Top 15 Netflix Coding Interview Problems, Ranked by Frequency

Netflix is the company that trained the world to binge-watch. Naturally, they also want to know if you've been binge-grinding LeetCode. The twist: their coding interviews are less "prove you have a PhD in graph theory" and more "show me you understand the thing we actually build." Caching layers. Real-time pipelines. Data structures that survive 10 million events per second.

If you've been stress-memorizing dynamic programming problems on blind faith, you may be over-prepared for some things and badly exposed in others.

This list is ranked by frequency, combining candidate reports on Blind and LeetCode Discuss, the GitHub repo tracking Netflix question frequency by company tag, and interview guides from candidates who went through the loop in 2024 and 2025. Where multiple variants of a problem appear, the most commonly cited version is listed.

Netflix's interview is highly team-dependent. Some teams skip LeetCode questions entirely and run whiteboard design sessions instead. Most teams land somewhere in the middle: one medium-to-hard coding problem in 45 minutes, with a practical twist and a follow-up about how the solution behaves at scale.

The Full List at a Glance

#	Problem	LC #	Difficulty	Pattern
1	LRU Cache	146	Medium	Linked list + hash map
2	Find Median from Data Stream	295	Hard	Two heaps
3	Merge Intervals	56	Medium	Sorting + intervals
4	Top K Frequent Elements	347	Medium	Heap / bucket sort
5	Meeting Rooms II	253	Medium	Interval + min-heap
6	Merge k Sorted Lists	23	Hard	Min-heap
7	Longest Substring Without Repeating Characters	3	Medium	Sliding window
8	Logger Rate Limiter	359	Easy	Hash map + timestamps
9	Number of Islands	200	Medium	Grid BFS / DFS
10	Course Schedule	207	Medium	Topological sort
11	Trapping Rain Water	42	Hard	Two pointers
12	Serialize and Deserialize Binary Tree	297	Hard	BFS / preorder DFS
13	Design Hit Counter	362	Medium	Queue / circular array
14	Clone Graph	133	Medium	BFS with hash map
15	Word Search II	212	Hard	Trie + backtracking

These Five Will Find You, Ready or Not

1. LRU Cache (LC 146)

This is the most consistently reported Netflix problem across every stage of the loop. Phone screens, onsites, warm-ups to harder design questions. Netflix runs some of the most aggressive CDN and content-serving caches in the world. Knowing how an LRU cache works is baseline literacy. Not knowing it is a red flag with a timestamp.

The trick is recognizing this is a design problem dressed as a coding problem. A plain hash map gives you O(1) lookup but no ordering. A plain doubly linked list gives you O(1) insert and delete but no fast access. The solution fuses both: hash map for O(1) get, doubly linked list to track recency, sentinel head and tail nodes to eliminate edge-case logic for inserts at the boundary.

When you're done coding, expect a follow-up: "How would this behave under concurrent access?" or "Where would you use this in a recommendation system?" Have an answer. The follow-up is where the real interview starts.

A programmer being asked about sync.Pool internals when all they studied was linked list reversal

The LRU cache is in there somewhere. You just have to believe.

2. Find Median from Data Stream (LC 295)

High frequency, genuinely hard if you haven't seen it. The naive approach of sorting the stream every time falls apart at scale. The insight: maintain two heaps. A max-heap holds the smaller half, a min-heap holds the larger half. Keep them balanced within one element. Median is either the top of one heap or the average of both tops.

Netflix streams continuous telemetry at massive volume. Latency percentiles, buffering events, playback quality metrics. Median is a better central tendency measure than mean for skewed data, and computing it on a live stream without storing everything is a real engineering problem, not a thought experiment.

The implementation detail that bites people: always push to the max-heap first, then rebalance. Getting the push order backwards produces silent bugs that only appear on specific input orderings. Your test cases will lie to you.

3. Merge Intervals (LC 56)

The highest raw frequency score in the GitHub company-tag dataset for Netflix at roughly 5.6%. Sort by start time, iterate, merge when current.start <= last.end. The merge condition uses <=, not <, because two intervals that share a boundary are touching, not separate. That one character has ended more interviews than people admit.

Netflix's practical twist often frames this as a scheduling or playlist problem: given a set of content segments, some of which overlap, produce a consolidated timeline. The underlying algorithm is identical. What changes is how you extract the start and end from a richer object, and whether you need to carry along metadata during the merge.

4. Top K Frequent Elements (LC 347)

Appears in multiple candidate reports, often in the context of recommendation systems. Given a stream of user events or content identifiers, return the k most frequently occurring ones. There are two clean solutions worth knowing: min-heap in O(n log k) or bucket sort in O(n). Know both. Netflix interviewers have asked for the O(n) version as a follow-up after candidates produce the heap solution.

Bucket sort here means: create an array indexed by frequency (max size n), push each element into its frequency bucket, then read from the back. Clean, and it demonstrates you can think beyond the obvious data structure. Bonus: the interviewer gets to feel smart for asking it.

5. Meeting Rooms II (LC 253)

Interval problems dominate Netflix's coding rounds. This one asks for the minimum number of meeting rooms required to host a set of overlapping meetings. Sort by start time, use a min-heap tracking end times of active meetings. When a new meeting starts, check if the earliest-ending meeting is already done. If yes, reuse its room (pop the heap). If no, add a room.

