YouTube System Design in 3 levels: junior, senior, and principal

System Design Interviews are conducted at big tech companies like Google, Amazon, Apple, and Netflix to assess candidates’ skills in designing large-scale distributed systems. Even more importantly, these interviews typically play a significant role in determining each candidate’s starting level and salary, ranging from junior to principal engineer. This was true when I was interviewing SWE candidates during my time at Meta and Microsoft—and it's still true today.

Here's the single most important thing to remember about System Design Interviews at top companies: depending on the level you are targeting, interviewers will be expecting different levels of detail and complexity in your answer. For example, a junior engineer would be expected to describe various components of a system without delving into much detail, while senior engineers are expected to dive into details of each component and the system’s workflow, discussing trade-offs and design choices based on those trade-offs. Naturally, much more is expected from principal engineers, including future system scalability, balancing trade-offs, failure mitigation techniques, and enhancing user experience during failures.

The good news is that you can use this structure to your advantage as a candidate. What does that mean in practice? Well, if a junior engineer demonstrates knowledge of more complex System Design concepts, there is a greater chance that the company will offer them at a higher level. Similarly, if a senior engineer shows only basic knowledge, they might be "down-leveled" into a more junior role instead.

The following table presents the three overarching engineering roles, and core expectations for each in a System Design Interview setting. For optimal interview performance, a candidate’s talking points should align with the expectations listed below:

Note: If you are new to the System Design domain and want to better understand the core fundamentals before diving into comprehensive interview prep, the hands-on course Grokking Modern System Design for Engineers & Managers is a terrific starting point.

This blog will walk you through the process of designing YouTube for a System Design Interview at three levels: junior engineer, senior engineer, and principal engineer. (This is one of my personal favorite System Design problems to ask in interviews and will provide a great real-world case study for this exercise).

By the end, you will have a clear idea of how to approach your System Design Interview no matter which level you are targeting—and be equipped with a few battle-tested strategies for scoping any System Design problem you encounter.

Let’s dive in.

YouTube System Design: An overview#

YouTube is a well-known video streaming platform with approximately 100 million daily active users (DAUs). It provides various video services, such as uploading, streaming, searching, commenting, liking/disliking, and sharing videos. According to YouTube, over 500 hours of video content is uploaded every minute, and approximately 694,000 hours of video content are streamed per minute. This makes YouTube a very large and busy platform for video streaming. 

Designing YouTube requires an in-depth understanding of the backend services, careful design considerations, and handling of different trade-offs. Many top companies will ask a System Design Interview question, like designing a video streaming service—and YouTube is a representative example.

Let’s start with the requirements and high-level design of a YouTube system. I will progress with the detailed design from the perspective of a junior, senior, and principal engineer, along with the expected talking points for each role in the interview.

YouTube design requirements#

Let’s set the stage for each engineering role by scoping the design problem with the following requirements:

• Functional requirements:

  • Stream video: The system should stream videos to users upon request.

  • Upload video: The system should allow users to upload a video.

  • Search video: Users should be able to search a video based on its title.

  • Share video: Users should be able to share a video.

  • Video stats: The system should be able to record the stats of the video, such as dislikes and the number of views.

  • Provide comments on a video: Users should be able to comment on videos.

• Nonfunctional requirements:

  • High availability: The system should provide a good percentage of uptime, i.e., above 99 percent.

  • Low latency: The system should provide a smooth streaming experience and avoid lag in video playback.

  • Scalability: The system should be scalable enough to handle the ever-increasing users.

  • Reliability: Videos uploaded to the system should be stored persistently, not damaged, corrupted, or lost.

Based on the above requirements, let’s create a high-level design and detailed design. The visual representation of the YouTube system makes it easy for the readers to understand each component's description and the system’s workflow.

High-level design of YouTube#

Clients upload videos via application servers in the high-level design of a video streaming service like YouTube. The application servers assign the uploaded videos to encoders, which compress and transcode videos into different formats and resolutions and store them in blob storage. Client and video metadata is stored in databases. The purpose of CDNs is to provide a smooth and continuous video streaming experience to clients.

