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Recommended Web Scraping Frameworks

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Background, Rationale, and Standard Usage

This document explains why the platform recommends using browser-based scraping frameworks in modern Web data collection scenarios, and outlines the officially recommended standard usage architecture.

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1. Background

With the rapid evolution of Web technologies, most modern target websites (such as TikTok, Instagram, major e-commerce platforms, and content communities) now exhibit the following characteristics:
  • Dynamic content rendering
    Page content is heavily generated after JavaScript execution.
  • Asynchronous data loading
    Core data is loaded dynamically via XHR / Fetch requests.
  • Advanced anti-bot mechanisms
    Including (but not limited to) browser fingerprint detection, behavior analysis, CAPTCHA challenges, and request rate limiting.
  • API protection strategies
    Encrypted parameters, token validation, request signatures, and authorization checks.
  • Responsive design
    Different content is returned based on device type and environment.
In this context, **relying solely on native Python HTTP requests (such as requestsor ** httpx) is no longer sufficient for stable and reliable data collection.

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2. The Platform’s Core Value

The platform provides stable and production-ready infrastructure for browser-based scraping frameworks, including:
  • Clean and dynamic proxy IP pools
    Automatic IP rotation and geo-location switching.
  • Realistic browser fingerprint environments
    Simulating different devices, operating systems, and browser profiles to counter advanced anti-bot detection.
  • Unified concurrency and queue management
    Optimizing resource usage while avoiding excessive pressure on target websites.
  • Task scheduling, monitoring, and retry mechanisms
    Ensuring long-term stability of scraping tasks.
Users do not need to build or maintain these complex systems themselves, and can instead focus entirely on business logic, such as page parsing and data extraction.

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❌ Typical Native Python Approach

import requests

resp = requests.get(
    "https://www.tiktok.com",
    headers={"User-Agent": "Mozilla/5.0"}
)

html = resp.text

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Problems with This Approach

FeatureNative Python RequestsBrowser Automation Frameworks
JavaScript execution❌✅
Full page rendering❌✅
Anti-bot resistance❌✅
Browser fingerprinting❌✅
Stability❌✅
Platform compatibility❌✅
Conclusion:
Native Python HTTP libraries are suitable for stable, open APIs, but not for scraping modern, JavaScript-heavy websites.

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4. Scraping Framework Comparison

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Framework Feature Comparison

FeatureDrissionPagePlaywrightSeleniumPuppeteer
Language supportPythonPython / Node / Java / .NETMulti-languageNode.js
Browser supportChrome / FirefoxChromium / Firefox / WebKitChrome / Edge / Firefox / SafariChromium
PerformanceMediumHighMedium–LowHigh
Dynamic renderingMediumStrongMediumStrong
Network interceptionBasicStrongWeakStrong
Multi-tabs / contextsSupportedSupportedSupported (complex)Supported
Ease of useHighMediumMediumHigh
Ecosystem / communitySmallMediumLargeMedium
Typical use casesPython crawlers, quick automationHigh-performance, cross-browser scrapingAutomation testingNode.js scraping, screenshots

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4.1 DrissionPage

DrissionPage is a Python library that integrates Selenium and requests, enabling a hybrid approach for both dynamic and static content. Advantages:
  • Python-native with high-level APIs; interacting with pages feels like manipulating the DOM.
  • Supports combining browser rendering (via Selenium) and direct HTTP requests to reduce overhead.
  • Built-in utilities such as auto-waiting, session persistence, screenshots, and JavaScript execution.
  • Beginner-friendly and fast to adopt.
Limitations:
  • Performance and compatibility depend on Selenium.
  • Python-only.
  • Smaller community compared to Playwright and Selenium.
  • Less flexible for advanced scenarios such as deep network interception or complex gesture simulation.
Best suited for:
  • Python projects requiring both static and dynamic scraping.
  • Rapid implementation where ultra-high performance is not critical.

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4.2 Playwright

Playwright is a modern browser automation library developed by Microsoft, supporting multiple languages. Advantages:
  • Multi-browser support (Chromium, Firefox, WebKit).
  • High performance and stability via DevTools-based architecture.
  • Advanced APIs: auto-waiting, request interception, device emulation, browser contexts.
  • Supports headless and headed modes, multiple tabs, and isolated sessions.
  • Cross-platform and multi-language.
Limitations:
  • Python version is slightly slower than Node.js.
  • Steeper learning curve due to its rich feature set.
  • Smaller ecosystem than Selenium, but growing rapidly.
Best suited for:
  • High-performance scraping and automation.
  • Scenarios requiring fine-grained browser control.

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4.3 Selenium

Selenium is the most mature and widely adopted browser automation framework. Advantages:
  • Large and established community with extensive documentation.
  • Supports many languages (Java, Python, C#, Ruby, JavaScript).
  • Excellent browser compatibility.
  • Works with real browsers, making it suitable for complex workflows.
Limitations:
  • Slower startup and execution.
  • Requires manual handling of waits and synchronization.
  • Weak network request control without additional tooling.
Best suited for:
  • Web automation testing.
  • Scenarios prioritizing compatibility and stability.

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4.4 Puppeteer

Puppeteer is a Chromium-focused browser automation library developed by Google. Advantages:
  • Extremely high performance and stability on Chromium.
  • Modern, intuitive API design.
  • Powerful features: screenshots, PDF generation, request interception, device emulation.
  • Ideal for Node.js projects.
Limitations:
  • Chromium-only; limited cross-browser support.
  • Python bindings rely on third-party wrappers with slower updates.
Best suited for:
  • Node.js-based scraping and automation.
  • Chromium-specific workflows.
The platform recommends separating responsibilities as follows: 
┌─────────────────────────────────────────┐
│        Platform Infrastructure Layer     │
│  ├─ Dynamic Proxy IP Pool                │
│  ├─ Browser Fingerprint Management       │
│  ├─ Task Scheduler (Queue / Retry)       │
│  └─ Monitoring & Alerting                │
└─────────────────┬───────────────────────┘
                  │
┌─────────────────▼───────────────────────┐
│                  SDK                    │
│  ├─ Task parameter retrieval            │
│  ├─ Standardized logging                │
│  ├─ Result submission                   │
│  └─ Error handling & retries            │
└─────────────────┬───────────────────────┘
                  │
┌─────────────────▼───────────────────────┐
│       Browser Automation Frameworks      │
│   ┌──────────────────────────────────┐  │
│   │ DrissionPage | Playwright         │  │
│   │ Selenium     | Puppeteer          │  │
│   └──────────────────────────────────┘  │
└─────────────────┬───────────────────────┘
                  │
┌─────────────────▼───────────────────────┐
│      Business Logic & Data Processing    │
│  ├─ Page parsing & extraction            │
│  ├─ Data cleaning & formatting           │
│  └─ Local storage or real-time delivery  │
└─────────────────────────────────────────┘

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Responsibility Overview

ModuleResponsibility
CafeSDKParameter handling, logging, result delivery
Scraping frameworksPage access, JS rendering, DOM parsing
Fingerprint & proxyManaged centrally by the platform

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6. Conclusion

When the target website is a modern Web application rather than a traditional static page, using a real browser environment is not an optimization—it is a prerequisite. Therefore, the platform officially recommends using DrissionPage, Playwright, Selenium, or Puppeteer as the standard scraping frameworks for page-level data collection.