Geek read: System Design Interview by Alex Xu Quiz

Explore foundational system design patterns and strategies, from scaling to distributed algorithms, as highlighted in popular interview preparations. Assess your knowledge of approaches, algorithms, and data architecture essentials.

1. System Design Interview Approach

   Which is the typical sequence of steps when answering a system design interview question?

   1. Start coding, test implementation, optimize, present results
   2. Understand the problem, propose high-level design, deep dive into design, wrap up
   3. Research online, present findings, interview discussion, summary
   4. Memorize common systems, recite definitions, answer clarifying questions, conclude

   Explanation: The four-step approach emphasizes understanding requirements, suggesting a high-level structure, diving into details, and summarizing. Starting with code or focusing on implementation skips critical requirements gathering. Memorizing systems or researching online is not a structured or expected method.

2. Rate Limiting Algorithms

   Which rate limiting method uses tokens that are added at a steady rate and consumed with each request until depleted?

   1. Leaking bucket
   2. Fixed window counter
   3. Sliding window log
   4. Token bucket

   Explanation: The token bucket algorithm adds tokens over time, allowing bursts up to unused tokens, making it flexible. Fixed window counter groups requests by time slots; sliding window log tracks recent requests precisely, and leaking bucket evens out request flows but differs from token replenishment.

3. Distributed System Consistency

   What is a key trade-off described by the CAP theorem that system designers must consider?

   1. Data type vs. Storage size
   2. Consistency vs. Availability
   3. Latency vs. Scalability
   4. Throughput vs. Authentication

   Explanation: CAP theorem states distributed systems must trade off between consistency and availability in the presence of network partitions. Latency vs. scalability is a different trade-off; data type vs. storage and throughput vs. authentication are unrelated to the CAP theorem.

4. Consistent Hashing Purpose

   Why is consistent hashing important in distributed systems such as key-value stores?

   1. It ensures fixed-size data storage per server
   2. It accelerates read and write operations by using RAM
   3. It minimizes data movement when nodes are added or removed
   4. It encrypts all communication between servers

   Explanation: Consistent hashing distributes data so only a small portion needs to move when the set of nodes changes. It does not guarantee fixed-size storage, perform encryption, or directly accelerate operations using RAM.

5. Data Structures in Search Autocomplete

   What data structure is commonly used to implement efficient search autocomplete features?

   1. Stack
   2. Heap
   3. Trie
   4. Queue

   Explanation: Tries enable fast prefix-based searches, which are ideal for autocomplete. Heaps manage ordered data, stacks are last-in-first-out, and queues are first-in-first-out, none of which directly support prefix search efficiently.