System Design DSA — Complete Guide for FAANG Interviews

Why System Design DSA Matters#

System design DSA problems are the bridge between pure algorithms and large-scale system design interviews. Unlike LeetCode 1 (Two Sum) which tests a single trick, problems like LRU Cache, LFU Cache, Design Twitter, and Time-Based Key-Value Store force you to compose multiple data structures and maintain invariants across them.

These problems appear in nearly every FAANG loop. Google asks LRU Cache (LeetCode 146) in over 30% of senior backend interviews. Meta favors Design Twitter (LeetCode 355) and Time-Based Key-Value Store (LeetCode 981). Amazon loves Hit Counter (LeetCode 362) and Min Stack (LeetCode 155). Apple frequently asks LFU Cache (LeetCode 460) for embedded systems roles.

The Seven Core Patterns#

Pattern 1 — Doubly Linked List Plus HashMap (O(1) LRU)#

Constant-time get and put by combining O(1) hashmap lookup with O(1) DLL node insertion and removal. The DLL keeps recency order, the hashmap keeps key lookup. Used in LRU Cache and many real caches like Memcached and Redis variants.

Pattern 2 — Min-Heap or Max-Heap Plus HashMap#

For leaderboards, top-K trending tweets, and median finding. Heap orders elements, hashmap allows O(1) deletion or update. Used in Design Twitter, Find Median From Data Stream, and Design Leaderboard.

Pattern 3 — Trie for Prefix Systems#

File systems, autocomplete, phone directories all rely on prefix tries. Insert and search run in O(key length) regardless of dataset size.

Pattern 4 — Bucketed Time Windows#

Hit counters, log storage, and rate limiters bucket events by timestamp granularity. Sliding window sum is O(window size) — much faster than scanning every event.

Pattern 5 — Skip List#

Probabilistic balanced structure with O(log n) average search, insert, and delete. Powers Redis sorted sets and is asked at companies that build databases.

Pattern 6 — Hashing for Compact IDs#

URL shorteners use base62 encoding plus collision-safe hashing. Encode/Decode TinyURL is the classic example.

Pattern 7 — Multiple Heaps for Streaming Stats#

Two-heap pattern — max-heap for the lower half, min-heap for the upper half — gives O(log n) insert and O(1) median. Foundation of Find Median From Data Stream.

The 21 Problems In This Series#

Number	Problem	Difficulty	Pattern
146	LRU Cache	Medium	DLL + HashMap
460	LFU Cache	Hard	DLL + HashMap + frequency buckets
355	Design Twitter	Medium	Heap + HashMap
1166	Design File System	Medium	Trie or HashMap
362	Design Hit Counter	Medium	Bucketed window
981	Time Based Key Value Store	Medium	HashMap + sorted list + binary search
642	Design Search Autocomplete	Hard	Trie + heap
1472	Design Browser History	Medium	Two stacks
1825	Design Leaderboard	Medium	HashMap + sorted structure
353	Design Snake Game	Medium	Deque + set
1206	Design Skiplist	Hard	Skip list
635	Design Log Storage System	Medium	Bucketed timestamps
379	Design Phone Directory	Medium	Set + queue
1396	Design In Memory File System	Hard	Trie of nodes
155	Min Stack	Easy	Two stacks or pair stack
295	Find Median From Data Stream	Hard	Two heaps
622	Design Circular Queue	Medium	Array with two pointers
535	Encode and Decode TinyURL	Medium	HashMap + base62
1603	Design Parking System	Easy	Counters
Recap	Master Recap	All	All seven patterns

How To Use This Series#

Each post follows the same structure: problem statement, why it matters, core insight, dry run, optimal solution in Python and JavaScript, common mistakes, interview tips, follow-ups, and key takeaways. Read them in order if you are new to system design DSA, or jump to a specific problem if you are prepping for a known company.

Common Mistakes Across All Problems#

• Picking a single data structure when the API requires two
• Forgetting to update auxiliary structures on every mutation
• Off-by-one errors in capacity, eviction, or window boundaries
• Using O(n) scans inside what should be an O(1) or O(log n) method
• Not thinking about thread safety when the interviewer asks the follow-up

Interview Tips#

• Always restate the API and constraints before coding
• Sketch the data structures on the whiteboard first
• Verbalize the invariant — what must always be true after each operation
• Walk through one example operation by operation, updating each structure
• End with complexity and a concrete follow-up about scaling

Key Takeaways#

• System design DSA composes multiple data structures around one API
• The seven patterns above cover almost every LeetCode design problem
• LRU Cache is the single most-asked problem in this category at FAANG
• Maintaining invariants across structures is the real interview test
• Time-window problems use bucketing to avoid per-event scans
• Heap plus hashmap solves nearly every top-K with deletion problem
• Practicing this list end-to-end prepares you for senior-level loops