Let’s move on with the detailed design from the junior engineer’s perspective.

YouTube System Design: Junior engineer approach#

One of the key purposes of a System Design Interview is to assess a candidate’s skills in designing a large-scale distributed system or infrastructure. Therefore, the following talking points are expected from a junior-level candidate:

• Infrastructure components description

• Workflow of the system and complete life cycle of a request

• Dive deep into one or two technical areas

• Discussion on trade-offs

The following is the detailed design of the YouTube system.

Infrastructure components description#

Let’s describe each component involved in the detailed design of the YouTube system that is described above:

The workflow and life cycle of a request#

In the high-level design of the YouTube system, clients’ requests for video streaming or uploading are directed toward the application servers via the load balancer. The load balancers manage website traffic by evenly distributing these requests among the application servers, which direct the requests to the related services. As for user-related actions, the application server communicates with the user’s service, which accesses the user’s metadata database to authenticate and retrieve the user’s information.

For video-related actions, the application server interacts with the video service. When a certain video is uploaded, it is placed in uploaded storage as the first step and then handed over to the transcoding service to convert the video to various formats and segments. The transcoded videos are stored in the blob storage, while the thumbnails are placed in Bigtable. The video service also adds relevant video information to the video metadata database. For video streaming, the application server interacts with the CDN and colocation sites to ensure smooth content delivery to users to provide them with a seamless video playback experience.

In the following section, let’s discuss how smooth video playback, fast search, and accurate video recommendation are ensured.

Smooth video streaming, fast search, and recommendations#

The YouTube system is primarily designed to provide a smooth video streaming experience. To further enhance user experience and engagement, other secondary features, such as fast search and video recommendations, should be provided to increase users’ retention on the platform. Let’s discuss how YouTube provides smooth playback, search, and recommendation as follows:

• Smooth video playback: In video streaming, one of the techniques that is considered a game change is adaptive bitrate (ABR) streaming. ABR streaming adjusts the video quality based on the internet condition, avoiding video buffering. In ABR, an uploaded video is encoded into several chunks of multiple quality levels. The video player on the client side monitors network conditions and adjusts the video quality by communicating with the streaming servers. The streaming server sends the appropriate video chunk to the client according to the network speed. The process is demonstrated in the following figure:

• Key components and services for ABR streaming: Several key software components and services are essential to ensure adaptive bitrate streaming and seamless chunking in YouTube’s design. The encoding service transcodes videos into multiple bitrates and resolutions, enabling adaptive streaming. The CDN is crucial for distributing these video chunks efficiently across global edge locations, reducing latency, and improving load times. The ABR server having the ABR logic implemented adjusts the video quality in real time based on the user’s network conditions to ensure smooth playback without buffering. The chunking service splits videos into small segments to facilitate adaptive bitrate switching. Another service called the playback monitoring service is also necessary to track and adjust playback performance based on network conditions and user behavior. These components work together to provide YouTube users a high-quality, adaptive streaming experience.

• YouTube search and recommendation: For designing a YouTube search system, a processing engine that uses a combination of algorithms and machine learning techniques to deliver relevant content along with recommended videos. The videos uploaded to YouTube platforms are periodically indexed based on various factors, such as title, description, tags, thumbnails, video length, channel name, subscribers, etc. The indexed metadata of videos is stored in the key-value stores. When a user enters a search query, the processing engine processes it to understand the intent behind the query. Once the query is understood, it is matched with the index content, where the relevant content, including videos, channels, and playlists, is fetched from different databases via the video service and displayed to the users.

The design of a YouTube-like system includes multiple trade-offs that must be balanced. In the following section, I will discuss them.

Trade-offs in the YouTube design#

Designing a YouTube system involves several trade-offs that should be carefully balanced to achieve the desired product goal and user experience. Let’s discuss some of the key trade-offs:

• Consistency vs. availability: The common trade-off in a YouTube-like system is whether to achieve consistency or availability (low latency) in the event of a network partition or normal operating conditions. According to the PACELC theorem, our priority should be availability and low latency in both cases because the streaming system should be highly available to provide a good playback experience. Therefore, in our design, consistency can take a hit for availability and low latency.

