Oct 08, 2026 by Offer

TikTok Interview Questions 2027: How to Solve the LRU Cache Problem

For this TikTok interview question — LRU Cache with O(1) get and put (LeetCode 146) — use a hash map plus a doubly linked list ordered by recency: get moves the node to the front; put inserts at the front and evicts from the back when over capacity. State the design before coding, then trace one example.

TikTok Interview Questions: What the LRU Cache Problem Tests

LRU Cache is commonly reported by candidates in TikTok's coding interviews because it compresses several skills into one problem: data structure design (combining a hash map with a linked list), careful pointer manipulation, and complexity reasoning. Interviewers are testing whether you recognize the pattern — O(1) lookup plus O(1) reorder means hash map plus doubly linked list — and whether you can implement it without bugs under pressure. It is a design problem disguised as a coding problem.

TikTok Interview Questions: How to Solve Step by Step

State the design first. "I'll use a hash map from key to list node for O(1) lookup, and a doubly linked list ordered most-recent to least-recent for O(1) reordering and eviction." Saying this before coding shows you see the structure.
Define the node. Key, value, prev, next pointers. Use dummy head and tail sentinels to eliminate null checks — this is the detail that prevents most bugs.
Write helpers. `addToFront(node)` and `removeNode(node)` — small, testable, and they keep get/put readable.
Implement get(key). If missing, return -1. If present, move the node to the front and return its value.
Implement put(key, value). If the key exists, update the value and move to front. If new, create the node, add to front, add to map; if over capacity, remove the tail node and delete its key from the map.
Walk through an example. Trace `put(1,1), put(2,2), get(1), put(3,3)` narrating the list state — interviewers love this.

Example line: "The key insight is that the hash map gives us O(1) access while the doubly linked list gives us O(1) reordering — neither structure alone satisfies both operations."

Common Mistakes

Forgetting to update recency on get. A get must move the node — this is the most commonly missed detail.
Evicting without cleaning the map. Removing the tail node but leaving its key in the hash map corrupts the cache.
Null-pointer edge cases. Dummy head/tail sentinels exist precisely to avoid these; use them.

Keep Reading

FAQ

What is the time and space complexity? O(1) for both get and put; O(capacity) space.

Can I use an ordered map instead? In languages with one (like Python's OrderedDict or Java's LinkedHashMap), yes — but interviewers usually want the manual implementation to test pointer skills.

What if I blank on the design? Reason from the constraints: O(1) lookup forces a hash map; O(1) reorder forces a linked structure — the combination follows.

Should I handle thread safety? Mention it as a follow-up consideration, not in the core solution — unless the interviewer asks.

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