Designing a Netflix-Like Streaming Service for System Design Interviews

Designing a Netflix-like video streaming service is a popular system design interview question because it touches on many fundamental concepts of scalable distributed systems.

Streaming platforms must handle massive scale – Netflix, for instance, serves hundreds of millions of users worldwide, streaming huge volumes of video content every day.

This means engineers must tackle challenges of storing and delivering large video files, ensuring low buffering and high quality, and keeping the service reliable and available to users 24/7.

In such an interview scenario, you’re expected to outline how you would build a large-scale video streaming platform.

This includes discussing how users upload or ingest video content, how that content is processed and stored, and how it’s efficiently delivered to millions of viewers.

You’ll need to address both the functional requirements (what the system should do) and the non-functional requirements (how the system performs under various conditions).

Let’s break down the problem step by step.

Understanding System Requirements

Before diving into architecture, clarify the system’s requirements:

Functional Requirements: At a minimum, our Netflix-like service should allow:

• Video Uploading: Content creators or admins can upload video files to the platform.

• Video Streaming Playback: Users can stream videos on-demand seamlessly on various devices (web, mobile, smart TV).

• Searching and Browsing: Users can search for videos by title/genre and browse through categories or recommendations.

• User Account Management: Users should be able to sign up, log in, and manage their profiles/subscriptions.

(We can also mention features like rating content or a recommendation system, but for a beginner-friendly scope, the focus is on core streaming functionality.)

Non-Functional Requirements: The system needs to be:

• Scalable: Able to handle millions of users and a high volume of concurrent video streams. The service will be read-heavy – one video upload could translate to thousands of view streams – so it must scale horizontally to serve many requests in parallel.

• Highly Available: The service should have minimal downtime. Users around the globe should reliably access content anytime. Redundancy and fault tolerance are important (e.g. if a server or data center fails, the system stays up).

• Performant (Low Latency): Videos should start playing quickly and stream smoothly. Low latency is crucial for user experience – buffering or slow load times will drive users away. The system should use techniques (like caching and CDNs) to minimize latency.

• Durable and Fault-Tolerant: Video data (which is often large) must be stored safely, with backups or replication to prevent data loss. The system should handle hardware failures, network issues, and spikes in traffic gracefully (perhaps via load balancers and auto-scaling).

• Maintainable & Extensible: (For real-world design) Using a modular or microservices architecture can help different teams develop and scale components independently. This is how Netflix’s actual system evolved – they use microservices for authentication, streaming, recommendations, etc., instead of a single monolith.

Expected Traffic & Scale Considerations

In an interview, you might estimate the scale to inform your design decisions.

For example, imagine having tens of millions of daily active users and thousands of new videos uploaded each day. The system could see peak loads of hundreds of thousands of concurrent streams.

With such numbers, you’d note a high read-to-write ratio: far more people watch videos than upload. This justifies investing in caching and replication for reads.

You’d also consider geographical distribution – users are worldwide, so the service should deliver content from regions closest to the user (to reduce latency).

These considerations will influence choices like database type, caching, and use of a Content Delivery Network.

High-Level Architecture Overview

At a high level, a Netflix-like streaming service consists of clients interacting with a backend composed of multiple specialized components.

Here’s an overview of the main pieces and how they fit together:

• Client Applications (Frontend): These are the Netflix apps or website that run on user devices (phones, TVs, browsers). Clients allow users to browse the catalog and initiate video playback. When a user presses “Play,” the client app sends a request to the backend to start streaming the video.

• Backend Services (Application Servers): The backend includes various services (possibly microservices) that handle different responsibilities:

  • An API Gateway or Web Server receives requests from clients and routes them appropriately (for example, route authentication requests to the Auth service, video upload requests to the Upload/Encoding service, etc.).

  • User Service & Authentication: Manages user accounts, login, and subscription status. Issues authentication tokens so that other requests can be validated.

  • Video Catalog Service: Manages video metadata (title, description, file locations, etc.), and handles search queries. It interacts with the database storing metadata.

  • Streaming/Content Service: Handles generating the streaming URLs or directing the client to the appropriate content server or CDN location for video playback.

  • Encoding/Processing Service: Handles processing of uploaded videos (more on this later).

• Data Storage Systems: This includes:

  • Video Storage (Object Store/Database): A storage service for the video files themselves. Videos are typically stored as large binary files in a distributed storage system (like Amazon S3 or a similar blob storage) rather than in a traditional relational database. This storage must handle petabytes of data and support high-throughput reads for streaming.