• Scalability vs. performance: The ever-increasing number of users can potentially lower the system’s performance. To maintain good performance and a smooth streaming experience, you should utilize a load balancer to distribute the traffic across various streaming servers. Distributed cache strategies such as CDNs can also enable us to increase performance.

• Retrieval speed vs. storage cost: To reduce storage costs, you should store frequently accessed videos on faster storage like SSDs, while less frequently accessed videos should be stored on cheaper storage like HDDs.

• Latency vs. data processing: Real-time processing of data, such as recommendations and live analytics, can cause latency. Stream processing frameworks such as pub-sub can be utilized to reduce latency. For non-critical tasks such as providing recommendations, you can employ offline (asynchronous) data processing.

In the above sections, I discussed the talking points expected from a junior engineer. Let’s move on to the senior engineer’s perspective from a more detailed discussion in the following section.

YouTube System Design: Senior engineer approach#

If you are appearing in the System Design iIterview for a senior engineering role, the following talking points will be expected from you. Remember, the following points implicitly include the expectations from a junior engineer discussed in the above section.

• Identify the critical components

• Identify potential choke points and bottlenecks

• Identify potential scaling issues and propose solutions

• Align the trade-offs with the desired product goals and user experience

Let’s explore the above talking points in more depth as follows.

Critical components#

The key critical components of the system are:

• Web and application services: These are the entry points to the system that provide user authentication and authorization services. The application service directs incoming requests to the relevant services. If these services fail, the burden on other services, such as CDN, video service, or user service, will increase, which can cause a cascading failure.

• Load balancers: If load balancers fail, incoming requests can overwhelm some servers, which can become a bottleneck, thereby increasing latency or, in the worst case, causing the service to fail, making the system unavailable.

• CDN and collocation sites: If the CDN of any region fails, it may cause slow response time and buffering in the video playback service for frequently accessed video, adversely impacting the user experience.

• Video and user service: The failure of this service can cause a content blackout and user frustration. The user may not be able to perform actions related to videos or user management, including creating and managing channels, uploading or streaming a video, editing a profile, commenting on a video, and so on.

• Storage: Data durability is of utmost importance in a YouTube-like system. In case of storage failure, there is a high chance of data corruption or loss, which may cause data durability issues.

Apart from the critical components, there might be some services that can potentially become choke points and bottlenecks during peak hours. Let’s discuss them below:

Potential choke points and bottlenecks#

• Video and transcoding services: A large number of simultaneous upload requests can burden the video and transcoding services, which can cause delays.

• CDN and colocation sites: The performance of CDN and colocation sites can be affected by limited bandwidth and network congestion during high traffic for frequently accessed or viral videos. This may cause delays in playing and videos.

• User service and metadata database: During peak hours, a large number of user activity for viral videos can overload the user's service and metadata databases, which can slow down the response time.

• Services as a single point of failure: A single instance of each service might not suffice to handle a large number of users and the requests for uploading and watching videos. Therefore, they can become a single point of failure.

After discussing the critical components and choking points, the next point is to discuss potential scaling issues in the design and provide solutions for each issue. Let’s explore that in the following section:

Potential scaling issues and propose solutions#

Identifying potential scaling issues and proposing a solution involves addressing various aspects of the system that could become bottlenecks as user-generated content grows. The following are some key scaling issues and the proposed solutions:

• Massive concurrent requests: One of the most stressful load-handling tasks for YouTube is the management of millions of simultaneous requests that involve video uploads, searches, and streaming, and that can easily cause servers to crash. One way to resolve this problem is to utilize horizontal scaling by adding more web and application servers to the fleet to distribute the load. Also, utilizing auto-scaling groups to add or remove instances based on traffic can de-escalate the issue. The system can also use the latest load balancing algorithms like least connections and weighted round-robin to evenly distribute incoming requests across servers to avoid overloading a single server.

