Mastering System Design Interview Questions for Engineers

System Design Interviews are a crucial milestone in the career growth of any engineer, and falling short of your interviewer’s expectations could get you down leveled (i.e., being offered a role that’s at a lower level role than the senior role you applied for).

When I led System Design Interviews at FAANG/MAANG companies, I saw many skilled candidates lose job opportunities because they didn’t understand what interviewers look for in a senior-level candidate.

The thing is — senior candidates might get similar, open-ended questions to junior candidates (e.g., “Design Quora”), but their answers must go beyond basic solutions to show more depth of knowledge.

I’ll be focusing on how seniors can demonstrate advanced knowledge for common design problems such as Design Quora, WhatsApp, and GFS.

Let’s dive in.

A senior engineer’s approach to System Design Interview questions

As opposed to junior engineers, senior engineers are expected to do a deeper dive into various technical considerations concerning the system’s long-term scalability, alignment with business needs, and so on.

Senior engineers should know various components and strategies to scale a system, such as database sharding, caching mechanisms, load balancer algorithms, and communication protocols.

To show your depth of knowledge in the System Design Interview, your talking points as a senior engineer can include:

• Deep dive into components and their interaction
• Complete lifecycle of a request (e.g., from client to server to back-end services)
• Aligning trade-offs with the desired product goals and user experience
• Consider long-term maintenance of the system

Remember: Even if you’re not prompted to do so, the onus is on YOU to discuss your design on a more detailed level.

Senior-level considerations for System Design Interview questions

Let’s explore senior-level considerations for common design problems: Design Quora, Design WhatsApp, and Design Google File System (GFS).

1. Quora System Design

Quora is a social Q&A platform, designed to provide more in-depth answers than search engines.

Consideration 1 (Efficient and scalable search)

Key concepts: Performance, scalability, and fault tolerance.

Strategies

• Inverted index:
  • Use an inverted index data structure, which is used for information retrieval in search engines or other systems.
  • Implementing an inverted index using sharding can increase the indexing speed and query performance.
  • Applying real-time time indexing enables us to search new data quickly, while caching frequently accessed data can reduce the query handling time.

• Distributed search frameworks:
  • To enhance fault tolerance and scalability, we can utilize frameworks such as Elasticsearch or Apache Solr to handle large datasets with low latency.
  • Elasticsearch and Solr use advanced indexing and search algorithms that optimize query performance and resource usage.
• Advanced ranking algorithms:
  • Incorporating algorithms such as Gradient-Boosted Decision Trees (GBDTs), collaborative filtering, and transformer models, can help the system achieve optimal performance and search relevance.
  • These algorithms are considered advanced due to their ability to model complex patterns in data, handle large-scale datasets efficiently, and improve predictive accuracy through techniques like boosting, latent factor modeling, and attention mechanisms.

Consideration 2 (Maintaining data consistency)

Key concepts: Consistency, low latency, and availability.

Strategies

• Multi-region replicated databases
  • Use databases like Google Spanner or DynamoDB Global Tables to replicate data global data replication and distribution, significantly enhancing system performance.
  • These databases offer high availability by replicating data across multiple regions, ensuring that even if one region experiences a failure, others can continue to serve requests.
  • Low latency is achieved by routing user queries to the nearest available region, reducing the distance the data travels and speeding up response times.
• Consistency models:
  • We must apply eventual consistency models where low latency is crucial, and the inter-region updates propagate asynchronously.
  • Crucial updates, such as financial transactions for exclusive content and critical user updates, should adopt strong consistency models.
• Distributed caching:
  • Using Redis or Memcached for storing frequently visited content closer to users reduces user-perceived latency. These caches store data in memory, allowing for much faster data retrieval than disk-based storage.
  • By placing frequently accessed content in a cache, the system can quickly serve user requests without repeatedly querying the primary database, improving response times and overall user experience.
• Conflict detection and resolution:
  • Adopt mechanisms to reduce conflict occurrences in concurrent updates across regions—for example, by providing comments or upvotes. Algorithms like Lamport Timestamps, Vector Clocks, and Conflict-Free Replicated Data Types (CRDTs) can be employed.
  • Lamport Timestamps ensure a total ordering of events in a distributed system, simplifying conflict detection but requiring careful synchronization.
  • Vector Clocks provide a way to track causality between events, offering a more detailed conflict detection mechanism but with increased complexity.
  • CRDTs automatically resolve conflicts using mathematically guaranteed convergence properties, making them highly efficient for operations like counters and sets, though they might be complex to implement for all data types.
• Database sharding:
  • To reduce load on a single server, databases should be partitioned among regional shards. This will minimize latency in accessing the content for the same users near a region.
• Backup and disaster recovery plans:
  • Backup systems and disaster recovery plans should be in place to recover data in large-scale failures or catastrophic events.
  • The systems should have monitoring, alerting, redundant data centers, and availability zones in different regions to redirect traffic toward them in disaster occurrences in a region.

