5 Netflix System Design Interview questions to master in 2024

Working in FAANG/MAANG is a dream for many software engineers. However, successfully breaking in can be a roller coaster ride, especially at Netflix, which is known for having some of the toughest interviews in Big Tech — especially for the System Design round.

In a lot of ways, Netflix engineers wrote the book on modern System Design. Now that Netflix is such a fixture in the modern streaming landscape, it’s easy to forget just how unprecedented Netflix’s pivot from a mail-based DVD distributor to one of the world’s most innovative tech companies really was. In fact, many of the practices they implemented to scale Netflix to accommodate millions of daily active users streaming billions of hours of high-quality video have since been adopted by many of the world’s largest media companies. So, when it comes to hiring new software engineers who are capable of contributing to such an impressive distributed system, the bar is set very high.

Today, let’s break down the unique approaches that make Netflix’s System Design so influential. In doing so, we will unlock exactly what it takes to be successful in the Netflix System Design Interview.

Here’s what we will cover:

1. Deep dive into Netflix architecture and tech stack

2. Breaking down the top Netflix System Design Interview questions

3. Insider’s strategies for success in System Design Interviews

Let’s dive in!

Netflix’s architecture & tech stack: A deep dive#

A highly scalable and reliable tech stack is absolutely critical for handling the immense traffic and data Netflix processes every day. Netflix has developed its tech stack accordingly.

Note: Netflix developed its own services, such as Eureka, used for service discovery; Hystrix, used as a circuit breaker; Ribbon, used for load balancing; and EVCache, used for in-memory caching, to meet the scalability and availability challenges.

Let’s discuss the architectural challenges that Netflix faces in its products.

Architectural challenges at Netflix#

Netflix operates at a scale and complexity that presents unique technical challenges.

Note: According to Netflix, as of Q1 2024, 260 million people are subscribers. Assuming a conservative average of 2 people per subscription, this means more than 500 million users.

Scaling the system, making it available, providing the best user experience, and streaming data quickly to this huge number of users present challenges. Netflix tries to solve such challenges with the following architectural components that work together to deliver a seamless user experience:

• Microservices architecture: Netflix pioneered adopting microservices instead of monolithic ones to scale the service. The application is broken into smaller services, independently handling tasks such as authentication, recommendation, streaming, payment, etc. Understanding how to design and manage microservices is critical to ace the System Design.

• Content delivery network (CDN): Netflix developed Open Connect, a global CDN that ensures efficient, high-quality video streaming. If asked during your interview, you should be able to design a scalable and reliable CDN.

• Recommendation system: The recommendation engine is the key component of Netflix’s architecture, providing personalized content suggestions to users. You should know about machine learning and data processing to analyze user behaviors and preferences and design an efficient recommendation system.

Along with the main components above, an efficient storage and caching system is crucial for the success of Netflix’s streaming service. The interviewer can examine the details of these specific components to analyze your suitability for the role.

Get yourself familiar with Netflix’s interview process, as illustrated below:

Note that in interview loop rounds (I and II), a director is part of the interview team and is responsible for thoroughly assessing a candidate.

System Design Interviews at Netflix#

At Netflix, the coding interview is important. The behavioral interview is even more important. But the most important interview loop for hiring decisions? It's the System Design Interview.

Just as Amazon loves leadership principles and, Google's coding interview reigns supreme, Netflix is all about System Design. Even in coding interview rounds, they sometimes prefer to ask questions involving System Design. For example, interviewers may ask you to map your code solution to a real-world system and explain the system’s efficiency with the defined solution.

Based on the defined tech stack and structure, Netflix’s primary goal is to ensure its service is highly scalable and available. Leveraging a microservices architecture, powerful data processing tools, advanced caching solutions like EVCache, and robust databases such as Cassandra and DynamoDB support my opinion.

