While writing a full-blown compiler for a programming language is a difficult and frustrating task, writing a smaller and more specific parser can be surprisingly easy if you know a small trick.
On the other hand, parsing problems pops up at several places in modern-day programming. So, learning this useful trick can be rewarding.
You need to know the basics of Python, more specifically, you should know the concepts of recursion and flow of control.
Objectives
After reading and understanding this post, you will be able to create simple calculators, interactive interpreters, parsers, very limited and small programming languages, etc. In general, you should be able to take input, tokenize it, perform whatever actions you want to on the tokens, and output the result of the process.At the end of this post, you will have created a simple, Lisp-like prefix calculator. Following is a demonstration of how it's going to look:> ( + 3 2 )= 5> ( / 2 0 )DivisionByZero> ( - -3 2 )= -5> -2= -2> ( + ( * 3 2 ) 5 )= 11
Step 1: Writing the Grammar
The first step to writing a parser is to write a clear grammar for its syntax. The grammar determines what is and what is not right. Once you have written the grammar, translating it to Python code is a trivial chore. The grammar will serve as our first pseudocode.For our tiny calculator, we know that the input can come in two forms: a Number (-2, .5, +8, 8.5, 9.) or a more complicated Expression begins with a (, followed by an operator, etc.).For writing a grammar, we need to identify different elements of the syntax. So far, we have Expression, Number, and Operator. The next important thing to do is to structure the elements (known as terms) into a hierarchical form. This is shown below:Expression:Number( Operator Expression Expression )Number:a floating-point number ([-+][0-9][*\.][0-9]*)Operators:
+
-
*
/
You will notice that Operator and Expression have no parent; they are independent terms.A grammar is read from the bottom up and different choices appear on distinct lines. Our grammar says that:
an Operator is one of +, -, *, /.
a Number is a floating-point number which matches the RegEx [-+][0-9]*[\.][0-9]*
an Expression is either a Number or a ( followed by an Operator, followed by two other Expressions, and finally ends in a ). Note that the definition of an Expression is recursive.
Step 2: Translating the Grammar into Pseudocode
Pseudocode is fake code resembling English which is supposed to be an intermediate code that can easily be converted into real code. Although writing pseudocode is optional, it is really helpful.The trick here is to put each term from our grammar into a separate function. Whenever we need to apply the grammar of a certain term, we only have to call the function. Following is the pseudocode implementing the grammar above:https://gist.github.com/HackerEarthBlog/f0a5a4304326936142da39b0d853f944This is our rough pseudo-code that should be good enough for our purpose. In the next step, we will write the real code.
3. Writing the Code
It is said very profoundly about Python that reading and writing Python feels like doing pseudocode. The same applies here, but there is one small caveat— Python doesn't provide any function for “unreading” or putting a character back in the input buffer.For this, I have created a small class which extends the file object to include this feature. To keep things simple, I have avoided inheritance and my class is not compatible with the file object provided by Python. Treat it like a black-box if you don't want to understand it.https://gist.github.com/HackerEarthBlog/6465f93e1ca155ded5e8b0c8294f16baHere is the buffer.py file which handles buffered input:https://gist.github.com/HackerEarthBlog/5330e5f11f96a22608b45affa61fa858
Explanation
expression():
expression() is our top-level function and maps the Expression grammar term. We first ignore all the whitespace. After that, it takes a single non-whitespace character as input and checks it against several possibilities.If the input string starts with +, -, ., or a digit, it is a number. We put the character back and input the entire number.If the input string starts with (, a complete expression is to follow. We input the operator, two more expressions which will serve as the operands, and finally the closing parenthesis. We then calculate the result and return it.
number():
The number function maps the Number grammar term and is very simple—just a wrapper around getword. We input a whole word and if it converts to a float, we return it, otherwise the function returns an error message.
operator():
The operator function inputs a single character and tests it for equality against several known operators. Like the above two functions, it also maps a grammar term, i.e., Operator. In case the given operator is not valid, an error message is returned.
calc():
The calc function is actually not necessary but makes the code substantially better. In an ideal program, each function should do only one logical task. calc removes some burden from expression.