  • Metadata Database: A database for all the metadata – information about videos (IDs, titles, genres, thumbnails, lengths), user info, watch history, etc. This could be a combination of a relational database (SQL) for structured data and transactions, and a NoSQL store for scalability. For example, Netflix uses Apache Cassandra (a NoSQL database) for some metadata because it can handle heavy read/write loads across regions . The metadata DB is usually designed to be highly available and partitioned (sharded) to scale with the number of videos/users.

• Caching Layer: To meet performance requirements, critical data is often cached. Two main types of caches in a streaming service:

  • Content Cache (CDN): This is a network of distributed servers that cache video content closer to users. We’ll discuss CDNs in detail below – it’s essential for streaming performance.

  • Metadata Cache: Frequently accessed metadata (like the homepage list of popular movies, or a video’s details) can be cached in-memory (using something like Redis or Memcached) to avoid hitting databases for every request. This provides quick responses for API calls that load the browsing screens or video information.

• Content Delivery Network (CDN): The CDN is a geographically distributed layer of caching servers that deliver videos to users from the closest location. When a user streams a video, ideally it’s served from a local CDN node rather than a distant central server, reducing latency and buffering.

• Load Balancers: At various points, load balancers are used to distribute traffic evenly. For example, a load balancer can sit in front of the application servers so that incoming requests are split among multiple servers. Similarly, CDNs and database clusters also use load balancing to handle many simultaneous connections. This ensures scalability, as we can add more servers behind a load balancer to handle growing traffic.

All these components work together as follows: a user’s request goes from their client to the backend (through load balancers) which authenticates the user, fetches needed metadata, and then directs the client to fetch the video from the CDN.

Meanwhile, content upload flows go through the encoding pipeline and populate the storage and CDN for future viewers. Next, let’s dive deeper into each part of the system design.

Step-by-Step System Design

Now we’ll break down the key components and design decisions in building our streaming service:

Data Storage for Videos

One of the first challenges is where to store the video files. Video files are large (potentially hundreds of MB or several GB each), and the system will accumulate a huge library over time. Storing this data efficiently and durably is crucial.

Object Storage / Blob Store

We typically use a distributed object storage system to save videos.

Examples include cloud storage services like AWS S3, Google Cloud Storage, or Azure Blob Storage, or a self-hosted distributed file system.

Object storage is ideal for large binary files – it can scale virtually infinitely and manage data replication under the hood. We can store each video under a unique key or URL.

For instance, when a video is uploaded and processed, it might be stored as multiple files (one per quality/format) in a storage bucket. The system keeps references to those files (their URLs or IDs) in the metadata database.

Why Not a Traditional Database for Videos?

Traditional relational databases are not suited for storing huge binary files or serving them to millions of users.

Instead, we store only metadata (and maybe a pointer to the file’s location) in a database, and keep the actual video content in the object store.

Data Replication and Backup: To ensure durability, the storage system should replicate video files across multiple servers or data centers. That way, if one server or disk fails, the video isn’t lost. Cloud storage services automatically handle replication (e.g., S3 stores data across multiple availability zones). If building our own storage cluster, we’d store copies in multiple locations. We might also use a content checksum or ID to avoid duplicates and facilitate caching.

Serving Videos: While clients will mostly get videos via the CDN, the original storage still needs to supply data to the CDN nodes (when content is new or not yet cached at the edges). So, the storage should handle high read throughput. Using a distributed file store means we can have many storage nodes serving different chunks or files in parallel, avoiding bottlenecks.

In summary, the design would likely use a reliable, scalable object storage service for videos, optimized for high throughput. Each video upload results in stored files (say in multiple resolutions), and those are later fetched by CDN servers or directly by clients if needed.

Video Processing & Encoding Pipeline

When a new video (movie or episode) is added to the system, it usually cannot be immediately served as-is. Video processing (transcoding) is a critical step:

Encoding to Multiple Formats

Users’ devices and network conditions vary widely. To provide a smooth experience, we encode each video into multiple resolutions and bitrates.

For example, we might create a 1080p version, 720p, 480p, etc., each with a different bitrate. This allows adaptive streaming – the player can switch to a lower quality stream if the user’s bandwidth is low, or ramp up to HD when possible.