In Netflix’s tech stack, you can notice that Netflix uses GraphQL along with REST APIs. API design is another aspect of System Design interviews that the interviewers ask at FAANG and especially at Netflix. Netflix employs microservices architecture, and designing an effective API for effective communication between microservices is crucial. So, understanding how to design such effective, scalable, and easy-to-use APIs will benefit you in a Netflix System Design Interview.

Note: The interviewer can ask questions about APIs, such as what benefits GraphQL brings over RESTful APIs. The course Grokking the Product Architecture Design Interview addresses how to design effective APIs for different services.

The System Design Interview at Netflix will likely focus on the following nonfunctional requirements of the system:

• Availability

• Scalability

• Security

• Low latency

If we had to prioritize just one of these nonfunctional requirements, Netflix will do anything to everything to make the service available. That means interviewers will be expecting to hear from you to ensure availability (as well as scalability) during the interview.

The questions would likely be unique at Netflix than at other FAANGs, mostly focused on the aforementioned aspects of the system. For example, they’ll ask about securing the service across global users and dive deep into details about this concern throughout the interview.

Note: The interviewers will likely ask questions about their work. It’d be great to research the product or problem ahead of time and be prepared to provide valid and efficient solutions.

Top Netflix System Design Interview Questions #

Like at other FAANG companies, Netflix interviewers are less interested in asking product-focused questions, such as design a newsfeed service. But this doesn’t mean they won't ask these questions.

This round is unique and challenging, asking for bespoke questions from the candidate. However, a good starting point would be to go through the following table that compiles the more generic System Design Questions typically asked at FAANG companies:

The above table contains product-focused questions you should prepare, considering that the interviewer may ask such questions.

For this blog, we’ll focus on Netflix’s product-specific questions — the most important of which are listed below:

Now let’s explore answers to 5 of the most essential questions mentioned above.

1) Design a scalable CDN#

A content delivery network (CDN) ensures a seamless user experience while streaming movies or TV shows. Let’s understand how it works.

Architecture#

Netflix’s Open Connect CDN distributes content from its data centers to end users through a hierarchical Open Connect Appliances (OCAs) network. Content is stored in Netflix data centers, distributed to central OCAsCentralized server that stores the original version of the content and handles initial requests., propagated to regional OCAs, and finally to edge OCAsThese are distributed servers placed closer to users, typically within or near to the ISP network. located within or near ISPInternet service provider networks. This structure, along with peering connections using ISPs, ensures that content is delivered efficiently and with minimal latency to end users via the nearest edge OCA, providing a scalable and high-quality streaming experience.

The following illustration shows how content propagates from the source to end users:

The following questions can arise during the design:

• How would you determine which content to be cached on edge servers?

• How would you distribute traffic evenly across multiple edge servers?

• How would you ensure the CDN infrastructure’s scalability, availability, and fault tolerance?

• How would you optimize the delivery and reduce the latency while streaming?

Let’s discuss the solutions to the aforementioned questions.

Caching#

After strategically analyzing and placing edge servers, the next thing is to implement a System Design caching strategy for the edger servers. We can implement a multi-tier cache strategy where content is first cached in edge servers and then in regional caches before fetching from the origin servers. This will help the CDNs scale and reduce latency, as most requests will be processed with cache data.

We should effectively apply cache eviction policies such as least recently used (LRU)A cache eviction policy to remove the least recently accessed data, considering it’ll also not be used in the near future. and least frequently used (LFU)A cache eviction policy that evicts data that is least frequently used, keeping in mind that data with less access frequency are less likely to be used.. This ensures that frequently accessed content stays in the cache long. We should also implement precaching/predictive caching based on viewing patterns and preload popular content before anticipated spikes, such as new releases. This ensures that systems remain available for newer requests.

Scalability and availability#

We should use load balancers to smartly balance the load on edge servers. We can also dynamically manage traffic load and redirect traffic amid outages or congestions. We can also use redundancy by replicating content and implementing a failover mechanism that automatically switches to backup servers (edge or origin servers). These all, plus the auto-scaling feature, will ensure the service’s availability and scalability.

