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Common Mistakes Candidates Make in System Design Interviews

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ArchitectureTraceChallenge
🎯
Common Mistakes Candidates Make in System Design Interviews
45 min · senior
senior

You are designing a system to help candidates prepare for system design interviews at a startup with 50 million daily active users (DAU). The system must provide real-time feedback, store candidate designs, and support collaborative review sessions. The system should handle high concurrency and provide low latency responses to keep users engaged.

Constraints
  • End-to-end encryption of candidate data at rest and in transit
  • Support offline design submission with eventual sync
  • P99 latency for feedback under 200ms
  • Scalable to 50M DAU with peak concurrency of 5M simultaneous sessions
  • Data consistency for collaborative review sessions
📋
Guided Steps
1/5
Step 1
Design Data Storage for Candidate Designs
How would you store candidate design submissions to support high write throughput and efficient retrieval for review?
💡 Hint
Consider a database optimized for write-heavy workloads with efficient range queries for versioning and collaboration.
Key Decision
Use Apache Cassandra with partition_key=candidate_id and clustering_key=timestamp to enable linear scalable writes and efficient time-range queries for design versions.
Interviewer Expectation
Mid: Names Cassandra for scalability. Senior: Explains partition key choice to avoid hotspots and clustering key for version ordering.
⚠️Common Wrong Answer
Using a relational database with candidate_id as primary key leads to write hotspots and poor write scalability at 50M DAU.
Step 2
Real-Time Feedback Delivery Architecture
How would you design the real-time feedback mechanism to ensure P99 latency under 200ms for millions of concurrent users?
💡 Hint
Think about low-latency messaging systems that preserve message order and support horizontal scaling.
Key Decision
Use Apache Kafka with candidate_id as partition key to ensure ordered feedback messages per candidate and horizontally scalable consumers for processing.
Interviewer Expectation
Mid: Mentions Kafka for messaging. Senior: Explains partition key choice to maintain order and avoid cross-partition coordination.
⚠️Common Wrong Answer
Using HTTP polling for feedback causes excessive load and latency spikes at scale.
Step 3
Handling Offline Design Submission and Sync
How would you enable candidates to submit designs offline and sync them reliably when back online?
💡 Hint
Consider conflict resolution strategies and durable message queues for eventual consistency.
Key Decision
Use Apache Pulsar with message deduplication and client-generated unique IDs to buffer offline submissions and ensure exactly-once delivery upon reconnection.
Interviewer Expectation
Mid: Suggests queue for offline buffering. Senior: Details deduplication and conflict resolution mechanisms.
⚠️Common Wrong Answer
Relying on client-side retries without server-side deduplication causes duplicate submissions and inconsistent state.
Step 4
Ensuring Data Security and Encryption
How would you implement end-to-end encryption for candidate data both at rest and in transit?
💡 Hint
Think about TLS for transport and envelope encryption with key management for data at rest.
Key Decision
Use TLS 1.3 for all client-server communication and AWS KMS-managed envelope encryption for data stored in Cassandra and Kafka topics.
Interviewer Expectation
Mid: Mentions TLS and encryption at rest. Senior: Explains key rotation, envelope encryption, and integration with KMS.
⚠️Common Wrong Answer
Encrypting only in transit leaves data vulnerable at rest, violating compliance requirements.
Step 5
Designing Collaborative Review Sessions
How would you design the system to support real-time collaborative review sessions with strong consistency guarantees?
💡 Hint
Consider WebSocket-based communication and conflict-free replicated data types (CRDTs) or operational transforms for concurrency control.
Key Decision
Use WebSocket connections for low-latency bidirectional communication and implement CRDTs for conflict-free concurrent edits stored in Redis Enterprise for fast state synchronization.
Interviewer Expectation
Mid: Suggests WebSocket for real-time updates. Senior: Explains CRDT choice to avoid locking and ensure eventual consistency.
⚠️Common Wrong Answer
Using REST polling for collaboration causes high latency and inconsistent views under concurrent edits.
🔌
API Design Challenge
Sketch 3 core API endpoints for the system that enable design submission, real-time feedback, and collaborative review.
Hints
  • How does the client submit a design and receive feedback?
  • How does the client sync offline designs when reconnecting?
  • How does the system handle real-time collaboration and acknowledgments?
📊
Scoring Rubric
▼ Show
Must Have - 60%
  • Use of a write-optimized distributed database like Cassandra with appropriate partitioning
  • Real-time messaging system with partition key to maintain order and scale (Kafka)
  • End-to-end encryption including transport (TLS) and data at rest (envelope encryption with KMS)
  • Support for offline submission with reliable sync and deduplication
Good to Have - 30%
  • Real-time collaboration using WebSocket and CRDTs for concurrency control
  • Detailed key management and rotation strategy
  • Clear API design addressing submission, feedback, and collaboration
Bonus - 10%
  • Discussion of failure modes and recovery strategies
  • Consideration of monitoring and alerting for latency and throughput
Anti-patterns to Avoid
Using relational databases for high write throughput causes hotspots and poor scalability, leading to write bottlenecks and degraded user experience at 50M DAU.
HTTP polling for real-time feedback leads to excessive load and latency spikes, as 50M DAU generate massive reconnects per minute, collapsing the system under load.
No deduplication in offline sync causes duplicate data and inconsistent state, resulting in corrupted candidate design histories and poor user trust.
Encrypting only in transit leaves data vulnerable at rest, risking compliance violations and potential data breaches.
REST polling for collaboration causes stale views and poor user experience due to high latency and inconsistent concurrent edits.
Ignoring key rotation risks key compromise and data exposure, undermining the security guarantees of encrypted candidate data.
✅
Model Answer
▼ Reveal

