Know the Difference: Web Crawler vs Web Scraper

As someone with 5 years of experience primarily using proxies for web scraping, I frequently leverage services like BrightData, Soax, and Smartproxy for their reliable proxies (though I‘ve had issues with Oxylabs in the past). When I need to gather data from the web, proxies are an essential tool to avoid blocks and captcha.

Web scraping and crawling are often used interchangeably, but they actually serve different purposes. In this comprehensive guide, I‘ll explain the key differences between crawlers and scrapers, when to use each, and how they work together in the data gathering process.

What is a Web Crawler?

A web crawler, also known as a spider bot, is an automated program that browses the web to discover and catalog pages by following links. Their main function is to create an index of web pages that search engines like Google use to return relevant results for search queries.

Some key things to know about web crawlers:

They recursively browse websites by fetching pages and extracting links to follow. This allows them to crawl an entire website.
They are used by search engines to build massive indices of web page content. This content can then be quickly searched and returned for relevant queries.
They follow links and scrape minimal page data such as titles, metadata, and text content to understand what the page is about.
They aim to be exhaustive in their crawling, gathering all publicly available pages on a website.
They avoid duplicate content by recognizing pages they‘ve already crawled.
They follow rules set by robots.txt files that tell them which pages not to crawl.
They are fully automated programs and run 24/7 to keep indices up to date.

So in summary, web crawlers focus on discovering and cataloging all available web pages to create searchable databases of content. They scrape a minimal amount of data from each page they crawl.

What is a Web Scraper?

Web scrapers have a different goal than web crawlers. Their aim is to extract specific data from pages rather than cataloging their existence.

Here are some key points about web scrapers:

They are programmed to visit targeted pages and extract predefined data sets. For example, all product prices on an ecommerce site.
The data they collect is structured for analysis rather than search indexing. This could include storing data in databases or feeding it into other software.
They often don‘t need to crawl entire websites. Scrapers can be pointed at specific sections or pages to extract relevant data.
The scraping work can be done manually or be fully automated based on the use case requirements.
Robots.txt and duplicated content isn‘t a big concern for scrapers focused on gathering specific data points.
Scrapers can use techniques like parsing HTML, matching patterns, and analyzing page structures to extract data.

In summary, web scrapers are focused on gathering specific sets of data from web pages rather than indexing page content. The data extracted is structured for further analysis rather than search.

Key Differences Between Crawlers and Scrapers

Now that we‘ve defined what web crawlers and web scrapers are separately, let‘s look at some of the key differences between them:

Purpose: Crawlers aim to catalog pages for search indexing. Scrapers extract defined data sets for analysis.
Scope: Crawlers attempt to be comprehensive and gather all pages. Scrapers target specific pages or sections.
Data Collected: Crawlers collect minimal data focused on page content and metadata. Scrapers collect targeted structured data.
Techniques: Crawlers recursively follow links. Scrapers directly visit pages and extract data through parsing, pattern matching, etc.
Automation: Crawlers are fully automated. Scraping can be automated or done manually.
Speed: Crawlers operate continuously at large scale. Scraping speed depends on use case requirements.

While their goals are different, crawlers and scrapers both aid in the process of gathering data from the web. In many cases, they work together with crawlers providing scrapers initial lists of pages to target.

Web Crawler Architecture

The core components of a web crawler architecture include:

Frontier: A queue of URLs to be crawled, prioritized based on objectives like coverage or freshness.
Fetcher: Downloads and retrieves page content from URLs in the frontier.
Parser: Extracts information like text content and links from downloaded pages.
Indexer: Stores and organizes extracted page data for search indexing.
Prioritizer: Evaluates which pages in the frontier should be crawled next based on rules.
Scheduler: Takes direction from the prioritizer and feeds URLs to the fetcher.

To start, seed URLs are placed in the frontier. The fetcher downloads these pages and the parser extracts information. New links get re-added to the frontier while page data gets indexed. The prioritizer determines crawl order to optimize objectives.

