StormCrawler vs Scrapy

Struggling to choose between StormCrawler and Scrapy? Both products offer unique advantages, making it a tough decision.

StormCrawler is a Development solution with tags like crawler, scraper, storm, distributed, scalable.

It boasts features such as Distributed web crawling, Fault tolerant, Horizontally scalable, Integrates with other Apache Storm components, Configurable politeness policies, Supports parsing and indexing, APIs for feed injection and pros including Highly scalable, Resilient to failures, Easy integration with other data pipelines, Open source with active community.

On the other hand, Scrapy is a Development product tagged with scraping, crawling, parsing, data-extraction.

Its standout features include Web crawling and scraping framework, Extracts structured data from websites, Built-in support for selecting and extracting data, Async I/O and item pipelines for efficient scraping, Built-in support for common formats like JSON, CSV, XML, Extensible through a plug-in architecture, Wide range of built-in middlewares and extensions, Integrated with Python for data analysis after scraping, Highly customizable through scripts and signals, Support for broad crawling of websites, and it shines with pros like Fast and efficient scraping, Easy to scale and distribute, Extracts clean, structured data, Mature and well-supported, Integrates well with Python ecosystem, Very customizable and extensible.

Feature	StormCrawler	Scrapy
Overall Score	N/A	N/A
Primary Category	Development	Development

Product Overview

StormCrawler

Description: StormCrawler is an open source web crawler designed to crawl large websites efficiently by scaling horizontally through Apache Storm. It is fault-tolerant and allows integration with other Storm components like machine learning pipelines.

Scrapy

Description: Scrapy is an open-source web crawling framework used for scraping, parsing, and storing data from websites. It is written in Python and allows users to extract data quickly and efficiently, handling tasks like crawling, data extraction, and more automatically.

Key Features Comparison

StormCrawler Features

• Distributed web crawling
• Fault tolerant
• Horizontally scalable
• Integrates with other Apache Storm components
• Configurable politeness policies
• Supports parsing and indexing
• APIs for feed injection

Scrapy Features

• Web crawling and scraping framework
• Extracts structured data from websites
• Built-in support for selecting and extracting data
• Async I/O and item pipelines for efficient scraping
• Built-in support for common formats like JSON, CSV, XML
• Extensible through a plug-in architecture
• Wide range of built-in middlewares and extensions
• Integrated with Python for data analysis after scraping
• Highly customizable through scripts and signals
• Support for broad crawling of websites

Pros & Cons Analysis

StormCrawler

Pros

• Highly scalable
• Resilient to failures
• Easy integration with other data pipelines
• Open source with active community

Cons

• Complex setup and configuration
• Requires running Apache Storm cluster
• No out-of-the-box UI for monitoring
• Limited documentation and examples

Scrapy

Pros

• Fast and efficient scraping
• Easy to scale and distribute
• Extracts clean, structured data
• Mature and well-supported
• Integrates well with Python ecosystem
• Very customizable and extensible

Cons

• Steep learning curve
• Configuration can be complex
• No GUI or visual interface
• Requires proficiency in Python
• Not ideal for simple one-off scraping tasks

Pricing Comparison

StormCrawler

• Open Source

Scrapy

• Open Source