. 1,000,000 URLs is the row limit of Excel, and matches the import limit of URL Profiler. However, processing this many URLs will take up a lot of your computer resources (CPU/RAM). Most desktop computers can comfortably handle 50k-100k URLs, and decent servers can handle 250,000 URLs. The Agency license allows up to 20 users.
Jan 07, 2019 While working on a project recently, I needed to grab some google search results for specific search phrases and then scrape the content from the page results. For example, when searching for a Sony 16-35mm f2.8 GM lens on google, I wanted to grab some content (reviews, text, etc) from the results. While this isn’t hard. ScrapeBox is a Windows and Apple Mac software. It runs on Windows XP, Vista, Windows 7, Windows 8, Windows 8.1, Windows 10, Windows 2003/2008/2012 Server on both 32 and 64 Bit as well as Apple Mac up to v10.15.x Catalina. It will also work under Windows emulation such as Parallels, VMWare, BootCamp etc on Linux. Aug 30, 2018 Search Google for a user given input, up to first 10 pages, and scrape all the URL titles, URLs and Descriptions and store it as a CSV/JSON in your local system.
Scrape emails from Craigslist
You can grab emails with the email grabber in the harvested urls section. It will let you harvest emails from a url or a local file.
Say you wanted to harvest emails from the Jobs category on Craigslist.
In a regular web browser open up Craigslist. Find the category you want to harvest from, in the case of the jobs category, most major cities it looks like this:
http://losangeles.craigslist.org/jjj/
I got this by selecting the city I wanted, and then clicking the 'jobs' link at the top of the category.
Then you would copy down that url, which is what is above. Note: make sure that if it gives you a spam warning you follow thru to get the actual url of the page that lists the ads.
If you like you can also copy down the urls of the 'Next 100 results'.
Then save off all of the urls from the categories you want.
Then import them into the Link Extractor addon.
Choose Internal only.
Then let it harvest all the urls from those pages. This will give you all the current craigslist ads for each category from all the pages you choose.
Then export the results to a txt file.
Then import that txt file into the urls harvester section.
Then use the email grabber to get the emails from those urls. Thus you have scraped all the emails from Craigslist for the current ads from the categories you have chosen.
The best part is the category urls are static, but the urls that you harvest from them change daily, so you can repeat this process over and over.
A Web Crawler is a program that navigates the Web and finds new or updated pages for indexing. The Crawler starts with seed websites or a wide range of popular URLs (also known as the frontier) and searches in depth and width for hyperlinks to extract.
A Web Crawler must be kind and robust. Kindness for a Crawler means that it respects the rules set by the robots.txt and avoids visiting a website too often. Robustness refers to the ability to avoid spider traps and other malicious behavior. Other good attributes for a Web Crawler is distributivity amongst multiple distributed machines, expandability, continuity and ability to prioritize based on page quality.
1. Steps to create web crawler
The basic steps to write a Web Crawler are:
- Pick a URL from the frontier
- Fetch the HTML code
- Parse the HTML to extract links to other URLs
- Check if you have already crawled the URLs and/or if you have seen the same content before
- If not add it to the index
- For each extracted URL
- Confirm that it agrees to be checked (robots.txt, crawling frequency)
Truth be told, developing and maintaining one Web Crawler across all pages on the internet is… Difficult if not impossible, considering that there are over 1 billion websites online right now. If you are reading this article, chances are you are not looking for a guide to create a Web Crawler but a Web Scraper. Why is the article called ‘Basic Web Crawler’ then? Well… Because it’s catchy… Really! Few people know the difference between crawlers and scrapers so we all tend to use the word “crawling” for everything, even for offline data scraping. Also, because to build a Web Scraper you need a crawl agent too. And finally, because this article intends to inform as well as provide a viable example.
2. The skeleton of a crawler
For HTML parsing we will use jsoup. The examples below were developed using jsoup version 1.10.2.
So let’s start with the basic code for a Web Crawler.
![Scrape Scrape](/uploads/1/2/6/6/126603363/965798791.png)
BasicWebCrawler.java
Os X Scrape Email For Urls 2017
Note
Don’t let this code run for too long. It can take hours without ending.
Don’t let this code run for too long. It can take hours without ending.
Sample Output:
Like we mentioned before, a Web Crawler searches in width and depth for links. If we imagine the links on a web site in a tree-like structure, the root node or level zero would be the link we start with, the next level would be all the links that we found on level zero and so on.
3. Taking crawling depth into account
We will modify the previous example to set depth of link extraction. Notice that the only true difference between this example and the previous is that the recursive
getPageLinks()
method has an integer argument that represents the depth of the link which is also added as a condition in the if...else
statement.Note
Feel free to run the above code. It only took a few minutes on my laptop with depth set to 2. Please keep in mind, the higher the depth the longer it will take to finish.
Feel free to run the above code. It only took a few minutes on my laptop with depth set to 2. Please keep in mind, the higher the depth the longer it will take to finish.
Sample Output:
4. Data Scraping vs. Data Crawling
So far so good for a theoretical approach on the matter. The fact is that you will hardly ever build a generic crawler, and if you want a “real” one, you should use tools that already exist. Most of what the average developer does is an extraction of specific information from specific websites and even though that includes building a Web Crawler, it’s actually called Web Scraping.
There is a very good article by Arpan Jha for PromptCloud on Data Scraping vs. Data Crawling which personally helped me a lot to understand this distinction and I would suggest reading it.
To summarize it with a table taken from this article:
Os X Scrape Email For Urls Windows 10
Data Scraping | Data Crawling |
---|---|
Involves extracting data from various sources including the web | Refers to downloading pages from the web |
Can be done at any scale | Mostly done at a large scale |
Deduplication is not necessarily a part | Deduplication is an essential part |
Needs crawl agent and parser | Needs only crawl agent |
Time to move out of theory and into a viable example, as promised in the intro. Let’s imagine a scenario in which we want to get all the URLs for articles that relate to Java 8 from mkyong.com. Our goal is to retrieve that information in the shortest time possible and thus avoid crawling through the whole website. Besides, this approach will not only waste the server’s resources, but our time as well.
5. Case Study – Extract all articles for ‘Java 8’ on mkyong.com
5.1 First thing we should do is look at the code of the website. Taking a quick look at mkyong.com we can easily notice the paging at the front page and that it follows a
/page/xx
pattern for each page.That brings us to the realization that the information we are looking for is easily accessed by retrieving all the links that include
/page/
. So instead of running through the whole website, we will limit our search using document.select('a[href^='http://www.mkyong.com/page/']')
. With this css selector
we collect only the links that start with http://mkyong.com/page/
.5.2 Next thing we notice is that the titles of the articles -which is what we want- are wrapped in
<h2></h2>
and <a href='></a>
tags. So to extract the article titles we will access that specific information using a
css selector
that restricts our select
method to that exact information: document.select('h2 a[href^='http://www.mkyong.com/']');
5.3 Finally, we will only keep the links in which the title contains ‘Java 8’ and save them to a
file
.Output: