Parsing Tool

Custom Parsing Tool | InsightIDR Documentation – Docs …

The Custom Parsing Tool gives you the ability to create custom parsing rules to extract log data that is most relevant to your organizational needs. With the Custom Parsing Tool, you can:Parse logs in a format that is unknown to InsightIDR, which allows you to pull and monitor data that is not automatically extracted by InsightIDR. Further parse log entries to include additional fields that are not parsed by example, if you are using an electronic health record (EHR) tool, you may want to parse out patient IDs, login successes and failures, and EHR event types. With this data, you can create operational dashboards that track the data that is uniquely important to your business ing custom parsing rules is simple. The Custom Parser provides an interface you can use to show InsightIDR exactly what you want to extract from the logs. Based on your input, it auto-generates the patterns needed to extract the to Know about Custom Parsing RulesAs you are building a custom parser, here are some things to keep in mind:Once your parsing rule is created, you can expect a 5-10 minute delay before your parsed data shows up in Log eviously collected data will not be parsed with your new parsing rules. Only data collected after the parsing rules are implemented will be parsed with rsing rules can impact current dashboards. You will need to check your filters to assess how the parsing rules will impact your dashboards and to a parsing rule are not applied retroactively. Older logs will not be updated. It may take up to 10 minutes for your edited rule to be applied to new that is parsed with a custom parsing rule appears in Log Search with a “custom_data” tag in the log line. If your data does not appear in Log Search, it could indicate an issue with your parsing rule. For more details, see the Troubleshooting complex log entries, the Parsing Tool works best if you create more than one parsing rule for the same set of entries. For example, if you need to parse log entries with many different types of entries. Or cases when log entries have many Custom Parsers WorkCustom parsers apply to raw events, which are lines of text collected by an event source. Your custom parsing rule will parse raw events when it is uploaded to the Insight Cloud. To build your rule, you define the values extracted from a log line and the values you want to map them to. With the custom parsing tool, you can also normalize the structure of your logs, making it easier to find the fields you want to to build a custom parsing rule:Task 1: Name your ruleTask 2: Select a logTask 3: Create a filterTask 4: Extract Fields from the logTask 5: Bulk Apply RulesBuild a Custom Parsing RuleTo build your rule, define the values extracted from a log line and the values you want to map them get started:Launch the Custom Parsing Tool. From the left menu, go to Log Search, and select Custom Data Parsing > Create Custom Parsing you are in the Custom Parsing Tool, complete tasks 1-5 to build your custom 1: Name your ruleYou should use a name that is unique and also descriptive so that you can easily find the rule later. For example, if you want to create parsing rules for your firewall logs, you could enter, “Firewall Log 1″ add a name:Enter a unique Next: Select 2: Select a logThis task involves two steps: selecting the log and setting the time range. The time range allows you to preview a specific range of logs that have the fields you want to parse. Log lines are displayed from oldest to newest, so it’s important to select a time range that will generate the most relevant sampling of data. In Task 4, you will extract fields from these log select a log:Under Step 1: Log select the log that you want to extract fields Step 2: Sample Log Lines select a time range. If you don’t see any sample data, you may need to increase the time range you’ve Next: Create a 3: Create a filterNext, let’s determine whether you need to create a filter. To do this, review the structure of your log lines: if your log streams have multiple formats for the log events, complete this step to ensure that your custom parsing rule applies to only the relevant logs. However, if your log lines are uniformly formatted, you can skip this step and proceed to Task 4: Extract lters added to your custom parsing rule are applied to incoming logs before they are parsed. Say for example, you have an event source that sends DNS, VPN, and firewall events in a single log line. You can create a filter that focuses on firewall events, giving you increased visibility into malicious activity happening along that create a filter:Enter the values that you want to include as part of your Apply. This filter will be applied to any data before it is parsed by this ruleClick Next: Extract 4: Extract Fields from the logIn this task, choose what data you would like to extract from the log using guided mode, or manually extract fields with the Regex Editor. Guided mode is the default mode for new parsing modeTo extract fields:Highlight the data you want to extract from the sample log. The Custom Parsing tool will automatically highlight the matching data in the other