The system uses Cassandra for scalable design storage, Kafka for ordered real-time feedback, Pulsar for offline sync with deduplication, TLS and AWS KMS for encryption, and WebSocket with CRDTs for collaborative reviews.

Key Decisions
  • Cassandra partition_key=candidate_id and clustering_key=timestamp to enable linear scalable writes and efficient time-range queries for design versions
  • Kafka partition_key=candidate_id to maintain ordered feedback messages per candidate and enable horizontal scaling of consumers
  • Pulsar with message deduplication and client-generated unique IDs to buffer offline submissions and ensure exactly-once delivery upon reconnection
  • TLS 1.3 for all client-server communication to secure data in transit
  • AWS KMS-managed envelope encryption for data at rest with automated key rotation and secure key management
  • WebSocket connections for low-latency bidirectional communication in collaborative review sessions
  • Redis Enterprise with CRDTs to enable conflict-free concurrent edits and eventual consistency without locking
  • Apache Cassandra with candidate_id partition key for scalable write-heavy design storage
  • Apache Kafka with candidate_id partition key for ordered, scalable real-time feedback delivery
  • Apache Pulsar with deduplication for reliable offline submission buffering and sync
  • TLS 1.3 for transport encryption and AWS KMS envelope encryption for data at rest
  • WebSocket for real-time bidirectional communication in collaborative sessions
  • Redis Enterprise with CRDTs for conflict-free concurrent state synchronization
  • Client-generated unique IDs for idempotent offline submission handling
  • Key rotation and secure key management integrated with AWS KMS

Practice

(1/5)
1. Without the retry queue component, what exactly breaks in the webhook delivery system when a subscriber's endpoint is temporarily unreachable?
medium
A. The system immediately drops the event, causing permanent loss
B. The Delivery Worker blocks sending other events to that subscriber until recovery
C. The event is lost silently, and the subscriber never receives it
D. The system continues sending events out of order, violating ordering guarantees

Solution

  1. Step 1: Understand retry queue role

    The retry queue buffers failed deliveries to preserve order by retrying later.

  2. Step 2: Trace failure without retry queue

    Without it, subsequent events may be sent immediately, causing out-of-order delivery.

