Lru Cache Faang Interview

Nineteen companies. That's how many major tech employers tag LRU Cache as an interview question in our dataset. Not two or three. Nineteen, including every single FAANG member, plus DoorDash, Oracle, Zoom, PayPal, and TikTok. No other problem in our 450+ problem bank has that kind of cross-company reach.

If you're preparing for technical interviews at any top-tier company, there's a good chance you'll see this problem. The why behind its ubiquity tells you something useful about what interviewers are actually testing.

⚡TL;DR

LRU Cache is tagged at 19 companies in our problem bank. No other problem comes close. It shows up everywhere because it tests five things in 30 minutes: hash map fluency, doubly linked list manipulation, O(1) amortized complexity, API design, and component composition.

How We Collected This Data

Every problem on Codeintuition's learning path is tagged with the companies known to ask it in interviews. Across 16 courses and 450+ handpicked problems, those tags create a dataset of which patterns and problems each major employer actually tests. The data comes from known interview questions collected from public sources and cross-checked against other preparation platforms. It's not a sample of every question ever asked at these companies.

The numbers here come directly from that tagging system. When we say LRU Cache appears at 19 companies, that's the count of distinct company tags on the LRU Cache problem in our Hash Table course.

Finding 1: Asked Across 19 Companies

LRU Cache is the most cross-company tested design problem in our dataset. It appears at 19 companies including Amazon, Google, Apple, Microsoft, and Meta. The reason it's everywhere: it tests hash map fluency, doubly linked list manipulation, O(1) amortized complexity reasoning, and API design in a single 30-minute problem. Nothing else in our data covers that many employers.

The full list: Amazon, Apple, Google, Microsoft, Facebook (Meta), PayPal, DoorDash, Adobe, Twilio, Uber, Oracle, TikTok, Zoom, eBay, Yandex, LinkedIn, Zillow, Intuit, and Cloudera.

For context, the next most broadly tested patterns are Counting (9 companies), Prefix Sum and Backtracking (8 companies each), and Binary Search (7 companies). LRU Cache doesn't just lead the list. It doubles the runner-up.

“LRU Cache is tagged at 19 companies in our dataset. The next closest pattern reaches 9. That gap tells you something about what interviewers collectively value.”

Codeintuition platform data

LRU Cache isn't a fashionable question. It's been a staple for over a decade. Companies keep using it because it's an efficient signal: one problem, 30 minutes, five skills tested at once. The longevity probably says more about interview design constraints than about LRU Cache itself. Interviewers need questions that reliably separate candidates across multiple dimensions in limited time, and very few problems pack that much signal into a single question.

Finding 2: Five Skills in One 30 Minute Problem

The concept of cache eviction isn't hard. The engineering depth required to implement it correctly under a timer is what interviewers care about.

When you build an LRU Cache from scratch, five things get tested at once:

1Hash map fluency: The get and put operations need O(1) lookup. That means a hash map mapping keys to nodes, not to values directly. Most candidates understand hash maps conceptually, but the indirection layer (mapping to a node reference rather than the value itself) reveals whether that understanding is mechanical or shallow.
2Doubly linked list manipulation: The eviction order requires a container where you can remove any element in O(1) and add to one end in O(1). A singly linked list can't do this. An array can't either. Only a doubly linked list with direct node references gives you constant-time removal from arbitrary positions. This tests whether you understand why the doubly linked list is necessary, not just that it's used.
3O(1) amortized complexity reasoning: The interviewer will ask about time complexity. Both get and put must be O(1). Explaining why requires understanding how the hash map and doubly linked list work together: the hash map gives constant-time access to the node, and the doubly linked list gives constant-time repositioning of that node. If either piece is missing, the complexity breaks.
4API design: LRU Cache has a clean two-method interface: get(key) and put(key, value). But getting the edge cases right is the hard part. Capacity overflow, key updates, the interaction between get (which promotes a key to most-recently-used) and put (which may evict). You have to think carefully about the contract between those two methods.
5Component composition: LRU Cache isn't one building block. It's two, composed to solve a constraint that neither handles alone. The hash map gives O(1) access. The doubly linked list gives O(1) ordering. Together they produce an O(1) cache with eviction. Candidates who can see that composition and build it from the parts are the ones who pass. The ones who've memorised a solution get stuck the moment an interviewer tweaks the requirements.

❗ Important

If you can implement LRU Cache from scratch, explain the O(1) complexity of both operations, and handle edge cases (empty cache, capacity of 1, duplicate puts), you're covering the exact skill surface that 19 companies independently decided was worth 30 minutes of interview time.

Finding 3: The Construction Most Candidates Get Wrong

Here's the implementation at the mechanism level, and where most candidates break down. The core is a HashMap paired with a doubly linked list. The list maintains access order: the head is the most recently used, the tail is the least recently used. Every get operation moves the accessed node to the head. Every put operation adds a new node at the head and, if capacity is exceeded, removes the node at the tail.

Python

Trace through a concrete sequence to see why each piece matters. Suppose capacity is 2:

1put(1, "A") adds Node(1, A) to the front. Cache: {1: Node}. List: head, (1,A), tail.
2put(2, "B") adds Node(2, B) to the front. Cache: {1: Node, 2: Node}. List: head, (2,B), (1,A), tail.
3get(1) removes Node(1, A) from its position and re-adds it to the front. List: head, (1,A), (2,B), tail. Returns "A".
4put(3, "C") triggers eviction. The tail's predecessor is Node(2, B). Remove it from both the list and the cache. Add Node(3, C) to the front. List: head, (3,C), (1,A), tail.