Netflix and YouTube use this approach: the uploaded source is converted into several files. For instance, a transcoding service might output a set of video files ranging from low quality (for mobile or slow connections) to high quality (for big screens).

Adaptive Streaming

Along with multiple encodings, the video is typically broken into small chunks (e.g., 2-10 seconds segments) rather than one big file.

Protocols like HLS (HTTP Live Streaming) or MPEG-DASH are standard – they create many tiny segment files for each quality level, plus manifest files (playlists) that tell the player how to fetch them.

The client can then download segment by segment and switch between quality levels on the fly. For our design, it’s enough to mention that we will segment the video and prepare streaming manifests, to enable smooth adaptive streaming.

Processing Workflow

We would likely have a background processing pipeline for uploads.

When an admin uploads a new movie, the file might first be stored in a temporary location. Then an Encoding Service (which could be a cluster of worker servers or a cloud service like AWS Elastic Transcoder) picks it up.

The video is encoded into the required formats and segments. We also generate additional assets like thumbnails (preview images) and maybe subtitle files if needed.

This process can be time-consuming (especially for long videos), so it’s done asynchronously.

Once complete, the processed files are saved to the video storage and also distributed to CDN edge caches (or ready to be pulled by CDN on demand).

Optimizing for Devices

We’d ensure the codecs and formats are widely supported.

For example, produce H.264/AAC encoded MP4 or HLS streams for broad device support. In advanced scenarios, newer codecs like HEVC or AV1 might be used to reduce file size (Netflix does use these to save bandwidth), but that’s more detail than needed for an interview unless asked.

Result: After encoding, we have multiple versions of the video. When a user hits “Play,” the system will direct them to the appropriate stream manifest. The player will start with a default quality and adjust using those prepared segments. The encoding pipeline ensures we balance quality and size – higher quality streams for those who can support it, and lower bitrate for those on slower internet or smaller screens.

Content Delivery Network (CDN) for Global Streaming

To achieve fast and reliable streaming worldwide, using a Content Delivery Network (CDN) is essential. A CDN is basically a network of servers distributed across many geographic locations, specialized in delivering content to users from the nearest node.

How CDN Works

Instead of every user fetching video directly from our central servers or storage (which might be in one region), the video content is cached on CDN servers around the world.

For example, if a user in London wants to stream a movie, there might be a CDN data center in London (or nearby) that can deliver the video to them much faster than a server in, say, New York. CDNs serve content via edge caches:

• When a video is first requested in a region, if the CDN edge doesn't have it cached, it will retrieve it from the origin (our storage or a central server), then save a copy locally.

• Subsequent viewers in that region will get the video directly from the edge cache, which is much quicker (lower latency) and reduces load on the origin.

CDN Providers vs. Custom CDN

Many companies use third-party CDN providers (like Akamai, Cloudflare, Amazon CloudFront, etc.) by uploading content to them or letting them fetch from the origin.

For an interview answer, you can say "Use a CDN to cache and deliver content" and that’s usually sufficient.

At extreme scale, companies consider building their own CDN. In fact, Netflix built a custom CDN called Open Connect – they place physical caching appliances inside ISPs around the world to localize traffic. This drastically lowers bandwidth costs and improves performance, because popular shows are served directly from ISP networks.

(For our design, we can simply state we’ll use a CDN, but noting Netflix’s approach shows you understand real-world optimizations.)

Benefits of CDN:

• Reduced Latency: Users get content from nearby servers, so video start time and buffering are minimized.

• Offload Traffic: The CDN carries most of the data traffic. Our core infrastructure (origin) sees fewer direct video requests, because the CDN caches take that load. This is critical when streaming high-bandwidth content like video.

• Scalability: CDNs are built to handle huge amounts of traffic and can automatically distribute load across many edge servers.

• Regional Failover: If one edge node is down, requests can be routed to another nearby node, improving reliability.

In our design, when a user requests a video stream, our backend might provide a URL that is actually a CDN URL (often with a token or authentication mechanism).

The client then fetches the video segments from the CDN. Meanwhile, we ensure that our origin is integrated with the CDN so that new or infrequently requested videos will be transferred and cached as needed.

Load Balancing and Traffic Management

To serve millions of users, we need multiple servers at every tier of the system. Load balancers ensure no single server becomes a bottleneck by distributing requests.

