As a full-stack developer, I‘ve worked on a wide variety of data-intensive projects over the years. One common challenge that comes up again and again is the need to efficiently gather large amounts of data from websites.
While it‘s certainly possible to build your own web scrapers and crawlers using tools like Python‘s Scrapy and Beautiful Soup libraries, I‘ve learned the hard way that building and maintaining custom crawlers is often a poor use of development resources. It‘s time-consuming, error-prone, and requires ongoing work to keep up with changes to the target sites.
In most cases, it‘s much more efficient to leverage a pre-built web crawling tool that abstracts away the low-level details and provides a user-friendly interface for defining and managing your crawl jobs. But with dozens of web crawler tools on the market, how do you choose the right one for your needs?
The Challenges of Web Crawling at Scale
Before we dive into evaluating specific tools, it‘s worth taking a step back and understanding some of the key challenges involved in web crawling. While it may seem simple at first glance – just request some URLs and extract the desired data from the response – there are many potential issues that can trip up crawlers.
Some of the most common challenges include:
Dynamic sites with heavy JavaScript: Many modern websites rely heavily on JavaScript to render content on the client side. This can make them difficult to crawl, as the raw HTML response from the server may not include the desired data. Crawlers need to be able to execute JavaScript and wait for pages to fully render.
CAPTCHAs and anti-bot measures: Popular sites are constantly being bombarded by bots, and many employ various techniques to try to prevent non-human traffic. These may include CAPTCHAs, rate limiting, user agent detection, honeypot links, and more. Crawlers need to be able to handle these anti-bot countermeasures.
Frequent site structure changes: Websites evolve over time, and crawlers that are dependent on specific page structures can easily break when those structures change. An intelligent crawler should be able to adapt to gradual site changes.
Inconsistent site performance: Some sites may be slow to respond or generate errors intermittently. Crawlers need to be resilient to these performance issues and able to gracefully handle errors.
According to a study by Imperva, a staggering 37.9% of all Internet traffic is from bots, and 24.1% of that traffic is from "bad" bots engaged in malicious activities like content scraping, fraud, and vulnerability scans. It‘s no wonder that sites are constantly adapting to try to block unwanted bot traffic.
Evaluating Web Crawler Software
With those challenges in mind, I set out on a mission to evaluate the landscape of web crawler tools and find one that could meet my needs as a developer across a variety of projects. Some of the key criteria I looked at included:
- Ease of use, especially for non-technical team members
- Ability to handle complex, JavaScript-heavy sites
- Features for bypassing common anti-bot measures
- Extensibility and APIs for custom integrations
- Cloud-based option for easy scaling
- Quality of documentation and customer support
- Transparent, affordable pricing
I compiled a list of well-known crawler tools to evaluate, including:
- Mozenda
- Scrapy Cloud
- Octoparse
- Import.io
- ParseHub
- Web Scraper (browser extension)
- Puppeteer
- Cheerio
After reading through the feature lists and documentation for each tool, I narrowed it down to a few top contenders for hands-on testing, including Mozenda, Scrapy Cloud, and Octoparse.
Hands-On Testing
For my initial hands-on testing, I decided to crawl a leading e-commerce site to extract product, pricing, and review data for a specific category of products. This is a common use case and would allow me to test how each tool handled a complex, JavaScript-heavy site with frequent layout changes.
Scrapy Cloud
Scrapy Cloud is a cloud-based platform for running web crawlers built on top of the popular open-source Scrapy framework. As an experienced Python developer, I liked that I could use the familiar Scrapy API and libraries.
However, I found that Scrapy Cloud isn‘t the most user-friendly for non-developers. It requires a good bit of Python coding to get everything configured. The UI for monitoring and managing crawl jobs is also fairly minimal.
Despite being cloud-based, I still ran into some performance issues when crawling the JavaScript-heavy e-commerce site. Scrapy wasn‘t able to handle some of the dynamic pop-up elements, leading to incomplete data extraction.
Octoparse
Next up was Octoparse, a desktop-based tool with a visual point-and-click interface for setting up crawlers. Of the three tools I tested, Octoparse was the easiest for a non-technical user to pickup and start using quickly.
However, that ease of use comes with some tradeoffs. I found the desktop app to be a bit clunky to use, with a fair number of bugs and crashes. When I tried to scale up my crawling jobs, I ran into performance limits due to the crawlers running locally on my machine rather than in the cloud.
Octoparse does offer a cloud-based option, but it‘s quite expensive – their cheapest cloud plan starts at $499/month, which is overkill for my needs.
Mozenda
Finally, I tested Mozenda, a cloud-based crawler tool with a nicely designed web interface. I was immediately impressed with the thorough documentation and video tutorials that made it easy to get started.
