Common Patterns
Data Structure Design
Composing hash maps, heaps, trees, and queues into custom structures — a pattern that tests whether you understand tradeoffs, not just APIs.
The whole workflow in about nine minutes: how to spot a data structure design problem, build the two canonical compositions (the LRU cache and the median finder), drive the AI from your design instead of deferring to it, and verify the result. Chapters let you skip to whatever you need. The written deep dive is below.
Some coding problems are about building the right container for your data. You'll see problems that ask you to design an LRU cache, implement a median finder, track connected components, or support range queries efficiently. What these all share is that choosing and composing the right data structures determines whether every operation hits the time complexity the problem demands.
This is where AI coding interviews get interesting. An AI assistant can absolutely implement a doubly-linked list or a heap for you. But you'll be in charge of putting the pieces together, directing the AI rather than deferring to it.
This pattern is a little different because it's less about identifying the solution and more about knowing how put pieces together.
Interviewers don't expect you to have every exotic data structure memorized. Few walk into an interview knowing the internals of a Fibonacci heap off the top of their head. What they want to see is that you can reason about what operations need to be fast, identify which primitives support those operations, and compose them into something that works. If you can do that thinking out loud, you're in great shape.
Recognizing the pattern
Data structure design problems usually announce themselves in one of two ways. Either the prompt literally says "design a data structure that..." (LRU cache, median finder, range tracker, autocomplete index), or it lists a set of operations with specific time complexity demands ("I need O(1) lookup by key, O(1) insert at front, O(1) removal of the oldest element").
A few signals that you're in this pattern:
- Multiple operations with conflicting demands. "Add in O(log n) and retrieve the median in O(1)," for example. No single primitive does both, so you'll be composing.
- An explicit "design a class" framing. This is the giveaway; the interviewer is testing whether you can pick the right primitives and wire them up.
- A familiar product feature that maps to a known shape. Recently-used lists, sliding-window stats, leaderboards, connected components, autocomplete, range queries: each has a canonical composition.
The pattern turns operation requirements into a concrete set of structures. That distinction matters because the AI is excellent at implementing whatever you specify, and bad at picking the composition itself.
The building blocks
Worked examples
LRU Cache
Median Finder
Union-Find
Designing the structure
Prompting the AI
Verifying the AI's code
When to use vs. alternatives
What interviewers expect
Putting it together
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