Web Crawling Vs. Web Scraping

Politeness settings: Configuration to avoid overloading servers, e.g. maximum requests per second, delays between requests to the same domain, obeying robots.txt.

Data storage: Mechanisms to efficiently store fetched content, metadata, and the URL graph, e.g. using distributed file systems, databases, or search indexes.

Crawlers can be configured to stay within a particular domain/subdomain or to venture across sites. Additional settings control things like maximum crawl depth (link hops from seed URLs), frequency of recrawling previously seen pages, file type filters, and maximum content size.

Popular open source tools for web crawling include:

• Scrapy: Python framework that allows for flexible configuration of crawling rules and pipelines. Handles multithreading, request queueing, and retry logic.

• Apache Nutch: Extensible Java-based crawler that can run on Hadoop for distributed operation. Offers plugin system for custom parsing, data normalization, and storage.

• Heritrix: Archival-quality crawler used by Internet Archive. Designed for long-running, large-scale crawls. Supports WARC file format for preservation.

How Web Scrapers Work

Web scrapers take a more targeted approach than crawlers, homing in on specific pages and elements rather than following every link. The goal is to extract structured data like prices, contact info, or other details into a machine-readable format for further analysis.

Configuring a web scraper involves:

1. Identifying the target websites and pages that contain the desired data
2. Determining which specific HTML elements hold the relevant information
3. Writing code to make HTTP requests, parse the HTML, and extract those elements
4. Saving the extracted data into a structured format like CSV or JSON
5. Scaling up the extraction job to handle thousands or millions of pages

Some key aspects that differentiate scraping from crawling:

Precision: Scrapers use CSS selectors, XPath expressions, or regex patterns to precisely pinpoint the data elements, ignoring boilerplate content.

Javascript rendering: Many modern sites build page content dynamically with front-end JS frameworks. Scrapers often use headless browsers like Puppeteer or Selenium to execute scripts and access the full DOM.

Concurrent requests: To optimize efficiency, scrapers typically send requests in parallel, carefully managing politeness and balancing between targets.

Data validation: Scraped values are checked against expected data types (int, float, date, etc.) and re-fetched if missing or invalid. Data normalization and cleaning are also common.

Popular tools for web scraping include:

• Parsehub: Visual tool that uses a point-and-click workflow to define scraping targets and rules. Useful for non-programmers.

• Octoparse: Another codeless option that offers built-in support for Javascript rendering, pagination, and form filling.

• Mozenda: Cloud-based platform that provides a hybrid of visual configuration and custom scripting for flexibility.

Growth and Adoption Trends

The market for web scraping tools and services continues to grow rapidly as more businesses recognize the value of data-driven decision making. A recent survey found that 45% of companies now use web scraping for marketing, financial analysis, ecommerce optimization, and other use cases.

The web scraping industry is expected to be worth $5.7 billion by 2025, growing at a CAGR of 15.6% from 2020 to 2025 according to Market Research Future. On the jobs front, postings for web scraping skills have grown 200% over the last 5 years based on data from Indeed.

Use Cases and Industry Applications

Some of the most common use cases I see for web crawling include:

Search engine indexing: Google, Bing, Yandex and other search engines constantly crawl the web to discover new content and update their indexes.

Website testing: Developers often run crawlers on their own sites to find broken links, 404 errors, redirect loops, and other technical SEO issues.

Copyright monitoring: Media companies use crawlers to scour the web for unauthorized uses of their articles, images, videos, and other assets.

Archiving: The Internet Archive and other digital preservation projects use large-scale crawlers to create historical snapshots of the web.

Web scraping powers a variety of business and research applications:

Ecommerce price monitoring: Retailers like Amazon, Walmart, and Target routinely scrape competitor sites to inform dynamic pricing and uncover arbitrage opportunities.

Financial data aggregation: Bloomberg, Dow Jones, and other business intelligence firms aggregate stock prices, company fundamentals, analyst ratings, and news from across the web.

Lead generation: Marketers scrape contact info of potential customers from industry directories, social networks, and review sites for targeted outreach.

Travel metasearch: Expedia, Booking.com, Tripadvisor and other travel sites scrape airline, hotel, and car rental listings to present comparison shopping results to users.

Choosing the Right Tools and Platforms

Web-scale crawling requires significant compute resources to quickly fetch, process, and store billions of pages. Many organizations rely on cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud to run production crawlers.

Key services used for crawling pipelines include:

• Storage (S3, Cloud Storage) for fetched HTML and metadata
• Virtual machines (EC2, Azure VMs) to run crawler processes
• Message queues (SQS, Pub/Sub) to manage URL frontier
• NoSQL databases (DynamoDB, Bigtable) to track crawl state
• Big data processing (EMR, Dataproc) to analyze crawled content

For web scraping, speed and flexibility are essential to extract data from different page structures at scale. Many scraping pipelines also leverage cloud platforms:

• Serverless functions (Lambda, Cloud Functions) to run scraper code on-demand
• Storage (S3, Cloud Storage) to land extracted data
• Managed databases (RDS, Cloud SQL) to store structured results
• Workflow orchestration (Airflow, Kubeflow) to manage dependencies
• BI and visualization (Tableau, Looker) to analyze and report on scraped data

There are also many SaaS platforms that provide web scraping capabilities without having to run your own infrastructure. These include:

• Scrapy Cloud: Managed service for running Scrapy spiders at scale
• Scraperbox: Serverless scraping platform with a simple API
• ParseHub: No-code tool for running scraping jobs in the cloud

Using Proxies for Web Crawling and Scraping

When making large volumes of HTTP requests to websites, it‘s important to be a good citizen and avoid overloading servers or getting your IP address blocked. Proxy servers allow you to route requests through an intermediary IP address, distributing traffic and masking your identity.