Like Octoparse, Mozenda uses a visual point-and-click interface for defining crawl jobs. But I found Mozenda‘s interface to be more intuitive and reliable than Octoparse‘s. It also offers some handy features like the ability to define custom regular expressions for extracting data from complex page elements.
Mozenda‘s cloud-based architecture delivered impressive performance. It was able to crawl the full e-commerce site, including all the JavaScript-rendered content, significantly faster than either Scrapy Cloud or Octoparse. And I didn‘t have to worry about provisioning any infrastructure.
I was also impressed with Mozenda‘s built-in features for bypassing common anti-bot measures. It automatically handles CAPTCHAs, user agent rotation, and other evasion techniques. You can configure the level of anti-bot countermeasures you want to add, depending on the level of protection of your target sites.
Another standout feature of Mozenda is the ability to set up scheduled crawls and receive alerts if a crawl job fails or the extracted data doesn‘t match your expected patterns. This allows me to "set it and forget it" – I can rely on Mozenda to keep my data up to date and proactively surface any data quality issues.
Putting Mozenda to the Test
After my initial evaluation, I decided to use Mozenda for a few real-world projects to see how it would perform. One of these projects involved building a market intelligence dashboard for a client in the automotive industry. The goal was to provide a daily rundown of news mentions, social media discussions, and competitor website changes related to the client‘s brand and products.
Using Mozenda, I was able to quickly set up crawlers for a hand-picked set of industry news sites, forums, and competitor websites. For each site, I defined the key page elements to extract (e.g. article title, date, author, content). Here‘s an example using Mozenda‘s point-and-click interface to extract article data from a news site:
I set the crawlers up to run automatically each morning, scraping any new content that was published within the past 24 hours. The extracted data gets automatically exported to a SQL database in a structured format, with each crawled page generating a new row with columns for each extracted data point.
From there, it was easy to integrate the scraped data into the client‘s BI dashboard. Using SQL and a bit of Python, I pulled the latest crawl results into a Pandas data frame each morning, did some light data cleaning and sentiment analysis, and generated a daily email report highlighting the key insights.
Here‘s a snippet of the Python code I used to retrieve the scraped data from Mozenda via their API:
import mozenda as mz
# Connect to Mozenda API
agent = mz.Mozenda(api_key=‘my_api_key‘)
# Get data from the crawl with ID 12345
results = mozenda.get_data(12345)
# Convert to Pandas data frame
import pandas as pd
df = pd.DataFrame(results)Mozenda‘s API made it straightforward to incorporate the web crawling workflow into my existing data pipeline. I was even able to use some of Mozenda‘s built-in data transformation features to do basic cleaning, like removing HTML tags and extracting dates, further reducing the amount of custom code I had to write.
The daily reports quickly became a valuable resource for the client. The Mozenda-powered crawlers surfaced several important discussions and announcements that their in-house team had missed. In one case, a crawler picked up on a press release from a competitor about a new product launch a full day before the client‘s team found out about it through other channels.
The ROI of Web Crawling
This automotive market intelligence project is just one example of the power of web crawling when applied to real-world business needs. Some of the other common use cases I‘ve come across include:
Price monitoring: Keeping tabs on competitor pricing and stock levels for key products. A 2021 survey by Revuze found that 76% of retailers use web crawling for competitor price monitoring.
Lead generation: Extracting contact information for potential customers from sites like LinkedIn, GitHub, and industry forums.
Brand monitoring: Tracking discussions and sentiment related to a brand across news sites, blogs, forums, and social media.
SEO: Analyzing search engine result pages (SERPs) to uncover content gaps and link building opportunities.
According to a recent survey by Bright Data (formerly Luminati Networks), 26% of all businesses use web scraping in some form, and usage is growing quickly. The most common department using web scraping is IT (45% of respondents), but marketing (35%) and finance (23%) are also heavy users.
Source: Bright Data
Web scraping and crawling is no longer just a niche technique used by a few tech-savvy companies – it‘s becoming a mainstream tool across industries and job functions. As the volume of valuable web data continues to grow exponentially, being able to efficiently collect and extract insight from that data is becoming a key competitive differentiator.
Conclusion
As a full-stack developer, I know first-hand how powerful web crawling can be for streamlining data collection and powering data-driven applications. But I also know that building and maintaining web crawlers in-house is often not the best use of development resources.
That‘s why I‘m glad to have found Mozenda – a crawler tool that strikes the right balance between ease of use, performance, and customization. It allows me to get up and running with crawlers quickly while still giving me the flexibility to tackle complex use cases. The time savings compared to building crawlers from scratch has been substantial.
If you‘re a developer looking to incorporate web scraping into your projects, I highly recommend checking out Mozenda. It‘s become an essential part of my toolkit, and I continue to find new ways to use it to deliver value for my clients and users.
While no tool is perfect, Mozenda comes closest to being a great all-around web crawler for developers and non-technical users alike. Give their free trial a spin and see how it can level up your data game.