log lines. When extracting data, we recommend that you not include brackets or quotation marks, as this may make it more difficult for you to search for your fields you are satisfied with the selected data, click your field, and click Add steps 1-3 until you have extracted all the fields you want to include in your rule. You may also switch to the Regex Editor to further define your parsing rule using regular Next: Bulk Apply EditorUse the Regex Editor to manually extract fields with regular expressions. You can use the editor to complete the entire field extraction process, or you can begin in guided mode and use the Regex Editor to fine tune your extracted you begin, note the following:The Regex Editor only supports RE2 regular expressions. At this time, unnamed capture groups are not that are extracted in guided mode will display as auto-generated regular expressions in the Regex Editor, and may vary in format from human-written regular names cannot contain numbers, special characters, or changes you make in the Regex Editor will be permanently lost if you revert back to the guided extract fields:From the Field Extraction step, click Open in Regex define the fields you want to extract, type the regular expressions in the text ready, click Apply. Your newly extracted fields will display in the summary section to the left of the editor, under Extract Fields. Within the Sample Log Preview, you’ll also see a highlight over the part of the log line being extracted by your Next: Bulk Apply modified with the Regex Editor cannot be revertedIf you modify or create a parsing rule with the Regex Editor and then save it, you cannot revert back to guided mode. If you want to edit a parsing rule that was modified or created with the Regex Editor, you must use the Regex Editor to do 5: Bulk Apply RulesYou can apply your new custom parsing rules to other logs. To do so, select your logs from the list of suggested must choose logs that are identical to the format of the parsing rule that you created. For example, if your sample logs are firewall logs, you can select matching firewall logs from this Custom Parsing RulesEdit a Parsing RuleYou can edit your rule to account for changes in log format, to add additional fields, or to refine your existing field selection. Note that edits are not retroactively applied to existing logs. It may take up to 10 minutes for changes to be applied to new edit a parsing rule:Go to Log Search, select Custom Data Parsing in the top right, and click Manage Parsing the Manage Parsing Rules page, find the rule you want to update, and click the Edit All Parsing RulesFrom the left menu, go to Log Search and choose Custom Data Parsing > Manage Parsing Rules. The Manage Parsing Rules table displays all of the parsing Extracted Fields for a Parsing RuleFrom the left menu, go to Log Search and choose Custom Data Parsing > Manage Parsing Rules. The Manage Parsing Rules table displays all of the parsing Extracted Fields column shows the extracted fields for each parsing Logs for a Parsing RuleFrom the left menu, go to Log Search and choose Custom Data Parsing > Manage Parsing Rules. The Manage Parsing Rules table displays all of the parsing Logs column shows the logs that the parsing rule is applied a Parsing RuleDeleting a parsing rule may affect the dashboards, queries, and custom alerts that use its data. Please review your queries. After you delete a parsing rule, you cannot revert the that once you delete your parsing rule, new incoming data will be unparsed. Previously parsed data will remain in a parsed delete a parsing rule:From the left menu, go to Log Search and choose Custom Data Parsing > Manage Parsing Rules. The Manage Parsing Rules table displays all of the parsing the parsing rule you want to delete and click the Delete oubleshoot your Custom Parsing RulesHere are some common scenarios and how to solve parser stopped parsing my logsIf the Custom Parsing Tool is no longer parsing your logs, the most likely reason is that the incoming logs no longer match the parsing rule (s) that you created, usually because a change made to the sending device has changed the format of the logs. Although you may not be aware of the device being changed, the vendor may have changed the format during a product upgrade or an admin might have changed the logging configuration. To fix this, you should create new parsing rule (s) that match the current logs. Depending on the log data, you may also wish to delete the old parsing parsing rule worked when I was testing it in the Custom Parsing Tool, but after I saved the rule I do not see any logs being parsedYou may be trying to extract too many fields with one parsing rule. Instead of using one parsing rule to extract many fields, try creating several parsing rules Regex Editor is able to parse out one or two fields from my logs, but when I try to add additional fields for parsing, the logs do not parse as expectedYou may need to create multiple rules to parse your particular logs. Create parsing rules to extract a smaller number of fields per rule rather than trying to create one rule that parses out all of the fields at once.
What Is Parsing of Data? - Blog | Oxylabs

