Web Scraping: Introduction and Best Reverse Engineering to Collect Data
The First Step of Web Scraping is sent a request to the server. This is the most important step for scraping of any website. Different backgrounds of people in terms of coding skills, use different programming languages to send a request. Today we show you different types of HTTP web requests in three different programming languages.
Http Web Request has Below Part :
1 – Header:
Any web request has header part. The header is given an identity of to server, from where the request for data is coming, which user – is it desktop user? or is it a mobile user? or Is it Tablet User.
Http, Web request is a different type, Like GET, POST, PUT, DELETE and many more. Each of these contains its own definition. Http web request is medium to communicate between client and server, the client means us and server means website or host.
GET— HTTP get request is the simplest way to get data from a website or host or any online resource. Below is a code snippet to send Http web request in c# using Http web request lib.
# Example showing how to use the requests library # Install requests moddule using below command # pip install requests # Import Moduel import requests # Send Request r = requests.get("https://www.worthwebscraping.com/services/") # Print Responce print (r.text())
Now you got the webpage, but now you need to extract the data. BeautifulSoup is a very powerful Python library which helps you to extract the data from the page. It’s easy to use and has a wide range of APIs that’ll help you to extract the data. We use the requests library to fetch an HTML page and then use the Beautiful Soup to parse that page. In this example, we can easily fetch the page title and all links on the page. Check out the documentation for all the possible ways in which we can use BeautifulSoup.
from bs4 import BeautifulSoup import requests #Fetch HTML Page r = requests.get("https://www.worthwebscraping.com/services/") soup = BeautifulSoup(r.text, "html.parser") #Parse HTML Page print "Webpage Title:" + soup.title.string print "Fetch All Links:" soup.find_all('a')
CHALLENGES IN SCRAPING:
Below are challenges when we scrap data at large scaleWhen we scrap data from more than one website we face issues in convert and generalize the data and store it into a database. Each website has its own structure of HTML. Most website changes their UI periodically, due to that some time we get incomplete data or crash scraper. This is the most commonly encountered problem.
Anti- Scraping Technologies:
Now a days many companies use anti-scraping script to protect their website from scraping and data mining. LinkedIn is a good example of this. If you scrap data from one single ip, they catch you and banned your ip address, sometimes they block your account also.
Honeypot traps :
Some website designers put honeypot traps inside websites to detect web spiders, There may be links that normal user can’t see and a crawler can. Some honeypot links to detect crawlers will have the CSS style “display: none” or will be color disguised to blend in with the page’s background color. This detection is obviously not easy and requires a significant amount of programming work to accomplish properly.
Captchas have been around for a long time and they serve a great purpose — keeping spam away. However, they also pose a great deal of accessibility challenge to the web crawling bots out there. When captchas are present on a page from where you need to scrape data from, basic web scraping setups will fail and cannot get past this barrier. For this, you would need a middleware that can take captcha, solve it and return the response.
POINTS SHOULD BE TAKE CARE DURING DATA SCRAPING:
Respect the robots.txt file:
Below are some important when we scrap data at large scale. we have to take care of it.Robots.txt is a text file webmasters create to instruct robots (typically search engine robots) how to crawl & index pages on their website So this file generally contain instruction for crawlers. Robots.txt should be the first thing to check when you are planning to scrape a website. Every website would have set some rules on how bots/spiders should interact with the site in their robots.txt file. Some websites block bots altogether in their robots file. If that is the case, it is best to leave the site and not attempt to crawl them. Scraping sites that block bots is illegal. Apart from just blocking, the robots file also specifies a set of rules that they consider as good behavior on that site, such as areas that are allowed to be crawled, restricted pages, and frequency limits for crawling. You should respect and follow all the rules set by a website while attempting to scrape it. Usually at the website admin area, one can find this file.
Do not hit the servers too Fast :
Web servers are not fail-proof. Any web server will slow down or crash if the load on it exceeds a certain limit up to which it can handle. Sending multiple requests too frequently can result in the website’s server going down or the site becoming too slow to load. While scraping, you should always hit the website with a reasonable time gap and keep the number of parallel requests in control.
User Agent Rotation :
A User-Agent String in the request header helps identify which browser is being used, what version, and on which operating system. Every request made from a web browser contains a user-agent header and using the same user-agent consistently leads to the detection of a bot. User Agent rotation and spoofing is the best solution for this.
Disguise your requests by rotating IPs and Proxy Services :
This we’ve discussed in challenges topic. It’s always better to use rotating IPs and proxy service so that your spider won’t get blocked in the near future. Get more on How to Use HTTP Proxy with Request Module in Python.
Scrape during off-peak hours:
To make sure that a website isn’t slowed down due to high traffic accounting to humans as well as bots, it is better to schedule your web-crawling tasks to run in the off-peak hours. The off-peak hours of the site can be determined by the geo-location of where the site’s traffic is from. By web scraping during the off-peak hours, you can avoid any possible load you might put on the server during peak hours. This will also help in significantly improving the speed of the scraping process.
Still are you facing problem while scraping then once visit our Python web scraping tutorials and download python script or get insight about data from previously scraped sample data of various data scraping services.