Consideration 3 (Distributed data storage)

Key concepts: Distributed databases, availability, fault tolerance, and performance.

Strategies

• Horizontal scaling and data replication:
  • Implementing horizontal scaling and data replication across the data center ensures high data availability and allows incoming requests to be distributed among different database servers that enable fast read/write operations.
  • Similarly, providing asynchronous updates for non-critical data speeds up the query processing time.

• Hardware redundancy:
  • This involves employing redundancy at the hardware level, such as a redundant array of independent disks (RAID) for storage and redundant network connections, to avoid a single point of failure.

• Distributed caching via consistent hashing:
  • Use a distributed cache to store frequently accessed data.
  • Consistent hashing should be applied to redundant cache servers to reduce load on a single server and the database.
• Data indexing:
  • The data and frequently queried fields should be indexed to increase the read performance.
  • For example, indexing the title and tag fields of a question in a search engine enables fast search queries.
• Disaster recovery plans:
  • Disaster recovery plans and an automated failover mechanism should be in place to maintain the data’s availability.

2. WhatsApp design

WhatsApp is a live messaging application similar to Facebook Messenger.

Consideration 1 (Reliable message delivery)

Key concepts: Reliability and exactly-once semantics.

Strategies

• Globally unique identifiers (GUIDs): Use GUIDs from a global sequencer to track message lifecycle and ensure unique delivery.
• Pub-sub system: Store messages with a “pending” status for durability. Messages are processed by idempotent receivers that use deduplication techniques (local cache or database table) to avoid reprocessing.
• Two-phase acknowledgment: Implement a two-phase acknowledgment system where the recipient confirms receipt, and this acknowledgment is stored reliably. This ensures a message is marked as delivered only once.
• Retry mechanism and dead letter queue: Handle delivery failures with retry logic and queue failed messages for later processing.
• Consensus algorithms: Use Paxos or Raft for node consistency across distributed servers to ensure consistency across distributed nodes, wherein all nodes agree on the state of messages and acknowledgments.

Consideration 2 (Real-time typing indicators and read receipts)

Key concepts: Latency and availability.

Strategies

• WebSocket connections:
  • Establish persistent WebSocket connections to enable immediate, real-time updates for typing indicators and read receipts without repeated HTTP requests.
• Event-driven design:
  • Use event-driven design to send “typing” and “read” signals only when there’s a change in status.
  • This approach reduces server load by moderating updates to only necessary events.
• Pub-sub system:
  • Use a pub-sub system or messaging queue to decouple services, which enhances scalability and reduces latency.
• Signal moderation:
  • To mitigate server load, these signals can be moderated, delivering updates only when there is a change in typing status (such as starting or stopping typing).
• Handling multiple concurrent connections:
  • A “read” signal is sent to the server once a recipient opens a message. Subsequently, the server updates the message status in the database and notifies the sender’s client through WebSocket.
  • Both processes can be managed effectively using a lightweight event-driven system like Node.js to ensure streamlined handling of multiple concurrent connections.