What Is Parsing of Data? – Blog | Oxylabs

If you work with development (whether part of the team or work in a company where you need to communicate with the tech team often), you’ll most likely come across the term data parsing. Simply put, it’s a process when one data format is transformed into another, more readable data format. But that’s a rather straightforward explanation.
In this article we’ll dig a little deeper on what is parsing of data, and discuss whether building an in-house data parser is more beneficial to a business, or is it better to buy a data extraction solution that already does the parsing for you.
What is data parsing?
Data parsing is a widely used method for data structuring; thus, you may discover many different descriptions while trying to find out what exactly it is. To make understanding this concept easier, we’ve put it into a simple definition.
What is data parsing? Data parsing is a method where one string of data gets converted into a different type of data. So let’s say you receive your data in raw HTML, a parser will take the said HTML and transform it into a more readable data format that can be easily read and understood.
What does a parser do?
A well-made parser will distinguish which information of the HTML string is needed, and in accordance to the parsers pre-written code and rules, it will pick out the necessary information and convert it into JSON, CSV or a table, for example.
It’s important to mention that a parser itself is not tied to a data format. It’s a tool that converts one data format into another, how it converts it and into what depends on how the parser was built.
Parsers are used for many technologies, including:
Java and other programming languagesHTML and XMLInteractive data language and object definition languageSQL and other database languagesModeling languagesScripting languagesHTTP and other internet protocols
To build or to buy?
Now, when it comes to the business side of things, an excellent question to ask yourself is, “Should my tech team build their own parser, or should we simply outsource? ”
As a rule of thumb, it’s usually cheaper to build your own, rather than to buy a premade tool. However, this isn’t an easy question to answer, and a lot more things should be taken into consideration when deciding to build or to buy.
Let’s look into the possibilities and outcomes with both options.
Building a data parser
Let’s say you decide to build your own parser. There are a few distinct benefits if making this decision:
A parser can be anything you like. It can be tailor-made for any work (parsing) you require. It’s usually cheaper to build your own ’re in control whatever decisions need to be made when updating and maintaining your parser.
But, like with anything, there’s always a downside of building your own parser:
You’ll need to hire and train a whole in-house team to build the intaining the parser is necessary – meaning more in house expenses and time resources ’ll need to buy and build a server that will be fast enough to parse your data in the speed you in control isn’t necessarily easy or beneficial – you’ll need to work closely with the tech team to make the right decisions to create something good, spending a lot of your time planning and testing.
Building your own has its benefits – but it takes a lot of your resources and time. Especially if you need to develop a sophisticated parser for parsing large volumes. That will require more maintenance and human resources, and valuable human resources because building one will require a highly-skilled developer team.
Buying a data parser
So what about buying a tool that parses your data for you? Let’s start with the benefits:
You won’t need to spend any money on human resources, as everything will be done for you, including maintaining the parser and the issues that arise will be solved a lot faster, as the people you buy your tools from have extensive know-how and are familiarized with their technology. It’s also less likely that the parser will crash or experience issues in general, as it will be tested and perfected to fit the markets’ requirements. You’ll save a lot on human resources and your own time, as the decision making on how to build the best parser will come from the outsourcing.