The problem maps directly to Netflix's buffering allocation, CDN slot scheduling, and content delivery windows. Expect a follow-up about handling cancellations or real-time rescheduling. The coding problem is a warm-up. The conversation after is the test.

The Next Ten: Same Algorithm, Fancier Framing

Netflix doesn't invent new problems for the lower half of this list. They take patterns you already know, wrap them in a streaming or pipeline story, and see if you can see through the narrative to the data structure underneath.

Merge k Sorted Lists (LC 23). Min-heap of size k. Push the first node from each list. Pop the minimum, push its next node. Netflix frames this as merging sorted streams of telemetry events. Same algorithm, richer input object. If you panic when the problem talks about events instead of nodes, practice translating.

Longest Substring Without Repeating Characters (LC 3). Two pointers, a hash set tracking the current window's characters. When you hit a duplicate, shrink from the left. Netflix frames this as parsing log strings or token streams. Know your character set assumptions: interviewers have asked what changes if the input is Unicode rather than ASCII. It will feel like a trick question. It is not.

Logger Rate Limiter (LC 359). Given a log message and a timestamp, return true only if the same message has not appeared in the past 10 seconds. Hash map from message to last-seen timestamp. The interesting follow-up is always about memory: what do you do when the hash map grows unboundedly? The answer is a sliding window of (timestamp, message) pairs with stale-entry clearing.

Number of Islands (LC 200). Grid BFS or DFS, mark visited cells. Netflix frames this as availability zone isolation: given a grid, how many disconnected regions exist? Practice both BFS and DFS versions. Interviewers have asked candidates to switch approaches mid-problem. Not because the second approach is better. Because they want to watch you adapt.

Course Schedule (LC 207). Topological sort with cycle detection. Kahn's algorithm counts in-degrees; when the queue empties, check whether all nodes were processed. The insight: processed_count != n means a cycle exists. Netflix frames this as feature dependency graphs or encoding pipeline ordering. The graph always has a reason to exist. Your job is to notice it.

Trapping Rain Water (LC 42). Two-pointer approach: track left-max and right-max. When left_max < right_max, the right side guarantees at least right_max height, so the left pointer is the bottleneck and you can safely compute trapped water there. Advance whichever pointer has the lower max. The key move is realizing you don't need the exact right max, just the guarantee.

Serialize and Deserialize Binary Tree (LC 297). The serialization encoding matters more than the algorithm. BFS with a null marker is readable. Preorder DFS with a sentinel is more compact. Agree on the format with your interviewer before you write a line. This is one of those problems where talking for two minutes upfront saves you fifteen minutes of backtracking.

Design Hit Counter (LC 362). A circular array of size 300 (one slot per second for 5 minutes) beats a plain queue on memory. Each slot holds a (timestamp, count) pair. This problem tests whether you can trade memory for time and articulate the tradeoff. Netflix interviewers want systems reasoning here, not just working code.

Clone Graph (LC 133). BFS from the start node, hash map from original node to its clone. When you visit a neighbor, if it already has a clone, link to it; otherwise, create the clone and enqueue it. The hash map prevents infinite loops in cyclic graphs. The edge case that trips people: the graph might be cyclic. You will visit nodes you've already visited. The hash map is not optional.

Word Search II (LC 212). Build a trie from the word list, DFS across the grid, prune branches not in the trie. The critical optimization: once you find a word, remove it from the trie so you don't report it twice. Appears almost exclusively at senior or staff level. If this shows up in your phone screen, either the job posting was wrong or you're being evaluated for a higher band.

Where to Actually Put Your Prep Time

A massive bodybuilder hunched over a tiny desk, coding by hand, captioned "when the interview says no AI assistance allowed"

Two hundred problems. No hints. One offer.

The pattern breakdown: 4 are interval or heap problems, 3 are graph or grid traversal, 3 are design problems with a data structure component, 2 are sliding window. This is not a graph-heavy or DP-heavy list. Google weights those more. Netflix weights practical data structures and real-time processing. If you've been drilling DP subsets hoping to get lucky, redirect that energy.

Work through problems 1 to 7 first. They cover the most ground and appear in both phone screens and onsites. Problems 8 to 13 are the ones that separate "knows the pattern" from "can explain the engineering tradeoff." Problems 14 and 15 only matter if you're targeting senior or above.

Finishing the coding problem early is common at Netflix. Use the remaining time to discuss systems implications, not to optimize further. At senior level, the hardest differentiation is not solving the problem but explaining the engineering tradeoffs out loud. "It works" is table stakes. "Here's what breaks it at scale and here's why I'd accept that" is the answer that gets you hired.

After each problem, ask yourself: how would this run at 10 million events per second? Netflix interviewers reliably push into that territory once the coding is done. If you want to practice the spoken, real-time version of that reasoning, SpaceComplexity runs voice-based mock interviews where you defend your solution out loud and get rubric-based feedback on communication and technical depth, not just correctness.

For the behavioral and system design parts of the loop, the Netflix onsite interview guide breaks down what each of the four to seven rounds actually tests. The Netflix system design interview guide covers what the design rounds specifically look for at each level.

For broader preparation context, Top 20 FAANG Coding Interview Problems covers the cross-company patterns that generalize well, and the sliding window algorithm guide goes deeper on the two-pointer and window variants behind problems 3, 5, and 7 on this list.