Consideration 3 (Handling message storage and synchronization for offline users)

Key concepts: Durability and synchronization.

Strategies

To handle frequent disconnections and reconnections of a user, it is important to implement a combination of reliable storage message queuing and synchronization strategies.

• Reliable storage:
  • Store offline messages in databases like DynamoDB or Firestore, chosen for their scalability and real-time synchronization across regions.
• Temporary storage and delta synchronization:
  • Use a pub-sub system to temporarily store messages for offline users.
  • Upon reconnection, retrieve stored messages and apply delta synchronization to sync only new or updated messages, minimizing server load.
• Batch synchronization:
  • Synchronize messages in batches to further reduce server requests and improve performance.
• Acknowledgment for consistency:
  • Ensure consistent message status across devices by sending acknowledgments for each delivered message.
  • This helps maintain accurate message delivery information even during frequent disconnections.

3. The Google File System Design

Google File System (GFS) is a distributed file system for handling substantial amounts of data through clusters of commodity servers.

Consideration 1 (Data replication to balance consistency, availability, and performance)

Key concepts: Consistency, availability, and performance

Strategies

• Primary-secondary replication model: Use a primary-secondary model for replication, where the primary node handles initial writes and then propagates changes to secondaries to ensure strong consistency.
• Lazy replication: Improve write performance by allowing acknowledgment after the primary and some secondaries commit. This reduces latency by avoiding synchronous updates across all replicas.
• Garbage collection and re-replication: Regularly clear unused data and replicate chunks from failed nodes to ensure data integrity and availability.

Consideration 2 (Optimal chunk size for file storage)

Key concepts: Performance and space optimization.

Strategies

Deciding the perfect chunk size within GFS is like finding a needle in a haystack—it requires a combination of factors to consider.

• Chunk size selection:
  • Balance performance and storage efficiency by choosing an appropriate chunk size (e.g., GFS’s default is 64MB).
  • Larger chunks simplify management by reducing the number of chunks, but smaller chunks can improve parallel processing.
  • One must dissect workload characteristics like average file sizes, access frequencies, and read/write ratios to identify the optimal chunk size. By comparing different chunk sizes against these workload metrics, we can identify the sweet spot that minimizes metadata overhead and balances performance and space efficiency.
• Parallelism vs. metadata overhead:
  • Larger chunks reduce metadata but may lead to storage inefficiency for smaller files.
  • Adjust chunk size based on workload, with larger sizes favoring data-heavy tasks and smaller sizes for high parallelism needs.

Consideration 3 (Load balancing for distributed read/write operations)

Key concepts: Removing single point of failure, high availability, and performance.

Strategies

• Real-time monitoring and chunk assignment:
  • Use the primary server to monitor chunk server performance and assign requests to less-burdened servers, preventing congestion.
• Locality-aware routing:
  • For read-heavy operations, route requests to the nearest or least busy server to reduce latency and optimize throughput.
• Chunk leasing for write operations:
  • Implement a short-term leasing system on the primary server to coordinate writes, avoiding conflicts and ensuring load is evenly distributed across chunk servers.
• Dynamic reallocation:
  • Periodically monitor chunk server load and reallocate chunks from overloaded servers to underutilized ones to maintain balanced resource consumption.

Claiming your spot as a senior level engineer

Unlike junior or mid-level engineers, senior engineers have to apply in-depth technical knowledge to lead projects, mentor juniors, and solve complex problems. To claim your spot as a senior engineer, you need to show that you’re ready for the high-stakes nature of senior roles.

To make sure you can demonstrate the right level of knowledge in the interview, I recommend you build a strong foundation in distributed systems, including:

• Distributed file systems
• Distributed databases
• Key-value stores
• Concurrency management
• Big-data processing systems
• Consensus algorithms

The following illustration depicts the main topics of each of the areas I mentioned above:

If you want to dive deeper into the design problems and concepts we discussed today, you can find them in Educative’s interactive courses, Grokking the Principles and Practices of Advanced System Design.

Good luck interviewing!