Of course, there are a few downsides to buying a parser as well:
It will be slightly more won’t have too much control over it.
Now, it seems that there are a lot of benefits to simply just buy one. But one thing that might make things easier to choose is to consider what sort of parser you’ll need. An expert developer can make an easy parser probably within a week. But if it’s a complex one, it can take months – that’s a lot of time, and resources.
It also falls to whether you’re a big business that has a lot of time and resources on their hands to build and maintain a parser. Or you’re a smaller business that needs to get things done to be able to grow within the market.
How we do it: Real-Time Crawler
Here at Oxylabs, we have a data gathering tool called Real-Time Crawler. This product is specifically built to scrape search engines and e-commerce websites on a large scale. We covered what Real-Time Crawler is and how it works in great detail in one of our articles, so make sure to check it out. Also, here’s a video below:
But why are we bringing up this tool? Well, Real-Time Crawler not only gathers the data – it also has a built-in parser that turns your HTML into JSON. If you choose to use Real-Time Crawler Callback method, after every job request, you’ll be provided with a URL to download the results in HTML or parsed JSON format.
Our built-in parser handles quite a lot of data daily. On February, 12 billion requests were made! And that’s back in February! Based on our 2019, Q1 statistics, the total requests grew by 7. 02% in comparison to Q4 2018. And these numbers continue to rise in accordance in Q2, 2019.
Our tech team has been working with this project for a few years now, and having this much experience we can say with confidence that the parser we built can handle any volume of data one might request.
So – to build or to buy? Well, building several years of experience, improvements, and maintenance of a tool that does its job to perfection – honestly, quite expensive.
Wrapping up
Hopefully, now you have a decent understanding of what is parsing of data. Taking everything into account, keep in mind whether you’re building a very sophisticated parser or not. If you are parsing large volumes of data, you will need good developers on your team to develop and maintain the parser. But, if you need a less complicated, smaller parser – probably best to build your own.
Also be mindful if you are a large company with a lot of resources, or a smaller one, that needs the right tools to keep things growing.
Oxylabs’ clients have significantly increased growth with Real-Time Crawler! If you are also looking for ways to improve your business, register here to start using our tools. Also, if you have more questions about data parsing, book a call with our sales team!
People also ask
What tools are required for data parsing?
After web scraping tools provide the required data, there are several options for data parsing. BeautifulSoup and LXML are two commonly used data parsing tools.
How to use a data parser?
Every data parsing tool will come with its own manual. Most of them will require some technical knowledge such as understanding Python and data from a web scraper.
What is data scraping?
Data scraping is the process of acquiring large amounts of data from the web through the use of automation and rotating IP address.
Gabija Fatenaite is a Product Marketing Manager at Oxylabs. Having grown up on video games and the internet, she grew to find the tech side of things more and more interesting over the years. So if you ever find yourself wanting to learn more about proxies (or video games), feel free to contact her – she’ll be more than happy to answer you.
All information on Oxylabs Blog is provided on an “as is” basis and for informational purposes only. We make no representation and disclaim all liability with respect to your use of any information contained on Oxylabs Blog or any third-party websites that may be linked therein. Before engaging in scraping activities of any kind you should consult your legal advisors and carefully read the particular website’s terms of service or receive a scraping license.
What is data parsing? - ScrapingBee