At the Application Layer: We can deploy a pool of application servers (for handling API requests, user interactions, etc.) behind a load balancer. Clients will hit a single endpoint (like api.netflix.com), and the load balancer will route each incoming request to one of the many servers available. This distribution can be based on least connections, response time, or simply round-robin. The goal is to keep all servers efficiently utilized and to allow scaling horizontally – if user traffic increases, we add more servers and the load balancer will include them in rotation.

Global Load Balancing: If our service is deployed in multiple regions (e.g., data centers in North America, Europe, Asia), we might also use DNS-based load balancing or geo-load-balancers to route users to the nearest regional cluster. For example, a user in Asia would be sent to the Asian data center’s servers by resolving to a different IP. This reduces latency and avoids transcontinental traffic.

Microservices and Internal Load Balancing: Within the backend, different services (e.g., the encoding service, search service, etc.) might also have their own clusters. We use load balancers or service discovery mechanisms so that one service can call another without hard-coding a single endpoint. Netflix’s real system uses something called Eureka for service discovery and Ribbon for client-side load balancing, among other tools, but in our simpler design we can assume standard load balancers.

Scaling and Fault Tolerance: Load balancers also help in failover situations. If one server goes down, the LB will stop sending traffic to it, automatically directing users to other servers. This improves fault tolerance. Additionally, modern architectures might auto-scale: e.g., if CPU use on servers is high, new server instances are spun up and the load balancer starts sending traffic to them. This elasticity ensures we can handle flash crowds (sudden surges in traffic).

In summary, load balancing is a fundamental piece that touches all parts of the system – user requests, service-to-service calls, and even database requests can benefit from it (databases often have read replicas behind a load balancer for read scaling). It enables the service to scale out and remain highly available under heavy load.

Metadata Storage and Search Service

Apart from the video files, our system needs to manage a lot of metadata and allow users to search or browse content. Let’s break this into two parts: metadata storage and the search functionality.

Metadata Storage: Metadata includes information about each video – title, description, genre, cast, duration, thumbnail URL, availability (which region or subscriber tier can watch it), etc. It also includes user data (profiles, watch history, favorites) and system data (like which videos are trending). We need a database that can handle frequent queries and updates to this information:

• A traditional approach is to use a relational database (SQL) like PostgreSQL or MySQL for metadata. This works well for ensuring consistency (e.g., when a new video is added, all its info is inserted in related tables). We can design tables for Movies, Users, Subscriptions, etc. However, as the number of records grows into millions and beyond, a single SQL DB can become a bottleneck.

• To scale, we could partition (shard) the relational database by some key (like video ID or region), and add replicas for read scaling. Many systems also use NoSQL databases for this layer, trading some strict consistency for massive scalability. For instance, a NoSQL document store or wide-column store can handle high throughput. Netflix in reality leverages Cassandra heavily for user viewing history and other metadata because it’s distributed and can scale horizontally. For our design, we might say: use a relational DB initially for simplicity, but if we expect huge scale, consider a distributed NoSQL solution for metadata to avoid a single point of failure.

Caching Metadata: As mentioned, a cache (like Redis) can store recently or frequently accessed metadata (e.g., the details of popular shows or user session data). This speeds up API responses since hitting an in-memory cache is faster than a database query. We’d just need to ensure we properly update/invalidates cache on changes (for example, if a video’s details are updated or new content added).

Search Service: Users will search for titles, actors, genres, etc. Doing full-text search or complex filtering directly on the main database could be inefficient. A common design is to use a search index:

• We can employ a search engine like Elasticsearch or Apache Solr, which indexes the metadata (titles, descriptions, tags). These systems are optimized for text queries, allowing features like auto-complete, typo tolerance, and ranking results by relevance.

• The search service would work by syncing with the main metadata store. For example, whenever a new video is added or updated, an entry is added to the search index. That way, when a user types "Stranger Things", the search service quickly finds matching titles or descriptions.

• Alternatively, if we keep it simpler, we could just use database queries (with proper indexes on title, etc.) for search in a smaller-scale scenario. But in an interview, mentioning a dedicated search service shows you’re thinking about scalability and performance.

How Search is Exposed: The frontend will call an API like /search?query=... which hits our Search Service. That service queries the index and returns a list of matching videos (with maybe short info for display). We also could cache popular search queries results to further speed it up.