UngetableInput
Although Python 3 supports buffered input through stdin.buffer, Python 2 has no such facility. Plus, Python 3's stdin.buffer would still require us to create some wrapper of our own.The UngetableInput class wraps Python's basic input to go through a buffer. We take input into the buffer and put a character back into the buffer when ungetc is called. Unless the buffer is empty, all input comes from the buffer.
Homework
This code works and leaves a lot of cleaning as homework for the reader. :) Following is a list of things you can do to improve and extend the rudimentary calculator:Improve buffer.py to handle input whitespace more accurately. Hint: You might want to use a string as the buffer.Implement a function to get a single character while skipping all whitespace and replace the whitespace skipping loop with it.Add the ability to create variables. Following the Lisp syntax, it should look something like the following:( define var_name 839457.892 )
What's Coming Next?
One of the most important parts of our program is the input buffer we created. Unfortunately, it's not general purpose and can break when used in something more complicated than our tiny calculator program. In the next article, we will examine a bigger module which does this chore better.
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A New Era of CodeVibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today,...
A New Era of Code
Vibe coding is a new method of using natural language prompts and AI tools to generate code. I have seen firsthand that this change makes software more accessible to everyone. In the past, being able to produce functional code was a strong advantage for developers. Today, when code is produced quickly through AI, the true value lies in designing, refining, and optimizing systems. Our role now goes beyond writing code; we must also ensure that our systems remain efficient and reliable.
From Machine Language to Natural Language
I recall the early days when every line of code was written manually. We progressed from machine language to high-level programming, and now we are beginning to interact with our tools using natural language. This development does not only increase speed but also changes how we approach problem solving. Product managers can now create working demos in hours instead of weeks, and founders have a clearer way of pitching their ideas with functional prototypes. It is important for us to rethink our role as developers and focus on architecture and system design rather than simply on typing code.
The Promise and the Pitfalls
I have experienced both sides of vibe coding. In cases where the goal was to build a quick prototype or a simple internal tool, AI-generated code provided impressive results. Teams have been able to test new ideas and validate concepts much faster. However, when it comes to more complex systems that require careful planning and attention to detail, the output from AI can be problematic. I have seen situations where AI produces large volumes of code that become difficult to manage without significant human intervention.
AI-powered coding tools like GitHub Copilot and AWS’s Q Developer have demonstrated significant productivity gains. For instance, at the National Australia Bank, it’s reported that half of the production code is generated by Q Developer, allowing developers to focus on higher-level problem-solving . Similarly, platforms like Lovable enable non-coders to build viable tech businesses using natural language prompts, contributing to a shift where AI-generated code reduces the need for large engineering teams. However, there are challenges. AI-generated code can sometimes be verbose or lack the architectural discipline required for complex systems. While AI can rapidly produce prototypes or simple utilities, building large-scale systems still necessitates experienced engineers to refine and optimize the code.
The Economic Impact
The democratization of code generation is altering the economic landscape of software development. As AI tools become more prevalent, the value of average coding skills may diminish, potentially affecting salaries for entry-level positions. Conversely, developers who excel in system design, architecture, and optimization are likely to see increased demand and compensation. Seizing the Opportunity
Vibe coding is most beneficial in areas such as rapid prototyping and building simple applications or internal tools. It frees up valuable time that we can then invest in higher-level tasks such as system architecture, security, and user experience. When used in the right context, AI becomes a helpful partner that accelerates the development process without replacing the need for skilled engineers.
This is revolutionizing our craft, much like the shift from machine language to assembly to high-level languages did in the past. AI can churn out code at lightning speed, but remember, “Any fool can write code that a computer can understand. Good programmers write code that humans can understand.” Use AI for rapid prototyping, but it’s your expertise that transforms raw output into robust, scalable software. By honing our skills in design and architecture, we ensure our work remains impactful and enduring. Let’s continue to learn, adapt, and build software that stands the test of time.
Ready to streamline your recruitment process? Get a free demo to explore cutting-edge solutions and resources for your hiring needs.
What is Systems Design?Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.Systems Design, in its...
What is Systems Design?
Systems Design is an all encompassing term which encapsulates both frontend and backend components harmonized to define the overall architecture of a product.