There are several types of proxies used for web scraping:

Data center proxies: IP addresses hosted in data centers, offering high speeds and low costs. Good for large-scale scraping of non-sensitive targets.

Residential proxies: IP addresses tied to real consumer devices and homes, providing more trust and anonymity. Useful for scraping targets that block data center IPs.

Mobile proxies: IP addresses from cellular carriers, often used to simulate traffic from mobile devices for app testing and ad verification.

The most popular proxy providers include:

• Bright Data: Wide selection of data center, residential, and mobile IPs with granular geotargeting options.

• Smartproxy: Residential and data center IPs in over 195 countries. Simple pricing model based on bandwidth usage.

• Oxylabs: Large residential proxy pool with rotating IPs and low failure rates. Offers automated proxy rotators.

• Proxy-Seller: Affordable residential and data center proxies with backconnect gateway for simplified rotation.

Tips for Safe and Effective Proxy Usage:

1. Use the right type of proxy for your use case to maximize performance and minimize blocks

2. Spread requests across many different IPs to avoid hitting rate limits from a single address

3. Rotate IPs after a certain number of requests or time interval to avoid extended sessions

4. Geotarget requests to the same country as your target sites for best success rates

5. Respect robots.txt rules and set a modest request rate per domain to avoid adverse impacts

Handling CAPTCHAs and Anti-Bot Measures

As bots have proliferated on the web, many sites employ measures to block or slow down automated access. One common measure is the CAPTCHA, which presents a visual test (e.g. identifying objects in an image) that is easy for humans but hard for bots to solve.

When CAPTCHAs appear, they can stall web scraping pipelines that expect to be able to make requests unimpeded. Some strategies for dealing with CAPTCHAs include:

1. Using a CAPTCHA solving service like 2Captcha or DeathByCaptcha which employs human workers to solve CAPTCHAs on demand through an API

2. Routing requests through a headless browser with extensions that automatically solve common CAPTCHA formats

3. Slowing down request rate and rotating IPs if CAPTCHAs appear to avoid seeming too bot-like

4. Using machine learning models trained on image sets to attempt to solve CAPTCHAs automatically

Other techniques I‘ve seen sites use to prevent scraping include:

• Rate limiting based on IP, user agent, or request patterns
• Serving altered HTML to bots with honeypot links and hidden fields
• Frequently changing page structure and class names to break scrapers
• Requiring logged-in sessions or API keys to access content
• Taking legal action against aggregators that violate terms of service

Staying on top of these anti-bot measures requires constant monitoring and adaptation. Using premium proxies, adjusting request rates, and handling CAPTCHAs will minimize the chances of your scrapers getting blocked.

Legal and Compliance Issues

The legality of web crawling and scraping is a complex issue that depends on jurisdiction and specific use case. In general, courts have held that scraping publicly accessible data is permitted, but terms of service can restrict automated access.

Some key legal cases that have impacted the legal landscape for web scraping include:

• HiQ vs LinkedIn (2019): 9th Circuit Court of Appeals ruled that scraping publicly accessible data does not violate the CFAA.

• Ryanair vs PR Aviation (2015): European Court of Justice found screen scraping lawful even when prohibited by website terms.

• Craigslist vs 3Taps (2013): Craigslist sued data aggregators 3Taps and PadMapper for scraping housing listings, resulting in a $1 million settlement.

When scraping personal data of individuals in Europe, you also need to ensure GDPR compliance. Under the GDPR, scraped personal data may only be processed if you have a valid legal basis like consent from the data subject. You must also provide notice about your data collection practices and honor requests to access or delete an individual‘s data.

To stay within ethical and legal bounds when scraping, I advise my clients to:

1. Only scrape data that is factual and publicly accessible, avoiding copyrighted materials

2. Respect instructions in robots.txt that prohibit automated access to specific pages or sections intended for humans only

3. Avoid scraping content behind a login wall or authentication barrier

4. Set a reasonable request rate and concurrent connections to avoid negatively impacting a site‘s performance or availability

5. Consult with legal counsel to evaluate your specific use case and jurisdiction

The Bottom Line

Web crawling and scraping are both powerful techniques for collecting data from websites at scale, but they differ in approach, use cases, and outputs.

Crawlers are suited for broad discovery and indexing of sites, while scrapers are ideal for targeted extraction of structured data points. By understanding the key considerations – from technical implementation to legal compliance – you can effectively apply these techniques while staying within acceptable bounds.

As the volume and variety of web data continues to grow, I expect to see more organizations turn to crawling and scraping to drive marketing, product, and strategic decisions. Those who can master these skills while adapting to evolving technologies and regulations will be well positioned to compete.

If you found this guide helpful or have further questions, feel free to reach out. Happy crawling and scraping!