We can use adaptive bit-streaming to optimize content delivery and lowering latency according to the network conditions.

Note: Review our detailed chapter on the design of a content delivery network for in-depth understanding.

2) Design a fault-tolerant streaming service#

We’re familiar with streaming services like Netflix, YouTube, Amazon Prime Video, etc., and we know how they work regarding their System Design. Netflix must be able to negotiate the challenge of remaining highly available amid an increasing number of users.

Netflix interviewers will want to hear from you about which strategies you would use to make the system highly available and fault-tolerant. You can go ahead with the following strategies:

• Distributed architecture: We can distribute and replicate data and resources across multiple servers and geographic locations to avoid a single point of failure (SPOF). If one server or data center fails, others can seamlessly take over, ensuring availability.

• Load balancing: We can use localLocal load balancers manage traffic within a specific region, ensuring even distribution and preventing overload on any single server. and global load balancersGlobal load balancers distribute traffic across different regions, directing users to the best-performing data center. (LB) to distribute the incoming requests to the least-loaded and nearest servers of a specific region. This combined approach ensures continuous availability and quick redirection in case of failure.

• Databases: We can use databases like Cassandra or DynamoDB to ensure scalability and availability. Cassandra’s distributed architecture provides high availability and fault tolerance, allowing seamless scaling across multiple data centers. DynamoDB offers automatic scaling and low-latency performance, easily handling massive amounts of data.

• Monitoring and failover mechanism: We can implement a monitoring and health check mechanism on servers to track their status and performance through heartbeat signalsServers regularly send heartbeat signals to indicate they are operational. If a server stops sending these signals, it is marked as down.. A failover mechanism is triggered whenever a monitoring system detects anomalies, such as missed heartbeats or errors. The load balancers usually redirect traffic from the failed server to other redundant, available servers.

• Data replication and sharding: Data replication and sharding can also help in availability by creating copies of data for backup and splitting data across multiple databases or servers to distribute the load.

• Caching: We can also leverage caching to store frequently accessed data in the cache memory. It serves data directly without requesting servers, ensuring they remain available for critical requests.

We can use adaptive bit streaming protocols like HTTP live stream (HLS) or dynamic adaptive streaming over HTTP (DASH) to adjust video quality based on network conditions. We can also fetch videos in chunks, improving fault tolerance as only a small part needs to be re-fetched in case of a failure.

Note: Explore the System Design of YouTube to better understand how we can make a streaming service highly available.

3) Design a personalized recommendation system#

Providing a personalized experience is key to success for streaming services like Netflix — and an aspect of modern System Design that Netflix truly helped to pioneer. A recommendation system ensures the provision of personalized content to each user. We can say that it is a system that employs smart algorithms to recommend content. Now, the following questions can arise while designing a recommendation system:

• What data will you use to build user profiles for recommendations, and how will you collect it?

• Which recommendation algorithms will you use?

• How will you handle the cold start problem for new users and content?

• How would you update recommendations in real time?

• How would you ensure the recommendation system scales to ever-increasing users?

Let’s break down the answers to these questions.

Data collection#

The recommendation system uses a data collector service that collects data based on user interaction from application servers, such as viewing history, ratings, search queries, watch times, etc. The collector service logs this data in a real-time messaging system such as Kafka or AWS Kinesis to make it available for immediate processing.

On the content side, the title’s metadata, such as genre, categories, actors, tags, release year, etc., is stored and continuously updated in a central database.

Note: As Netflix states, they also use data such as the time of day a person is watching, the preferred language, the device, and how long a person watched a specific title for contextual recommendations.

Data processing#

Once the data is collected and logged in the messaging system, it is processed through a real-time or batch processor. Real-time processing systems, such as Apache Storm or Spark streaming, immediately process users’ interactions, update their profiles, and generate real-time recommendations.

Batch processors are used for more detailed analysis, periodically processing data to improve the accuracy of the recommendation systems. The processed data is then stored in a central scalable database such as Apache Cassandra or Amazon S3.