• High volumes of video uploading: A large number of simultaneous uploading of videos can overload the video and transcoding server causing delays in processing the videos. A solution to this problem is to apply asynchronous processing using a pub-sub system. It will quickly acknowledge the upload request while processing the videos asynchronously. Similarly, cloud systems can he­lp video processing in a few ways. First, the­y use many servers to transcode­ videos at once. Second, these cloud solutions can add or remove workers as needed, providing elastic services (that can grow or shrink based on the current demand). Further, edge­ computing is also helpful as it processes and compresses videos close to whe­re they are uploade­d, reducing the workload on the origin servers.

• Storage management and performance: Accessing a large amount of video content and metadata can overwhelm the video service, blob storage, and relational databases, resulting in a slow response time. A possible solution is to store frequently accessed videos on high-speed drives like SSDs, while the less accessed videos are stored on the cheaper HDDs or on Amazon S3 Glacier, a cloud-based storage option. Also, the metadata can be stored on NoSQL databases such as Google Bigtable and MongoDB, which are suitable for fast read and write operations. Furthermore, metadata database sharding can also overcome this issue, which splits data into smaller and more manageable shards based on criteria such as user IDs or video IDs. This enables parallel processes of queries, reducing the burden on a single server.

• Network bandwidth and latency: Avoiding the problems associated with video streaming such as buffering during peak times, one has to deal with network bandwidth and latency. A CDN can be a useful tool to this end which enables the geographical delivery of the content, reducing the distance the data has to travel, thereby lessening delay and congestion. Similarly, ABR allows each client to get a video that perfectly matches their network conditions, reducing latency. On the other side, locating data centers in various regions cuts off several microseconds and ensures network traffic is equally distributed.

• Data consistency and availability: One of the primary concerns in creating distributed systems is the high availability and consistency of data. In the YouTube system, eventual consistency models can be employed for non-critical data that ensure high availability. On the other hand, quorum-based replication models can be used for some important data, providing a good balance between availability and consistency.

• System security: Let’s first note that scaling the system comes with risks of unauthorized attacks, and it becomes tricky to protect the infrastructure from intruders. It can be addressed by implementing security best practices such as encrypting the system and using strong authentication, as well as conducting audits on a regular basis. Use Cloudflare or AWS Shield services to secure the network against DDoS attacks. Moreover, imposing strict access controls and making sure that permissions are regularly checked to guarantee that only those who are authorized have access to the critical parts/components of the systems.

Scaling the system also brings some trade-offs that need to be aligned with the product goal and user experience. Let's explore that in the following section.

Trade-offs: Aligning with product goals and user experience#

Let’s discuss the trade-offs involved in designing the YouTube system and how they can be aligned with the product goals and user experience. Some of the key trade-offs are discussed as follows:

• Consistency vs. availability: Our priority in this trade-off is the availability of the system with eventual consistency. In other words, you need to make the system available to a large number of simultaneous users, say 1 million. With the availability, a good user experience can be provided by ensuring the videos are always available for streaming with slight inconsistencies in metadata.

• Scalability vs. performance: Providing high performance to individual users is often achieved with the cost of allocating significant resources, which can hinder the system’s scalability. However, with the distributed architecture, you can achieve both. The desired product goal is to provide a smooth and quick streaming experience that can minimize latency and buffering for the end users.

• Retrieval speed vs. storage cost: To overcome this trade-off, you are required to store frequently accessed videos in fast storage like SSDs, while for less frequently accessed videos, you can use low-cost storage like HDDs. The product goal is to provide quick access to videos, especially high-definition and with low cost. In turn, the user experience can be optimized with fast video streaming and minimum delays in accessing videos.

• Latency vs. data processing: The data processing in the critical path of the users’ activities can cause delays; therefore, there should be a pub-sub system to decouple various services and to asynchronously process the data for searching and recommendations. The product goal is to provide relevant content to users, which will enhance user experience by providing content with minimum latency.

