Parsing (or scraping) is an automated collection, processing, and analysis of a large amount of data from different sources. The main purpose of parsing is to get large amounts of data in a short time. The spread of the Internet and the widespread use of web technologies in all areas of business have led to the appearance of large amounts of data in the open access, the analysis of which allows you to make more accurate forecasts and make effective decisions for the development of certain projects.
Specialized software – parsers-is used to implement this process. Their other name is "search bots" or "web spiders". Depending on the specifics of the task, universal parsers can be used, which can be found on the Internet, or special versions of them can be developed for non-trivial tasks.
More about parsers and parsing
There are usually three main steps in the process of parsing data: getting access and loading data, processing it to extract the necessary information, saving the received information in a convenient format for further use. All these steps are implemented programmatically inside the parser.
The work of parsers is divided into four stages.
The first step is to scan the target web pages. The parser sends many HTTP requests to the desired pages, saving the received responses. In this case, the list of URL pages is either set in advance, or formed during the scanning process according to the specified algorithm.
The second stage is the most important and technically difficult. It consists in implementing algorithms for analyzing and selecting the desired content from the array of data obtained at the first stage.
There are several approaches to solving the problem of selecting the necessary data from downloaded web pages. They differ in the complexity and used depending on the specifics of a task.
The most common approaches to developing such algorithms are:
- using regular expressions
- analysis of the tree structure of HTML templates
- the loading of pages with the help of browsers automated control
- application of machine learning technologies
The third step is to bring the useful data that has already been extracted into a convenient form. At this stage, the data is cleared of unnecessary elements, clustered, if necessary, further modified and formatted.
The fourth step is to save the data in the required format. In the simplest case, the data can be saved to a text document or a spreadsheet. But for the most part, the array is serialized according to a certain model and stored in the database.
Each of these stages does not have to be pronounced. As part of the parser, one module can perform several functions at once, such as formatting and saving data in the desired format.
Scope of data parsing
When talking about parsing, collecting data from web pages on the Web is usually implied. Most often, we are talking about getting a large amount of data about products offered on the market, their prices and assortment. In this case, data parsing basically involves collecting information as a cyclical of processes for the purpose of continuous monitoring of the market over time.
Businesses need very different data. Most often, parsers collect the following types of content:
- Types of products and their prices on trading platforms
- Content for filling websites: texts, pictures, videos, etc.
- Users personal data: login, email, phone, and others
- Reviews, comments, and social media posts
- Results of athletes performance and sports betting
- Classified advertising services
As you can see, data parsing is ultimately universal. Collecting competitors data, reviewing the market status of the product you are interested in, and getting content for direct use or processing - all this can be useful for projects in any field.
Most often, data parsing is used by SEO specialists, but its scope is growing every day. Perhaps, in the near future, it will be impossible to imagine business development in most industries without parsing.
But why do you need proxies for parsing?
You see, the parsing of the data creates unpleasant consequences for websites. If the amount of collecting data is huge and the parser has to send a large number of requests, this creates unnecessary load on web servers, which is surely not welcome. Another unpleasant point is that copying content created by another person is not always fair.
All this leads to the fact that big Internet resources are trying to protect themselves from parsing, or at least prevent them from doing it in large volumes. There are various ways to protect yourself against parsing.
|
Type of protection |
Description of the type of protection |
|
Establishing the boundaries of access |
Hiding the website structure data from ordinary visitors. Access to the full functionality is granted only to authorized users and administrators. |
|
Blacklists |
Creating blacklists that include the IP addresses of users suspected of automated data collection. |
|
Restricting requests |
Sets the minimum time interval between requests to a value. Because parsers send a large number of requests per unit of time, this will significantly slow down their work. |
|
Protection from robots |
Such methods are actively used on the Internet to protect against any automated loads on the servers, whether it's parsing, mass posting or mass account creation in social media. The most well-known method of protection is ReCAPTCHA. |
The most effective way to overcome this barrier is to use proxy servers.
To implement the main security methods, the website needs to identify a client who sends a request. User identification is performed using various data obtained when setting up an HTTP connection: IP address, DNS server address, fingerprint, and others.
Proxies can hide real user data and replace it with fake one. A proxy server is an intermediary between your device and the target resource, which makes it possible to send multiple requests without getting blacklisted or restricted.
Comments