  3. Final Answer:

    Option D -> Option D

  4. Quick Check:

    No retry queue -> out-of-order delivery possible [check]
Hint: No retry queue -> ordering breaks on failures [check]
Common Mistakes:
2. If the spam detection service becomes unavailable, what is the most likely failure mode affecting the Q&A feed?
medium
A. The feed service crashes due to unhandled exceptions from missing spam data
B. Spam scores default to zero, causing spammy content to appear in feeds without filtering
C. All new questions and answers are blocked until spam detection recovers
D. Users see delayed feed updates but spam filtering continues normally

Solution

  1. Step 1: Analyze spam detection dependency

    Blocking all writes (A) is too strict; defaulting scores (B) risks spam but is safer; delayed updates (D) unlikely if spam data is missing.

  2. Step 2: Unhandled exceptions cause crashes

    If the feed service does not handle missing spam data gracefully, it crashes (C), causing feed unavailability and degraded user experience.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Missing spam data causes unhandled exceptions and feed crashes [check]
Hint: Unhandled missing spam data crashes feed service [check]
Common Mistakes:
3. Without the message broker (e.g., Kafka) in a high-throughput chat system, what exactly breaks in the message delivery flow?
medium
A. Messages are sent synchronously causing sender blocking and message loss on receiver failure
B. Alice's send operation blocks until Bob's server responds; if Bob's server crashes, the message is lost with no retry
C. Messages are encrypted incorrectly causing security breaches
D. The system automatically switches to batch mode causing high latency

Solution

  1. Step 1: Role of message broker

    Broker decouples sender and receiver, enabling asynchronous delivery and retries.

  2. Step 2: Consequence of missing broker

    Without it, messages are sent synchronously causing sender blocking; if receiver crashes, messages are lost; encryption (A) unaffected; blocking and loss (C) is exact failure; batch mode (D) not automatic.

  3. Final Answer:

    Option A -> Option A

  4. Quick Check:

    Broker removal causes blocking sends and message loss on receiver failure [check]
Hint: No broker means synchronous sends block and lose messages [check]
Common Mistakes:
4. At 10x scale, which component in a Write-Through caching system is most likely to become the bottleneck first, and why?
hard
A. The cache, because synchronous writes increase latency and reduce throughput
B. The application servers, because they block waiting for cache writes to complete
C. The database, because it must handle every write synchronously from both cache and application
D. The network, because cache-to-database communication saturates bandwidth

Solution

  1. Step 1: Identify Write-Through bottleneck

    Write-Through synchronously writes to DB on every write, increasing DB load linearly with scale.

  2. Step 2: Compare other components

    Cache handles fast reads; network bandwidth usually sufficient; app servers block but DB saturation causes overall failure first.

  3. Final Answer:

    Option C -> Option C

  4. Quick Check:

    DB synchronous writes bottleneck Write-Through at scale [check]
Hint: Write-Through sync DB writes bottleneck at scale [check]
Common Mistakes:
5. At 10x scale, which component in a message processing system with Dead Letter Queues is most likely to become the bottleneck first, and why?
hard
A. The monitoring system, because alert volume grows exponentially with message failures
B. The main message broker, because partition count limits throughput and causes uneven load
C. The retry worker pool, because retrying messages requires synchronous processing and cannot be parallelized
D. The Dead Letter Queue storage, because it accumulates failed messages faster than they can be retried or inspected

Solution

  1. Step 1: Analyze scaling impact on DLQ storage

    At 10x scale, failed messages accumulate rapidly in DLQ storage, which may not scale as fast as main queue throughput.

  2. Step 2: Evaluate other components

    Main broker (A) can scale with partitions; retry workers (C) can be scaled horizontally; monitoring (D) is less critical bottleneck.

  3. Final Answer:

    Option D -> Option D

  4. Quick Check:

    DLQ storage fills faster than retries can process at 10x scale [check]
Hint: DLQ storage fills first at scale, not broker or workers [OK]
Common Mistakes:
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