This architecture continuously runs to keep the indexed search database updated as the web changes. Search engines operate crawlers at massive scale to index the entire web.

When to Use Crawlers vs Scrapers

Now that we understand their differences, when should each tool be used? Here are some general guidelines:

Use a crawler when you need to:

Create a searchable index of all pages on a website or set of sites
Continuously re-crawl sites to keep an index up to date
Build a database of web documents for analysis (text mining, NLP, etc)
Discover links to new or deep pages that can then be scraped

Use a scraper when you need to:

Extract specific structured data from pages for analysis
Gather data from just a portion of a site rather than all pages
Perform one-off data extractions rather than ongoing crawling
Complement a crawler by scraping pages it finds

Scrapers and crawlers solve different but complementary problems in gathering web data. Here are a few examples of how they can work together:

A crawler indexes a retail site. A scraper then extracts product data from pages the crawler found.
A news crawler identifies article pages. A scraper extracts article text for trend analysis.
A marketing crawler maps a site‘s link structure. A scraper gathers contact info for outreach.

Determining whether a crawler or scraper better serves your use case will ensure you apply the right tool for the job.

Web Scraping Basics

Now that we‘ve covered web crawlers in depth, let‘s provide some more detail around web scrapers. Here are the key steps in a basic web scraping workflow:

1. Identify Target Pages

First, determine the specific set of pages you want to scrape. This could be product pages, article listings, or search results. Crawlers can help identify a site‘s entire page structure.

2. Extract Relevant Data

Next, use information extraction techniques to pull the required data points from each page. Common approaches include:

Pattern Matching: Look for data using regular expressions that match the format of target data.
HTML Parsing: Traverse and analyze a page‘s DOM structure to locate elements.
Analyzing Site Templates: Understand how templates are used to display records and extract accordingly.

3. Store Data

With data extracted, it needs to be structured and stored. Exporting to CSV, JSON, databases, or other formats allows analysis in other tools.

4. Manage Scoping & Frequency

Determine whether you need a one-time scrape or ongoing automation. Scope if only sampling data is needed.

Following these steps allows scrapers to gather targeted, structured data from web pages for any purpose.

Tools for Crawling vs Scraping

There are a variety of tools available for both web crawling and web scraping depending on your needs:

Web Crawler Tools

Search Engine Crawlers like Googlebot and Bingbot automatically crawl the web.
API Based Crawlers like the Bing Web Crawler API allow customized crawling.
Configurable Open Source Crawlers like Scrapy and Apache Nutch provide control for advanced users.
Crawl Services like Mozbot and Screaming Frog are paid crawler services.

Web Scraper Tools

Visual Tools like ParseHub, Portia, and Octoparse for scrapers without coding.
Scraper APIs like PromptCloud andScraperAPI for fast scraping without maintenance.
Browser Automation libraries like Puppeteer, Playwright, and Selenium for scraper customization.
General Purpose Languages like Python and Javascript can build customized scrapers.

The ideal tool depends on your budget, technical skills, and customization needs. For one-off scraping tasks, an API or visual tool may be the fastest option. For complex scraping needs, open source or coding your own solution provides the most control.

Wrapping Up

To recap, while web crawling and web scraping are related concepts, there are some core differences:

Web crawlers catalog and index all pages on sites for search engines. Web scrapers extract specific data sets for analysis.
Crawlers recursively follow links and scrape minimal data focused on indexing. Scrapers directly visit and parse pages to collect targeted structured data.
Crawlers aim for exhaustive coverage and continuous operation. Scrapers can target portions of sites and conduct one-off extractions.
Crawlers power search engines while scrapers enable uses like data mining, price monitoring, contact gathering, and more.
Tools for each task range from fully automated crawlers and APIs to highly configurable open source platforms.

Understanding these differences allows selecting whether a crawler, scraper, or both are needed for a project. By knowing how these technologies complement each other, robust data pipelines can be built leveraging the strengths of each approach.