Metadata updates: It’s worth noting that things like view counts or likes are constantly changing for videos (especially in user-generated content systems like YouTube). If we had those, we might handle them via counters in a database or even a separate analytics system. For Netflix, which is more curated content, view counts aren’t shown publicly, but they do track them internally for recommendations. We might skip deep analytics in our design due to time.

In summary, the design includes a reliable metadata store (scalable via sharding/replication or using NoSQL), complemented by caching and a specialized search component for handling user queries efficiently.

User Authentication & Monetization

No streaming service is complete without user management, including authentication and subscription handling (monetization):

User Authentication

We need a secure way for users to sign up, log in, and access content. Typically:

• Users register with an email and password (or via OAuth with Google, etc.), and we store hashed passwords in the database.

• The Auth Service verifies credentials and issues an authentication token (like a JWT) that the client uses for subsequent requests. This token might carry info like user ID and subscription level.

• Every request to play a video or access account info will include this token, and our backend will validate it to ensure the user is logged in and has rights to the content.

Authorization & Profiles Netflix often has profiles under one account and may restrict how many streams you can run in parallel based on your plan. In a simplified design, we can assume a user account has a subscription plan associated (Basic, Standard, Premium) and maybe an expiry date for the subscription. The system should check these before allowing streaming. For example, if a user is not subscribed or their plan doesn’t include HD streaming, certain content or quality levels might be restricted. (This can get complex, but a brief mention shows awareness.)

Monetization (Subscriptions/Payments) Our service likely uses a subscription model (monthly fee for unlimited streaming). Designing a full payment system is beyond scope, but we can outline it:

• We would integrate with payment gateways (like Stripe, PayPal, etc.) to handle credit card transactions securely. When a user subscribes or their renewal is due, the Payment Service charges them through these gateways.

• The system stores the user’s subscription status (active, canceled, tier level) in the database. This data is referenced whenever the user attempts to play a video.

• We could also include a billing service that keeps track of subscription periods, sends reminders or invoices, etc. In an interview, simply stating “we need to handle payments and ensure only paying users can access the service” is usually enough.

Account Limits Optionally mention, if relevant, that to prevent abuse, you might enforce rules like maximum number of concurrent streams per account. This could be done by tracking active stream sessions for a user in a central service.

Security Considerations: For a real design, we’d also consider content protection. Netflix uses DRM (Digital Rights Management) to prevent piracy (ensuring streams are encrypted and only playable on authorized devices). Implementing DRM involves licensing third-party DRM providers and is quite complex, so one would just mention it as a note if needed. In a basic design interview answer, DRM might be beyond scope unless asked specifically.

In summary, the Authentication & User Management component ensures that only legitimate, paying users access the service. It verifies user identity, stores profile data, and works with a payment system to manage subscriptions. This component ties in with the other parts (for example, the streaming service will check “is this user allowed to stream this now?” before giving them a video stream URL).

Trade-offs & Alternative Approaches

In designing such a system, we face various trade-offs and design choices. Here are some key considerations and alternatives:

Database Choice (SQL vs NoSQL)

Using a relational database for metadata ensures strong consistency (important for financial data like subscriptions or when updating video info).

However, at very large scale, a single SQL database can become a bottleneck, even with sharding. A NoSQL database (like Cassandra or DynamoDB) offers better horizontal scaling and fault tolerance at the cost of weaker consistency (eventual consistency).

Netflix, for example, leans heavily on Cassandra for its scalability and always-on requirements.

In an interview, you could say: for a beginner design, start with a SQL DB for simplicity, but mention that in a real-world system at Netflix scale, a NoSQL solution might be chosen for certain datasets (user viewing logs, etc.) to handle the load.

Content Delivery Strategy

Relying on a third-party CDN is straightforward and quick to implement – you benefit from their infrastructure but cost can be significant (CDNs charge for bandwidth).

For a huge library and user base, those costs add up (Netflix reportedly delivers petabytes daily, which would cost millions per day if using a public CDN).

The alternative is building a proprietary CDN: Netflix’s Open Connect is an example, where they invested in custom caching appliances to push content closer to ISPs and users.

This yields long-term cost savings and performance gains but requires a lot of engineering effort. For most companies, third-party CDN is fine; building your own CDN is only justified at massive scale.

Monolith vs Microservices

A design trade-off in system architecture – a monolithic application (all components in one codebase and deployment) is simpler initially and avoids network overhead between services.

However, it can’t scale each component independently and becomes hard to maintain as it grows.

