OBJECT DESIGN · LLD · HARD CODEX

Object Design · LLD Interview Questions — Hard

Q: How do you design an API Rate Limiter at the class level? Compare Token Bucket and Leaky Bucket models.

- Token Bucket: Stores tokens up to a maximum capacity. Tokens are added at a constant rate. Each request consumes a token. Allows bursts of traffic. - Class Layout: RateLimiter class holds lastRefill

Q: How do you design a lock-free thread-safe concurrent queue?

- Concept: Bypasses heavy OS-level mutex synchronization using Compare-And-Swap (CAS) CPU primitives. - Implementation: Employs the Michael-Scott Queue algorithm. Uses atomic references (AtomicReferen

Q: Design a thread-safe, high-performance in-memory cache with an LRU eviction policy.

- Data Structures: - HashMap: Provides $O(1)$ lookup speed to locate cache keys. - Doubly Linked List: Tracks usage order in $O(1)$ time. Whenever a key is read/updated, its node is moved to the head.

Q: Design a custom Connection Pool supporting acquisition timeout configurations.

- Key Fields: blockingQueue representing available connections, activeConnectionsCount, maxPoolSize, and lock. - Acquisition Flow: A thread requests a connection using poll(timeout, TimeUnit). If the

Q: Explain the structural design of a Multi-Level Cache (L1/L2) system.

- L1 (In-Memory): Fast, small memory size (local heap). - L2 (Distributed): Slower, larger memory size (Redis/Memcached). - Coherence Policies: - Write-Through: Updates both L1 and L2 immediately. - W

Deep runtime internals, memory models, distributed design, concurrency failure modes, and architectural decisions.

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THEORY QUESTIONS & SOLUTIONSShowing 5 of 50 questions

Q1How do you design an API Rate Limiter at the class level? Compare Token Bucket and Leaky Bucket models.

Token Bucket: Stores tokens up to a maximum capacity. Tokens are added at a constant rate. Each request consumes a token. Allows bursts of traffic.
- Class Layout: RateLimiter class holds lastRefillTimestamp, allowedTokens, refillRate, and a lock.
Leaky Bucket: Requests enter a queue (bucket) and leak out at a constant processing rate. Smoothes out traffic bursts.
- Class Layout: LeakyLimiter wraps a thread-safe blocking queue of size capacity with a background thread consuming requests at interval leakRate.

Q2How do you design a lock-free thread-safe concurrent queue?

Concept: Bypasses heavy OS-level mutex synchronization using Compare-And-Swap (CAS) CPU primitives.
Implementation: Employs the Michael-Scott Queue algorithm. Uses atomic references (AtomicReference in Java) for the head and tail nodes. Pushing and popping elements runs inside a CAS loop, continuously retrying until successful.

Q3Design a thread-safe, high-performance in-memory cache with an LRU eviction policy.

Data Structures:
- HashMap: Provides $O(1)$ lookup speed to locate cache keys.
- Doubly Linked List: Tracks usage order in $O(1)$ time. Whenever a key is read/updated, its node is moved to the head. The tail represents the least recently used node.
Synchronization: Uses a ReentrantReadWriteLock to permit concurrent readers while serializing write evictions.

Q4Design a custom Connection Pool supporting acquisition timeout configurations.

Key Fields: blockingQueue representing available connections, activeConnectionsCount, maxPoolSize, and lock.
Acquisition Flow: A thread requests a connection using poll(timeout, TimeUnit). If the queue is empty and activeConnectionsCount < maxPoolSize, the pool instantiates a new connection dynamically. Otherwise, the thread blocks up to the designated timeout.

Q5Explain the structural design of a Multi-Level Cache (L1/L2) system.

L1 (In-Memory): Fast, small memory size (local heap).
L2 (Distributed): Slower, larger memory size (Redis/Memcached).
Coherence Policies:
- Write-Through: Updates both L1 and L2 immediately.
- Write-Back: Updates L1 immediately and queues asynchronous updates to L2.
- Eviction: Evicting from L1 does not affect L2. Evicting from L2 triggers invalidation signals to all local L1 caches.

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