Designing robust and scalable systems requires a deep understanding of application, architecture and their underlying components like networks, data, interfaces and modules.
Systems Design, in its essence, is a blueprint of how software and applications should work to meet specific goals. The multi-dimensional nature of this discipline makes it open-ended – as there is no single one-size-fits-all solution to a system design problem.
What is a System Design Interview?
Conducting a System Design interview requires recruiters to take an unconventional approach and look beyond right or wrong answers. Recruiters should aim for evaluating a candidate’s ‘systemic thinking’ skills across three key aspects:
How they navigate technical complexity and navigate uncertainty How they meet expectations of scale, security and speed How they focus on the bigger picture without losing sight of details
This assessment of the end-to-end thought process and a holistic approach to problem-solving is what the interview should focus on.
What are some common topics for a System Design Interview
System design interview questions are free-form and exploratory in nature where there is no right or best answer to a specific problem statement. Here are some common questions:
How would you approach the design of a social media app or video app?
What are some ways to design a search engine or a ticketing system?
How would you design an API for a payment gateway?
What are some trade-offs and constraints you will consider while designing systems?
What is your rationale for taking a particular approach to problem solving?
Usually, interviewers base the questions depending on the organization, its goals, key competitors and a candidate’s experience level.
For senior roles, the questions tend to focus on assessing the computational thinking, decision making and reasoning ability of a candidate. For entry level job interviews, the questions are designed to test the hard skills required for building a system architecture.
The Difference between a System Design Interview and a Coding Interview
If a coding interview is like a map that takes you from point A to Z – a systems design interview is like a compass which gives you a sense of the right direction.
Here are three key difference between the two:
Coding challenges follow a linear interviewing experience i.e. candidates are given a problem and interaction with recruiters is limited. System design interviews are more lateral and conversational, requiring active participation from interviewers.
Coding interviews or challenges focus on evaluating the technical acumen of a candidate whereas systems design interviews are oriented to assess problem solving and interpersonal skills.
Coding interviews are based on a right/wrong approach with ideal answers to problem statements while a systems design interview focuses on assessing the thought process and the ability to reason from first principles.
How to Conduct an Effective System Design Interview
One common mistake recruiters make is that they approach a system design interview with the expectations and preparation of a typical coding interview. Here is a four step framework technical recruiters can follow to ensure a seamless and productive interview experience:
Step 1: Understand the subject at hand
Develop an understanding of basics of system design and architecture
Familiarize yourself with commonly asked systems design interview questions
Read about system design case studies for popular applications
Structure the questions and problems by increasing magnitude of difficulty
Step 2: Prepare for the interview
Plan the extent of the topics and scope of discussion in advance
Clearly define the evaluation criteria and communicate expectations
Quantify constraints, inputs, boundaries and assumptions
Establish the broader context and a detailed scope of the exercise
Step 3: Stay actively involved
Ask follow-up questions to challenge a solution
Probe candidates to gauge real-time logical reasoning skills
Make it a conversation and take notes of important pointers and outcomes
Guide candidates with hints and suggestions to steer them in the right direction
Step 4: Be a collaborator
Encourage candidates to explore and consider alternative solutions
Work with the candidate to drill the problem into smaller tasks
Provide context and supporting details to help candidates stay on track
Ask follow-up questions to learn about the candidate’s experience
Technical recruiters and hiring managers should aim for providing an environment of positive reinforcement, actionable feedback and encouragement to candidates.
Evaluation Rubric for Candidates
Facilitate Successful System Design Interview Experiences with FaceCode
FaceCode, HackerEarth’s intuitive and secure platform, empowers recruiters to conduct system design interviews in a live coding environment with HD video chat.
FaceCode comes with an interactive diagram board which makes it easier for interviewers to assess the design thinking skills and conduct communication assessments using a built-in library of diagram based questions.
With FaceCode, you can combine your feedback points with AI-powered insights to generate accurate, data-driven assessment reports in a breeze. Plus, you can access interview recordings and transcripts anytime to recall and trace back the interview experience.
Learn how FaceCode can help you conduct system design interviews and boost your hiring efficiency.
Impact of Online Assessments in Technical Hiring
In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.