The get(1) call in step 3 is where candidates stumble. Without moving Node(1, A) to the front, the eviction in step 4 would remove the wrong node. Most candidates who fail this problem get the basic layout right but miss the promotion-on-access mechanic. They understand what LRU means but haven't traced the state transitions closely enough to implement the ordering invariant.

The dummy head and tail nodes are the other frequent source of errors. Without them, every add and remove operation needs special-case handling for empty lists and single-element lists. The dummy nodes eliminate those branches entirely. Candidates who skip them usually spend 10 minutes debugging null pointer exceptions they could've avoided.

“Most candidates who fail LRU Cache understand the concept. They fail the implementation because they haven't traced the state transitions that keep the ordering invariant intact.”

Based on 200,000+ submissions

What This Means for Your Interview Preparation

If you're interviewing at any company in the top 20, LRU Cache should be on your short list. But solving it once on LeetCode and building it from scratch under a timer are two different things. What actually helps:

Build from components, not memory: Don't memorise the implementation. Understand why you need a hash map (O(1) lookup), why a doubly linked list (O(1) removal from arbitrary position), and why dummy nodes (eliminate edge cases). If you can answer those three "why" questions, you can reconstruct the whole thing under pressure.
Trace at least 6 operations end to end: Step through put, get, put (eviction), get (miss), put (update existing key), and get (after update). Every operation changes both the hash map and the linked list. If you haven't traced both in parallel, you'll miss the interaction.
Practice under a timer: LRU Cache is rated Medium. In a real interview, you'll have roughly 20 minutes after clarifying the problem. That's enough if you know the construction. It's not enough if you're figuring out the doubly linked list mechanics on the fly.
Know the common mistakes: Forgetting to remove the evicted key from the hash map. Forgetting to promote on get. Skipping dummy nodes and drowning in null checks. Not handling the "put with existing key" case (which should update the value and promote the node).
Nail the complexity explanation: "Both operations are O(1)" isn't a complete answer. Explain how: the hash map gives O(1) key-to-node access, the doubly linked list gives O(1) node removal and front insertion. That composition is what guarantees O(1) amortized for both get and put.

From Understanding to Construction

There's a real difference between knowing that something works and knowing why it works. Cognitive science calls it elaborative interrogation: asking "why does this work?" instead of "what's the answer?" Engineers who prepare by asking "why does LRU Cache need a doubly linked list instead of a singly linked list?" retain the construction far longer than those who memorise the code.

On Codeintuition, the Hash Table course covers LRU Cache as a design problem that combines five pattern families you've already worked through individually. By the time you reach it, you've built hash tables from scratch using four different collision resolution strategies and you've done doubly linked list operations in an earlier course. It doesn't arrive as a memorisation exercise. It's a composition challenge where you already understand both components.

Go back to step 3 in the trace above. The get(1) call moved Node(1, A) to the front, which meant the eviction in step 4 correctly removed Node(2, B) instead of the node you just accessed. That single state transition, promotion-on-access, is where most candidates fail. Not because they don't understand caching, but because they haven't traced the ordering invariant closely enough to implement it under pressure. Solving LRU Cache once means you need to remember the code. Constructing it from the invariants means you understand why the code has to be that way. Nineteen companies independently decided that distinction is worth thirty minutes of interview time.

LRU Cache combines hash table mechanics with doubly linked list manipulation. The free Arrays and Singly Linked List courses on Codeintuition build the linked list fluency that LRU Cache requires, including pointer manipulation, reversal patterns, and node-level operations. If the first-principles teaching approach fits how you learn, the complete learning path builds through the Hash Table course where LRU Cache is taught as a composition problem at $79.99/year.

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It tests five engineering skills in a single 30-minute problem: hash map fluency, doubly linked list manipulation, O(1) amortized complexity reasoning, API design, and component composition. Very few problems pack that much signal into one question, so interviewers get a strong read on candidates without needing multiple rounds.

A HashMap paired with a doubly linked list. The hash map provides O(1) key-to-node lookup, and the doubly linked list provides O(1) removal from any position and O(1) insertion at the front. Neither alone satisfies the O(1) constraint for both get and put operations. The composition of both is what makes it work.

For a Medium-difficulty problem, interviewers typically expect 15-20 minutes for a clean implementation after initial problem clarification. That's achievable if you understand the construction from first principles. If you're building the doubly linked list logic from scratch during the interview without prior practice, you'll likely run over. Timed practice before the interview matters.

The three most frequent failures are: forgetting to promote a node to the front of the list on a get operation (which breaks the LRU ordering invariant), forgetting to delete the evicted key from the hash map when capacity overflows (which creates a memory leak and stale references), and skipping dummy head and tail nodes (which forces complex null-check branching on every insert and remove). All three come from not having traced the full state transitions before the interview.

In most FAANG interviews, it's a coding question rated Medium difficulty. You're expected to implement the full working solution with get and put methods, not just describe the architecture. Some companies use it as a warm-up in system design rounds to verify that candidates can think at the building-block level before discussing distributed caching. Either way, a working implementation is expected.

Practice in the language you'll use during the interview. The core logic is identical across Python, Java, C++, JavaScript, and TypeScript, but pointer manipulation syntax differs enough that switching languages under time pressure costs minutes you don't have. The doubly linked list operations in particular require comfort with your language's reference semantics.