All competitors do that yet not everyone talks about that. We are going to tell about parsing in detail further: what it is, why and for whom the data is parsed, how it is technically implemented, what problems may arise and whether there are any pitfalls. Most importantly, we will discuss how to parse data correctly to make it useful and safe.
Let's start with definitions.
What Is Data Parsing?
Parsing definition implies the process of automated info collection for further conversion and structuring. Parsing can be used, for example, when you need to consolidate disparate information about prices or contacts from different places and sources into a single database. This makes the analysis faster and more convenient.
A parser is a special program or automated script that performs the process of data collection and processing.
Parsers can work with various data sources like local file copies, spreadsheet files and text formats as well as contact storage software etc.
There are parsers capable of recognizing text on images, bypassing bot protection measures (such as CAPTCHA, IP blocking etc.), emulating user behavior, logging into personal accounts etc.
Parsing has gained the utmost popularity in the online environment, where the web page content is analyzed and the required data is entered into special tables for further processing, filtering, cleansing, conversion etc. This process is also frequently referred to as database parsing (i.e., the process of collecting a database for a specific purpose/task).
What Is Parsing Used For?
- Analysis of the structure of your own web pages: creating a list of H1-H3 headings, meta tags (title, description, etc.), generating a sitemap, checking nesting levels, identifying duplicates etc.;
- Error detection and finding correct web page answers, checking the available redirects and their configurations;
- Testing bot/DDoS attacks protection systems;
- Competitor analysis - gathering prices and product stock information, understanding the product assortment, detecting promotions and special offers, collecting positions and keyword inquiries (semantic core), compiling a database of product/category descriptions etc.;
- Collecting contact information for email marketing campaigns;
- Creating and filling aggregator websites with content (link directories, reviews, domain data etc.);
- Monitoring changes on certain pages (such as those within specific forum topics), searching for new product listings etc.;
- Tracking the best prices (for personal or bulk purchases);
- Brand and reputation management in the network: searching for brand mentions, trademarks, specific individuals (e.g., well-known personalities) for on-time response as well as for understanding popularity/rating etc. These are all a part of SERM (Search Engine Reputation Management) techniques;
- Building a semantic core by parsing queries from specialized services like Google Trends;
- Analysis of positions in the search engine results and gathering long-tail key phrases to better understand user interests and evaluate search engine algorithms (for effective SEO optimization);
These are just a few examples - there are multiple parsing applications out there.
For example, parsing is used by many programs that work through an API (Application Programming Interface). The thing is, API responses often provide data in "as is" or "all at once" formats, without separating individual blocks or structures. Even if there is a structure, the server response still needs to be "parsed" on a client side to extract only the most necessary data. A parser is always responsible for the data extraction process. Parsing through an API is typically organized using special XML tags or utilizing the JSON format.
All the mentioned goals and tasks share one common aspect: the need to transform the available data in its original format into another format that is more convenient to work with. These are usually databases or charts. Additionally, you can simultaneously solve tasks to clean the data from unnecessary clutter, ensuring that the database contains the required info only.
How Does a Data Parser Work?
As mentioned above, a parser is a special program that is responsible for retrieving, processing and converting the data if necessary. In other words, it takes the input data, processes it (converts and/or cleans it) and then returns it in a format that is convenient to work with.
Like any other software, parsers can be written in different programming languages. They can be paid, free or distributed on a freemium model (with a trial version). They can also have open or closed source code, being created for various specific tasks etc. Naturally, parsers can work as stand-alone software (installed on client workstations) or use a SaaS or a PaaS model, operating in the cloud on a subscription basis.
Each parser uses its own approaches, algorithms and technical solutions. However, they also share common characteristics as it is difficult to find a unique solution during the parsing process, especially when it comes to web page analysis.
This is how the algorithm of operation for almost any web parser looks like:
- The script accesses a specific web page address (based on a given URL, while the URL list for parsing can be generated automatically based on addresses extracted from the source/starting page). To increase the chances of receiving an automated response, a parser may identify itself as a browser or a search engine bot;
- The script retrieves the web page HTML code after a successful connection;
- The HTML code is then parsed into tags, and the required sections or blocks are detected in the page. For a more comprehensive HTML code analysis, many parsers may have a built-in browser;
- The extracted data is copied into a special internal database (this is typically the SQL format, but other formats can be used as well);
- Information from the internal database can be exported to other formats such as CSV, XML, JSON, YAML etc.;
- The collected data is stored on the local disk or in the cloud storage (depending on the type of program and subscription capabilities).