What is data parsing? – ScrapingBee

07 June, 2021
10 min read
Kevin worked in the web scraping industry for 10 years before co-founding ScrapingBee. He is also the author of the Java Web Scraping Handbook.
Data parsing is the process of taking data in one format and transforming it to another format. You’ll find parsers used everywhere. They are commonly used in compilers when we need to parse computer code and generate machine code.
This happens all the time when developers write code that gets run on hardware. Parsers are also present in SQL engines. SQL engines parse a SQL query, execute it, and return the results.
In the case of web scraping, this usually happens after data has been extracted from a web page via web scraping. Once you’ve scraped data from the web, the next step is making it more readable and better for analysis so that your team can use the results effectively.
A good data parser isn’t constrained to particular formats. You should be able to input any data type and output a different data type. This could mean transforming raw HTML into a JSON object or they might take data scraped from JavaScript rendered pages and change that into a comprehensive CSV file.
Parsers are heavily used in web scraping because the raw HTML we receive isn’t easy to make sense of. We need the data changed into a format that’s interpretable by a person. That might mean generating reports from HTML strings or creating tables to show the most relevant information.
Even though there are multiple uses for parsers, the focus of this blog post will be about data parsing for web scraping because it’s an online activity that thousands of people handle every day.
How to build a data parser
Regardless of what type of data parser you choose, a good parser will figure out what information from an HTML string is useful and based on pre-defined rules. There are usually two steps to the parsing process, lexical analysis and syntactic analysis.
Lexical analysis is the first step in data parsing. It basically creates tokens from a sequence of characters that come into the parser as a string of unstructured data, like HTML. The parser makes the tokens by using lexical units like keywords and delimiters. It also ignores irrelevant information like whitespaces and comments.
After the parser has separated the data between lexical units and the irrelevant information, it discards all of the irrelevant information and passes the relevant information to the next step.
The next part of the data parsing process is syntactic analysis. This is where parse tree building happens. The parser takes the relevant tokens from the lexical analysis step and arranges them into a tree. Any further irrelevant tokens, like semicolons and curly braces, are added to the nesting structure of the tree.
Once the parse tree is finished, then you’re left with relevant information in a structured format that can be saved in any file type. There are several different ways to build a data parser, from creating one programmatically to using existing tools. It depends on your business needs, how much time you have, what your budget is, and a few other factors.
To get started, let’s take a look at HTML parsing libraries.
HTML parsing libraries
HTML parsing libraries are great for adding automation to your web scraping flow. You can connect many of these libraries to your web scraper via API calls and parse data as you receive it.
Here are a few popular HTML parsing libraries:
Scrapy or BeautifulSoup
These are libraries written in Python. BeautifulSoup is a Python library for pulling data out of HTML and XML files. Scrapy is a data parser that can also be used for web scraping. When it comes to web scraping with Python, there are a lot of options available and it depends on how hands-on you want to be.
If you’re used to working with Javascript, Cheerio is a good option. It parses markup and provides an API for manipulating the resulting data structure. You could also use Puppeteer. This can be used to generate screenshots and PDFs of specific pages that can be saved and further parsed with other tools. There are many other JavaScript-based web scrapers and web parsers.
For those that work primarily with Java, there are options for you as well. JSoup is one option. It allows you to work with real-world HTML through its API for fetching URLs and extracting and manipulating data. It acts as both a web scraper and a web parser. It can be challenging to find other Java options that are open-source, but it’s definitely worth a look.
There’s an option for Ruby as well. Take a look at Nokogiri. It allows you to work with HTML and HTML with Ruby. It has an API similar to the other packages in other languages that lets you query the data you’ve retrieved from web scraping. It adds an extra layer of security because it treats all documents as untrusted by default. Data parsing in Ruby can be tricky as it can be harder to find gems you can work with.
Regular expression
Now that you have an idea of what libraries are available for your web scraping and data parsing needs, let’s address a common issue with HTML parsing, regular expressions. Sometimes data isn’t well-formatted inside of an HTML tag and we need to use regular expressions to extract the data we need.
You can build regular expressions to get exactly what you need from difficult data. Tools like regex101 can be an easy way to test out whether you’re targeting the correct data or not. For example, you might want to get your data specifically from all of the paragraph tags on a web page. That regular expression might look something like this:

(. *)<\/p>/
The syntax for regular expressions changes slightly depending on which programming language you’re working with. Most of the time, if you’re working with one of the libraries we listed above or something similar, you won’t have to worry about generating regular expressions.
If you aren’t interested in using one of those libraries, you might consider building your own parser. This can be challenging, but potentially worth the effort if you’re working with extremely complex data structures.
Building your own parser
When you need full control over how your data is parsed, building your own tool can be a powerful option. Here are a few things to consider before building your own parser.
A custom parser can be written in any programming language you like. You can make it compatible with other tools you’re using, like a web crawler or web scraper, without worrying about integration issues.
In some cases, it might be cost-effective to build your own tool. If you already have a team of developers in-house, it might not too big of a task for them to accomplish.
You have granular control over everything. If you want to target specific tags or keywords, you can do that. Any time you have an update to your strategy, you won’t have many problems with updating your data parser.
Although on the other hand, there are a few challenges that come with building your own parser.
The HTML of pages is constantly changing. This could become a maintenance issue for your developers. Unless you foresee your parsing tool becoming of huge importance to your business, taking that time from product development might not be effective.
It can be costly to build and maintain your own data parser. If you don’t have a developer team, contracting the work is an option but that could lead to step bills based on developers’ hourly rates. There’s also the cost of ramping up developers that are new to the project as they figure out how things work.
You will also need to buy, build, and maintain a server to host your custom parser on. It has to be fast enough to handle all of the data that you send through it or else you might run into issues with parsing data consistently. You’ll also have to make sure that server stays secure since you might be parsing sensitive data.
Having this level of control can be nice if data parsing is a big part of your business, otherwise, it could add more complexity than is necessary. There are plenty of reasons for wanting a custom parser, just make sure that it’s worth the investment over using an existing tool.
Parsing meta data
There’s also another way to parse web data through a website’s schema. Web schema standards are managed by, a community that promotes schema for structured data on the web. Web schema is used to help search engines understand information on web pages and provide better results.
There are many practical reasons people want to parse schema metadata. For example, companies might want to parse schema for an e-commerce product to find updated prices or descriptions. Journalists could parse certain web pages to get information for their news articles. There are also website that might aggregate data like recipes, how-to guides, and technical articles.
Schema comes in different formats. You’ll hear about JSON-LD, RDFa, and Microdata schema. These are the formats you’ll likely be parsing.
JSON-LD is JavaScript Object Notation for Linked Data. This is made of multi-dimensional arrays. It’s implemented using the standards in terms of SEO. JSON-LD is generally more simple to implement because you can paste the markup directly in an HTML document.
RDFa (Resource Description Framework in Attributes) is recommended by the World Wide Web Consortium (W3C). It’s used to embed RDF statements in XML and HTML. One big difference between this and the other schema types is that RDFa only defines the metasyntax for semantic tagging.
Microdata is a WHATWG HTML specification that’s used to nest metadata inside existing content on web pages. Microdata standards allow developers to design a custom vocabulary or use others like
All of these schema types are easily parsable with a number of tools across different languages. There’s a library from ScrapingHub, another from RDFLib.
We’ve covered a number of existing tools, but there are other great services available. For example, the ScrapingBee Google Search API. This tool allows you to scrape search results in real-time without worrying about server uptime or code maintainance. You only need an API key and a search query to start scraping and parsing web data.
There are many other web scraping tools, like JSoup, Puppeteer, Cheerio, or BeautifulSoup.
A few benefits of purchasing a web parser include:
Using an existing tool is low maintenance.
You don’t have to invest a lot of time with development and configurations.
You’ll have access to support that’s trained specifically to use and troubleshoot that particular tool.
Some of the downsides of purchasing a web parser include:
You won’t have granular control over everything the way your parser handles data. Although you will have some options to choose from.
It could be an expensive upfront cost.
Handling server issues will not be something you need to worry about.
Final thoughts
Parsing data is a common task handling everything from market research to gathering data for machine learning processes. Once you’ve collected your data using a mixture of web crawling and web scraping, it will likely be in an unstructured format. This makes it hard to get insightful meaning from it.
Using a parser will help you transform this data into any format you want whether it’s JSON or CSV or any data store. You could build your own parser to morph the data into a highly specified format or you could use an existing tool to get your data quickly. Choose the option that will benefit your business the most.

Frequently Asked Questions about parsing tool

What is a parsing tool?

It’s a tool that converts one data format into another, how it converts it and into what depends on how the parser was built. Parsers are used for many technologies, including: Java and other programming languages. … Interactive data language and object definition language. SQL and other database languages.Sep 13, 2021

What is the parse tool in alteryx?

This tool takes text in one column and splits it into separate, multiple columns (or rows).

What does parsing data mean?

Data parsing is the process of taking data in one format and transforming it to another format. … They are commonly used in compilers when we need to parse computer code and generate machine code. This happens all the time when developers write code that gets run on hardware. Parsers are also present in SQL engines.Jun 7, 2021

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