Content recommendation#

The core of the recommendation system relies on algorithms to generate personalized suggestions. An AI systemIt is a distributed framework like Apache Spark to train recommendation models on large dataset to recommend content. can use a combination of collaborative filteringA filtering technique to recommend content based on the behavior of similar users or items., content-based filteringA filtering technique to recommend content based on similarities between items or content based on metadata and content features., and sometimes a hybrid approach to accurately recommend the content.

The AI system also employs advanced techniques like matrix factorization and deep learning models to analyze factors that influence users’ preferences for better content recommendations. Lastly, the recommendation service generates a ranked list of content for each user based on the processed data and algorithms’ results, which are sent to the users.

When there are concerns about a system’s scalability, we can always leverage horizontal or vertical scaling of the resources, distributed systems, microservices architecture, and caching content where feasible.

4) Design a low-latency streaming service#

A common focus in Netflix software engineering interviews is not just on designing a robust streaming service but also on ensuring it’s fast and buffer-free for end users. Interviewers want to hear about the strategies you choose to employ to guarantee low-latency streaming.

5) Design an advanced search system with autocomplete for Netflix#

The next feature the interviewer would focus on is designing an advanced search system with autocomplete for Netflix. It involves instantly suggesting relevant content as the user types. The system should leverage indexing, intelligent ranking algorithms, and personalized recommendations to ensure accurate results.

Insider tips for Netflix System Design Interviews#

The only way to feel comfortable in a System Design Interview is to get plenty of practice. Based on my experience conducting interviews at Meta and Microsoft, I will now share a structured approach to help you prepare for your interviews. (This is essentially how I would recommend that candidates prepare before their Facebook SWE interview).

Before the interview#

Whether you’ve submitted the application or are considering applying, you should start preparing for the interview as soon as possible.

• Familiarize yourself with the basic concepts of distributed systems. I’ve prepared a guide to ace the System Design Interview to help you understand the basic concepts and approaches to solving a large-scale system. After that, you should learn about components of systems that together make large-scale systems.

• The next step is to analyze your concepts through real-world problems. You should read my list of the top 25 System Design Interview questions, where I discussed tried and true approaches to common System Design problems.

• Now, if you can, you should try to benchmark your skills with a mock interview. Educative’s AI mock interview tool will get you hands-on with real-world design problems — and give you personalized feedback based on your strengths and skill gaps.

You can repeat the above steps to ensure you’re fully prepared before the interview.

During the interview#

Having technical System Design knowledge is one thing. Confidently presenting your knowledge during the interview is another. Be sure to account for the following during your interview:

• Interaction and communication is a key to your success during the interview. Engage your interviewer in technical discussion, ask clarifying questions, and demonstrate your thought process.

• Companies like Netflix are highly customer-focused. Always tie your solutions and trade-offs back to the user experience. You can discuss how your design choices impact performance, availability, and usability for the end-users.

• Always expect your interviewer to dive deep into a specific aspect of your design. Be prepared to discuss and showcase your expertise.

After the interview#

After your interview, take some time to reflect on your experience. There is always something to learn whether you clear the interview. You can take those learnings to your next interview in the same company. If possible, seek feedback from the interviewer to understand areas of improvement.

Final thoughts: How to get hired at Netflix#

Netflix interviewers are famous for throwing curveballs with their unique System Design Interview questions. If there’s one word of advice I can share, it’s to not ignore questions specifically focused on the Netflix product. This will enable you to be prepared to design optimal solutions to bespoke (and even on-the-fly) questions and follow-ups.

If you are interested in taking your System Design skills to the next level, I highly recommend Educative’s popular Grokking Modern System Design course. It’s a great resource for solidifying your System Design fundamentals and gauging your understanding of real-world design problems:

More so than any other FAANG company, the System Design Interview questions at Netflix might appear especially daunting. But with a strategic approach to preparation, I am confident that you will be successful.

Good luck with your interview — and happy learning!