The Top 6 System Design Interview Questions Engineers Should Prepare For

System design questions have become a standard part of the engineering interview process at companies like Google, Facebook, Amazon, and Microsoft These complex and open-ended questions aim to test candidates’ technical knowledge, problem-solving skills, and communication abilities when it comes to designing large-scale systems

With the competitiveness of landing a job at a top tech firm it’s important for engineers to thoroughly prepare for system design interview questions. In this article we’ll explore the top system design questions engineers frequently face during interviews and provide tips to help you demonstrate your skills when encountering similar questions.

What is CAP Theorem?

The CAP theorem, also known as Brewer’s theorem, is a fundamental concept in building distributed systems. It states that for any shared data system, only two of the following three properties can be guaranteed:

• Consistency – All nodes in the system see the same data at the same time.
• Availability – Every request receives a response, even if some nodes are down.
• Partition Tolerance – The system continues operating even if some nodes fail or the network partitions.

Understanding the implications of the CAP theorem is key to making appropriate trade-offs in system design. For example, heavily distributed systems like Cassandra sacrifice consistency for availability and partition tolerance. On the other hand, consensus-based systems like Zookeeper maintain consistency at the cost of reduced availability in certain failure scenarios.

When answering CAP theorem questions, be sure to explain the 3 properties and the inherent trade-offs involved in real-world systems. Use relevant examples to demonstrate your understanding.

How is Horizontal Scaling Different from Vertical Scaling?

Scalability is a crucial consideration when building any large-scale system. Horizontal and vertical scaling represent two main approaches:

• Horizontal scaling means increasing the number of nodes in the system, like adding more servers. It allows linear scalability but can add complexity.

• Vertical scaling means increasing the resources of an individual node, like upgrading to a more powerful server. It has hardware limitations but is simpler to implement.

Key differences:

• Horizontal scaling is more cost-effective and provides higher availability through redundancy. Vertical scaling has hardware constraints and single points of failure.

• Horizontal scaling allows incremental growth through the addition of commodity servers. With vertical scaling, growth happens in large chunks by upgrading hardware.

• A horizontally scaled system is often more complex to design and manage. Load balancing and data distribution introduces challenges. Vertical scaling has simpler architecture.

To demonstrate your understanding, use specific examples like scaling a web application tier or database to highlight when horizontal vs vertical scaling is appropriate. Discuss the trade-offs involved.

What is Load Balancing?

Load balancers distribute network or application traffic across multiple servers to optimize resource utilization, maximize throughput, minimize response time, and avoid overload. Key concepts include:

• Load balancing helps eliminate single points of failure and bottleneck servers by spreading loads across redundant resources.

• Load balancers can route traffic based on different algorithms and metrics like random selection, least loaded, round robin, etc. to achieve an optimal distribution.

• Load balancers require health checks to detect server failures and shift traffic accordingly. Common approaches include heartbeats, status endpoints, and active monitoring.

• Load balancers provide benefits like increased scalability, flexibility, and availability. Trade-offs include increased cost and complexity.

When answering load balancing questions, use specific examples like balancing web servers or implementing failover. Discuss appropriate algorithms and health checks given a particular use case.

What is Latency, Throughput, and Availability of a System?

Understanding system performance metrics is key to making design decisions:

• Latency is the delay or time taken to respond to a request. It impacts user experience.

• Throughput is the number of requests a system can handle per second. It impacts scalability.

• Availability is the percentage of time a system remains operational. High availability is crucial for mission-critical systems.

These metrics often trade off against each other. For example, strong data consistency improves latency but lowers availability. Replication provides higher availability but reduces throughput.

When asked about performance, clearly explain the relevance of each metric to the system’s goals. Use examples to demonstrate how specific designs impact latency, throughput, and availability differently.

What is Sharding?

Sharding is a database partitioning technique that splits large datasets across multiple database servers called shards. Key concepts:

• Sharding enables horizontal scaling of databases by distributing load across servers.

• Sharding requires a shard key to determine how data is distributed. Common approaches involve hash- or range-based partitioning.

• Query routing and aggregation across shards can introduce complexity. Trade-offs exist with join operations.

• Rebalancing shards as data volumes change can be challenging. Schema and data movement must be handled carefully.

When asked about sharding, focus on issues like choosing an appropriate shard key, handling queries across shards, and schema changes. Provide examples relevant to the database or use case being discussed.

How are NoSQL Databases Different from SQL Databases?

While SQL databases rely on predefined schemas, NoSQL databases use more flexible data models and are optimized for specific use cases:

• NoSQL databases like Cassandra and MongoDB offer high scalability and availability by sacrificing strong consistency.

• Data is typically modeled differently in NoSQL. Key-value, document, and graph formats are common rather than tabular relations.

• NoSQL queries are focused on document lookups and simple CRUD operations. More complex joins and aggregations require compute-heavy application logic.

• NoSQL databases can leverage cheap commodity hardware via sharding and replication. Product-specific expertise is often required.

When comparing NoSQL and SQL, identify the trade-offs involved and discuss which approach makes sense for a given use case. Cite examples like Cassandra for time series data or MongoDB for storing semi-structured application data.

Key Takeaways

• Understand the fundamentals behind system design concepts like CAP theorem, scaling, caching, and sharding.

• Analyze the trade-offs involved when making decisions around consistency, scalability, performance, and fault tolerance.

• Use specific examples relevant to the question and engage the interviewer to clarify ambiguous requirements.

• Discuss alternate approaches and optimizations while explaining your design thought process clearly.

With the right preparation on common system design concepts and a structured approach to answering open-ended questions, engineers can master the system design interview. Companies are evaluating not just your technical knowledge, but also your problem solving skills and communication ability while designing complex systems with real-world constraints. Preparing explanations related to key topics like distributed systems trade-offs, database selection, scaling, and system performance will help demonstrate your capabilities during the system design interview.