scaling and resource optimization
- Security: Pod security standards and RBAC implementation
- Monitoring: Structured logging, metrics, and health checks
# Deploy complete orchestration system
make deploy
# Monitor batch processing
make status
make logs
# Scale operations
make scale-up
make job-batchsrc/
├── main.py # Application entry point
├── orchestrator.py # Kubernetes batch orchestrator
├── routes.py # Crawlee request routing logic
├── core/
│ ├── base_parser.py # Abstract parser base class
│ ├── logger.py # Structured logging configuration
│ ├── parser_registry.py # Dynamic parser discovery
│ ├── parser_manager.py # Parser selection strategy
│ ├── proxy_config.py # Proxy management system
│ └── seeds.py # Input processing pipeline
├── parsers/
│ ├── generic_news.py # Universal news extraction
│ └── weibo.py # Social media specialized parser
└── schemas/
└── news.py # Type-safe data models
deployment/kubernetes/
├── batch-orchestrator.yaml # Orchestrator deployment
├── job-template.yaml # Batch job templates
├── cronjobs.yaml # Scheduled operations
├── orchestrator-config.yaml # RBAC and configuration
└── deploy.sh # Automated deployment
Comprehensive test coverage demonstrating TDD practices:
- 26+ Automated Tests: Unit, integration, and end-to-end coverage
- Multi-Python Support: Testing matrix across Python 3.9-3.12
- Mock Strategies: Isolated testing with Playwright simulation
- CI Integration: Automated testing on every commit
# Example: Parser validation testing
@pytest.mark.asyncio
async def test_parser_discovery():
"""Validates dynamic parser registration"""
registry = ParserRegistry()
parsers = await registry.discover_parsers()
assert len(parsers) > 0
@pytest.mark.asyncio
async def test_type_safety():
"""Ensures Pydantic schema compliance"""
result = await parser.parse(mock_page, context)
assert isinstance(result, NewsArticle)
assert result.model_validate(result.model_dump())- Multi-stage Docker builds optimized for production
- Security hardening with non-root user execution
- Multi-platform support (ARM64/AMD64) via GitHub Actions
- Kubernetes manifests for cloud-native deployment
# Automated workflow demonstrating enterprise practices
name: CI/CD Pipeline
on: [push, pull_request]
jobs:
test:
strategy:
matrix:
python-version: [3.9, 3.10, 3.11, 3.12]
build:
runs-on: ubuntu-latest
steps:
- name: Build multi-platform image
- name: Security vulnerability scan
- name: Publish to GitHub Container Registry- Kubernetes deployment with configurable replicas
- Resource management with requests/limits
- Environment configuration via ConfigMaps
- Service exposure with load balancing
- Async Processing: Concurrent request handling with Playwright
- Queue Management: Crawlee-based request deduplication
- Structured Logging: JSON-formatted logs with correlation IDs
- Error Recovery: Graceful failure handling with retry logic
- Resource Optimization: Memory-efficient content extraction
Python • Apache Cassandra • Distributed Systems • LangChain • AI/ML Engineering • Async/Await • Pydantic • Playwright • Docker • Kubernetes • GitHub Actions • Test-Driven Development • Clean Architecture • Design Patterns • Type Safety • CI/CD • Container Orchestration • Web Scraping • Parser Registry • Strategy Pattern • Prompt Engineering • Content Analysis • Database Engineering • Data Deduplication • Time-Series Analytics