The basic parsing capabilities can be expanded through plugin or module integration. For example, proxy server lists can be utilized, letting the parser send requests not on its own behalf (not from its IP address) but from an IP proxy. This significantly increases the number of flows and enables parallel processing of a bulk amount of data in less time.
Some parsers can work with APIs. For instance, there are APIs provided by Amazon (read more about tools to scrape data from Amazon) and other popular platforms. Additional modules responsible for separate APIs are used to handle the successful parsing of automated server responses because each website has its own peculiarities.
Parsers can provide data through their own API. This functionality is usually available in cloud services or programs/scripts used to work within a remote server.
Website parsing programs can be either general-purpose, intended for a wide range of tasks, or specialized. Sneaker bots is one of the best samples. These are parsers that exclusively work with branded sneaker websites. Another example of specialized parsing is Netpeak Spider. This is a parser for meta tags and server response codes.
Advantages of Data Parsing
The first and the most significant advantage of data parsing is the automation of the data collection process. The same data can be collected manually, but it is expensive, time-consuming, and potentially prone to numerous errors due to the human factor.
The general advantages of data parsing meaning can be summarized as follows:
- Dealing with a vast amount of data: Parsing allows extracting data from numerous competitor websites, granted that you have such a necessity and sufficient budget/technical capabilities;
- Fully automated operation: Minimal number of errors, only the specified data is collected (either determined by you or predefined by the parser developers). The collection procedures can be either episodic (on demand) or scheduled;
- Data export in a convenient format: Many parsers support exporting data to various file types. With API support, parsers can be integrated with other corporate software and external web services;
- Parallelization of flows: This is possible when proxy list support is available. The more flows work simultaneously, the less time is required for parsing;
- There are specialized parsers available in the market as well as universal solutions for a broad range of tasks;
- The distribution format can be cloud-based and/or stand-alone (to be installed on a PC or a dedicated server). A combination of both is sometimes possible as well;
Problems and Drawbacks of Data Parsing
- Websites, especially large online marketplaces and web services, don’t like parasitic loads and use various measures to combat scraping programs. There is always a risk of being blacklisted (for example, based on IP address or other technical indicators);
- The layout of scraped websites can sometimes change and errors may be evident there. This means that you either need to manually update the parsing script or wait for developers to release an update (if manual configuration is not possible for some reason);
- Most parsers are paid. Most often, subscription models are used instead of one-time purchases. Free versions usually come with limited functionality or work on a trial basis (with time restrictions);
- When using desktop software, extra expenses may be required along with fixed pricing. These may include tasks like automating captcha recognition and renting proxies. Your IP address will quickly end up in a blocklist (or you will have to significantly reduce the request frequency, which will eventually result in a rapid decrease in parsing speed) without a proxy;
- When working with a large number of flows, the parsing program creates a significant load on the processor and consumes other resources like network bandwidth, RAM, and disk space. While the processor may simply heat up under the load, adding disk space is not a quick task. In some cases, it is all about terabytes of data, requiring high-capacity and fast disks or specialized cloud storage;
- Fine-tuning a parser requires good technical knowledge and experience. Solid awareness of HTML markup is a must-have;
- A large number of parallel flows may impose a significant load on hosting infrastructure. In certain situations, parsing may not differ a lot from DDoS attacks.
Is Data Parsing Legal?
Technically, you collect data from publicly available sources. You can also do that manually, but you simply use automation tools (parsing programs) instead.
However, there is a distinction between "seeing" information and recording/storing/using it for your own purposes.
Various legal restrictions may be applied here (these may vary with regard to the country, though):
- It is not allowed to store and transmit copyright-protected content to third parties. Legal protection may apply not only to images and videos but also to text;
- Personal information cannot be collected. This especially applies to parsing email addresses and phone numbers. However, there are more serious violations when customer databases are stolen (parsed);
- Excessive load on hosting is not allowed. Otherwise, legislation governing punishment for causing financial damage (due to increased hosting expenses) and/or restricting access to information (blocking due to a DDoS attack) may be applied;
- User agreements must not be violated. Theoretically, they may include penalties for parsing. This may result in civil lawsuits and legal disputes.
Data parsing is an excellent solution for automating the collection of specific data on the web, be it brand reviews or pricing information from a competitor's catalog. Tasks and objectives may vary, but the technical implementation largely follows the same pattern of sending a request to the server and parsing the HTML code into components. However, parsers can be either general-purpose or specialized. Some may work on a PC or your own server, while others are available as ready-to-use cloud solutions. Each approach has its pros and cons.
Web crawling by search engine bots follows a similar principle. You can learn more about the difference between web crawling and web scraping.
If advanced performance and a large number of parallel flows matter to you most, proxies are a must.
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