The ease and...
Impact of Online Assessments in Technical Hiring
In a digitally-native hiring landscape, online assessments have proven to be both a boon and a bane for recruiters and employers.
The ease and efficiency of virtual interviews, take home programming tests and remote coding challenges is transformative. Around 82% of companies use pre-employment assessments as reliable indicators of a candidate's skills and potential.
Online skill assessment tests have been proven to streamline technical hiring and enable recruiters to significantly reduce the time and cost to identify and hire top talent.
In the realm of online assessments, remote assessments have transformed the hiring landscape, boosting the speed and efficiency of screening and evaluating talent. On the flip side, candidates have learned how to use creative methods and AI tools to cheat in tests.
As it turns out, technology that makes hiring easier for recruiters and managers - is also their Achilles' heel.
Cheating in Online Assessments is a High Stakes Problem
With the proliferation of AI in recruitment, the conversation around cheating has come to the forefront, putting recruiters and hiring managers in a bit of a flux.
The problem becomes twofold - if finding the right talent can be a competitive advantage, the consequences of hiring the wrong one can be equally damaging and counter-productive.
As per Forbes, a wrong hire can cost a company around 30% of an employee's salary - not to mention, loss of precious productive hours and morale disruption.
The question that arises is - "Can organizations continue to leverage AI-driven tools for online assessments without compromising on the integrity of their hiring process? "
This article will discuss the common methods candidates use to outsmart online assessments. We will also dive deep into actionable steps that you can take to prevent cheating while delivering a positive candidate experience.
Common Cheating Tactics and How You Can Combat Them
Using ChatGPT and other AI tools to write code
Copy-pasting code using AI-based platforms and online code generators is one of common cheat codes in candidates' books. For tackling technical assessments, candidates conveniently use readily available tools like ChatGPT and GitHub.
Using these tools, candidates can easily generate solutions to solve common programming challenges such as:
Debugging code
Optimizing existing code
Writing problem-specific code from scratch
Ways to prevent it
Enable full-screen mode
Disable copy-and-paste functionality
Restrict tab switching outside of code editors
Use AI to detect code that has been copied and pasted
Enlist external help to complete the assessment
Candidates often seek out someone else to take the assessment on their behalf. In many cases, they also use screen sharing and remote collaboration tools for real-time assistance.
In extreme cases, some candidates might have an off-camera individual present in the same environment for help.
Ways to prevent it
Verify a candidate using video authentication
Restrict test access from specific IP addresses
Use online proctoring by taking snapshots of the candidate periodically
Use a 360 degree environment scan to ensure no unauthorized individual is present
Using multiple devices at the same time
Candidates attempting to cheat often rely on secondary devices such as a computer, tablet, notebook or a mobile phone hidden from the line of sight of their webcam.
By using multiple devices, candidates can look up information, search for solutions or simply augment their answers.
Ways to prevent it
Track mouse exit count to detect irregularities
Detect when a new device or peripheral is connected
Use network monitoring and scanning to detect any smart devices in proximity
Conduct a virtual whiteboard interview to monitor movements and gestures
Using remote desktop software and virtual machines
Tech-savvy candidates go to great lengths to cheat. Using virtual machines, candidates can search for answers using a secondary OS while their primary OS is being monitored.
Remote desktop software is another cheating technique which lets candidates give access to a third-person, allowing them to control their device.
With remote desktops, candidates can screen share the test window and use external help.
Ways to prevent it
Restrict access to virtual machines
AI-based proctoring for identifying malicious keystrokes
Use smart browsers to block candidates from using VMs
Future-proof Your Online Assessments With HackerEarth
HackerEarth's AI-powered online proctoring solution is a tested and proven way to outsmart cheating and take preventive measures at the right stage.
With HackerEarth's Smart Browser, recruiters can mitigate the threat of cheating and ensure their online assessments are accurate and trustworthy.
Secure, sealed-off testing environment
AI-enabled live test monitoring
Enterprise-grade, industry leading compliance
Built-in features to track, detect and flag cheating attempts
Boost your hiring efficiency and conduct reliable online assessments confidently with HackerEarth's revolutionary Smart Browser.
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