Let's be real - technical interviews are tough for everyone involved. Candidates face pressure to perform under artificial conditions, while interviewers struggle to design meaningful assessments that actually predict job success.
The best interviews I've participated in were designed to simulate what candidates would actually do in their day-to-day work. If the role involves writing code, the interview should include a substantial coding component. But what makes a coding challenge truly effective?
The ideal challenge strikes a delicate balance: it should closely mirror real work scenarios while remaining practical for an interview setting. This means creating tasks that are concise enough to explain quickly, manageable to complete within time constraints, yet comprehensive enough to reveal problem-solving skills.
After interviewing 50+ developers since 2019, I've curated a collection of challenges that produce insightful conversations. Today I'm sharing my personal favorite: "Async Memoization." That is a scary sounding name, isn't it? But I promise (pun intended) it's not as bad as it sounds!
This is the task:
Before reading further, I encourage you to give it a try on (Typescript playground):
If you solved it on your first try, excellent work - you've demonstrated solid JavaScript fundamentals! If you're still working through it, that's completely normal. This challenge trips up even experienced developers, so don't worry if it takes some time. Below are the progressive hints I share during interviews, followed by the complete solution.
Let's break down the problem step by step. We need to prevent unnecessary calls to calculateExpensive() when we already have the result for a given argument. This means we need to store and reuse results from previous function calls - in other words, we need a cache.
What data structure should we use for caching? Since calculateExpensive() takes a single number argument and returns a promise resolving to a number, we need a data structure that maps each unique argument to its corresponding result. The most straightforward mapping structure in JavaScript is an object:
const cache: Record<string, number> = {};The cache must be declared outside calculateCheap() to persist between function calls
We have to keep the cache variable outside of calculateCheap function so it won't be recreated between calls. The function logic is simple: check the cache first - if we have the result, return it immediately. If not, call calculateExpensive(), cache the result, and return it:
const cache: Record<number, number> = {};
async function calculateCheap(num: number): Promise<number> {
if (cache[num] !== undefined) {
return cache[num];
}
const result = await calculateExpensive(num);
cache[num] = result;
return result;
}Looks simple enough! However, if you test this solution, you'll notice that "calculateExpensive called with = 2" still logs three times.
This is a common stumbling point that reveals a subtle timing issue. When candidates need guidance, I suggest:
- Mentally execute the code step by step, simulating how JavaScript processes each line
- Pay attention to the order of operations: when does the cache get populated versus when do we check it?
The fundamental issue is a timing mismatch: we check for cached values synchronously, but we only store results asynchronously after the promise resolves.
Since the calling pattern is fixed - per requirements, calculateCheap() must handle concurrent calls without any coordination from the caller - we need to flip our approach and populate the cache synchronously as well.
Wait, what? How can we do that when calculateExpensive is an async function? We need to store a value in the cache before we even know what that value will be!
What do async functions return? Always promises. Right now, we're awaiting the promise from calculateExpensive() to extract its value before caching. But consider this: do we actually need the unwrapped result inside calculateCheap()? We're not transforming it - just passing it through. Since calculateCheap() is async, whatever we return gets wrapped in a promise regardless.
Here's the key insight: JavaScript already has a construct for "values we don't know yet" - promises! The solution is elegant: cache the promise itself, not what it resolves to:
const cache: Record<number, Promise<number>> = {};The rest should be easy. Here is the complete solution (I've used JS Map to hold the cache).
First, this task works universally across JavaScript roles. Whether you're hiring for backend, frontend, or full-stack positions, any candidate expected to understand JavaScript should be able to tackle this challenge. It requires no external libraries, frameworks, or specific tooling — no React, Express, or Nest.js knowledge needed. Just pure vanilla JavaScript working with core data structures and promises.
Second, it's genuinely practical. You won't find yourself reversing binary trees, counting unique characters, or working through yet another Fibonacci sequence. This isn't a Leetcode puzzle — it's a real-world problem I've personally encountered and solved multiple times across both frontend and backend projects.
Here are common scenarios where this pattern is useful:
- Multiple React components mounting simultaneously and requesting the same data - user profiles, configuration settings, or API responses. Without memoization, you'll spam your backend with duplicate requests.
- Rate-limiting expensive operations like third-party API calls, database queries, or file system operations. When multiple concurrent requests need the same external service response, memoization prevents hitting rate limits and reduces costs.
Third, this task tests two fundamental skills: Caching - which is notoriously one of computer science's "two hard problems" along with naming things (and off-by-one errors :)) Asynchronous programming - understanding promises, race conditions, and how to handle concurrent operations gracefully.
Finally, this challenge offers flexibility in difficulty scaling. You can tailor it precisely to the candidate's experience level and the role's demands. Start junior developers with synchronous memoization to focus on caching fundamentals, then layer on async complexity as they progress. For senior candidates, jump directly to the full async version and explore edge cases like cache invalidation strategies.
Consider these advanced follow-ups:
-
"What will
console.log(calculateCheap(5) === calculateCheap(5))print?" This reveals whether they truly understand why the solution works - and catches candidates who might have used AI assistance without understanding it (yes, this has happened in one of my interviews). And by the way, the answer differs depending on whethercalculateCheapis marked asasyncor not. -
"What would be the best data structure for the cache here?" Explore their choice between Object, Map, and WeakMap:
Object: Works, but requires awareness of key stringification limitations Map: Usually the optimal choice - has nicer API and is designed for frequent additions/deletions WeakMap: Only viable when arguments are objects and you want automatic garbage collection tied to argument lifetime
-
"What happens when calculateCheap gets called with millions of unique arguments?" This surfaces a critical flaw: unbounded memory growth leading to potential out-of-memory crashes. Strong candidates will suggest cache eviction strategies - LRU policies, time-based expiration, or size limits. This opens discussion of different eviction algorithms and their trade-offs.
-
"How would you share this cache across multiple application instances?" The answer involves distributed caching solutions like Valkey or Redis, revealing the candidate's understanding of horizontal scaling challenges.
If you're an interviewer, I'd love to hear how this challenge works in your interviews - drop me a line with your experiences! And if you're preparing for interviews, you now have a powerful pattern in your toolkit that will serve you well beyond just interviews.
I've got several other fascinating challenges lined up that I'm excited to share soon. What's your go-to interview question? Share your favorites in the comments!