A microservices architecture (separating user service, video service, search service, etc.) adds complexity in terms of distributed communication, but it allows each piece to scale and evolve on its own.

Netflix pioneered a microservices approach to achieve their scale.

In an interview answer, you might mention starting with a monolith for an MVP, and then extracting services as the system grows.

Caching and Freshness

Caching (both CDN for videos and Redis for metadata) greatly improves performance, but introduces a trade-off with data freshness.

For example, if a video’s description is updated, the cache might still serve the old data until updated.

We need cache invalidation strategies (like clearing or updating the cache on writes, or using short TTLs) to ensure users eventually see up-to-date information.

The trade-off is between absolute freshness vs. performance. In practice, slight staleness in non-critical data (like view counts or recommendations) is acceptable for the benefit of speed.

Pre-computation vs On-Demand Processing

We choose to pre-encode videos into multiple formats ahead of time, which uses extra storage and processing power upfront.

The benefit is that playback is instant and smooth since the work is already done. The alternative could be on-the-fly encoding (encode a new format when a user requests it).

That saves storage (you don’t store many versions of a video that might never be watched in 480p, for example) but at the cost of delaying the user’s playback and heavy real-time computation.

Most systems opt for pre-computation (as does Netflix) to optimize the user experience. This is a classic storage vs computation trade-off.

SQL vs Search Engine for Queries

We mentioned using a search service for handling queries.

One could ask, why not just use the database for searching titles?

It’s possible for small scale, but as data grows and queries get complex, dedicated search engines are more efficient.

The trade-off is the extra complexity of maintaining an index vs. the performance of search queries.

Most large platforms use an external search system to provide rich search features and keep the main DB from slowing down.

Each of these decisions can be discussed in an interview to show you understand not just one solution, but also why you’d pick one approach over another.

Real-world streaming services continuously balance these trade-offs and even revisit decisions (for example, Netflix moved some workloads from Cassandra to newer databases as their needs evolved). The key is to justify your choices based on requirements like scale, development speed, and cost.

Final Thoughts & Key Takeaways

Designing a Netflix-like streaming service involves bringing together many components to work in harmony. To recap the key components of our design:

• Requirement Analysis: Always start by clarifying what the system needs to do (functional requirements) and how well it must do it (non-functional requirements like scale and performance).

• High-Level Architecture: Outline the major pieces – clients, backend services (for users, videos, search, etc.), databases, storage, CDN, and so on. This gives the interviewer a map of your solution.

• Storage & Processing: Plan for storing huge video files in an efficient way (object storage) and processing them (encoding pipeline) for optimal delivery. These ensure the content is ready to stream.

• Efficient Delivery: Use CDNs and caching to get videos to users quickly and reliably. Incorporate load balancers and scalable servers to handle large numbers of requests concurrently.

• Data Management: Use appropriate databases for metadata and user info, and consider search indexing for handling queries. Ensure the system can scale its data layer (through replication, sharding, or NoSQL solutions).

• Robust User Management: Secure the service with proper authentication and handle subscriptions/payments to monetize the platform.

• Consider Trade-offs: There is often no one “right” design – discuss alternatives (which DB, which caching strategy, etc.) and explain why your chosen design meets the requirements well. This shows critical thinking.

Best Practices for Interview: In a system design interview, communicate your thoughts clearly:

• Start with requirements and assumptions (e.g., “Let’s assume X million users, global service…”).

• Sketch a high-level diagram (on the whiteboard or verbally) before diving into details. This might include users -> load balancer -> servers -> databases -> CDN.

• Tackle one component at a time (as we did step-by-step), explaining decisions. It’s often helpful to use the outline: data storage, computation (encoding), delivery, etc.

• Mention how you’d ensure scalability and reliability in each part. Use phrases like “to avoid single points of failure, we do …” or “to handle increased load, we can …”.

• Address bottlenecks and failure scenarios (e.g., what if a server goes down? What if traffic spikes?).

• Finally, briefly touch on improvements or future considerations (like “if we had more time, we’d discuss the recommendation system or how to optimize cost”).

By covering the above points, you demonstrate a well-rounded understanding of designing a complex system like a streaming service.

Even at a medium level of depth, the interviewer will see that you’ve considered the core challenges.

Designing a Netflix-like service is indeed challenging, but by breaking it down into these components, you can methodically explain your approach.