Detailed design of YouTube from a senior engineer’s perspective#

As discussed above, a senior engineer should at least accommodate the following points in the design presented by a junior engineer to cover various aspects of the design:

• Handling massive concurrent requests by having redundant servers and auto-scaling groups

• A cluster of users’ databases and cache to reduce latency in retrieving user-related data

• Asynchronous processing of uploaded videos using pub-sub system

• Increasing the response time of video retrieval by incorporating SSDs and HDD storage. 

• Using AWS Shield to prevent DDoS attacks

The detailed design from a senior engineer’s perspective should look like as below, after incorporating the above points:

I have covered the junior and senior engineers’ perspectives on YouTube design so far. In the following section, I will move to the principal engineers’ perspectives on YouTube design in the System Design interview.

YouTube System Design: Principal engineer perspective#

Principal engineers should propose a design that addresses immediate requirements considering future growth. The main points of the discussion should include the following:

Build solutions for immediate needs with consideration for future scalability#

Building a YouTube-like­ system needs smart planning. It should work we­ll now and be ready for more use­rs later. To handle future growth, the­ system needs te­chnology that can scale up easily. Adding more servers and resources as ne­eded is called horizontal scaling. Cloud se­rvices for storage and computing let you scale­ resources based on traffic. Scalable­ databases like distributed database­s store more user data without slowing down. Tools like­ Docker and Kubernete­s help manage and scale applications automatically base­d on demand. By using these strate­gies, the YouTube syste­m can meet current ne­eds and be ready for more­ users later, giving everyone a smooth experience.

Let’s expand on the potential impacts of user experience and service level agreements (SLAs) during partial or complete system failures.

User experience during failures and its impact on SLAs#

YouTube’s SLA typically provides availability, performance, security, privacy, and data integrity. It is crucial to ensure uninterrupted services during any failures in the YouTube system to maintain the service level agreements. Let’s explore the following failures and how these can be mitigated to ensure seamless user experience and satisfaction:

• Server failure: During a server failure, users may face failure in video upload, slower response time, or interrupted video streaming. To mitigate this issue, there need to be multiple web and application servers with load balancing to direct the load to another server during a failure. Also, there needs to be real-time monitoring. and an auto-recovery mechanism to quickly restart or spin another server. Further, during server failures or heavy loads, the system should degrade gracefully to provide reduced functionality rather than completely going down. For example, during a high load, the system should prioritize playing videos over showing comments and recommendations. The impact of this failure is directly on the availability and performance SLAs.

• Network failure: During a network failure, users may experience buffering, increased latency, or might not be able to access the service. To resolve this issue, the CDNs should be placed in different regions to minimize the impact of the failure. Also, there should be multi-regions setup to reroute traffic to another region in case of a regional network outage. The impact of this failure is also on performance and availability.

• Database and storage failures: During this failure, the user may face issues such as failure to access their playlist and metadata or even logging issues. To mitigate such failure, the databases should be replicated, and appropriate sharding mechanisms should be employed. Further, robust backup and recovery mechanisms should be employed to reduce the impact of such failures. These failures directly impact the data integrity SLA. However, they could also have a diverse effect on performance and availability.

Real-world consequences of deployed solutions#

Designing a YouTube-like system is complex and requires a number of critical decisions during or after deployment that can have potential real-world consequences. Let’s discuss some key considerations to address these real-world consequences:

• Scalability and performance issues: To address these issues, auto-scaling groups should be used to reduce or increase the number of active servers based on demand. Similarly, advanced load-balancing algorithms should be employed to distribute the load evenly. Further, enabling edge computing services will process data closer to the users, which can improve performance and minimize latency.

• Service availability and reliability: To provide high availability and reliability, redundant servers, geographically distributed data centers, and disaster recovery plans should be in place.

• Cost management issues: For resource optimization and usage, monitoring tools should be used that can provide a better estimation of the resource and avoid over estimation. Similarly, multi-tier storage can be used to optimize cost and performance, such as storing less frequently accessed data on slow and low-cost storage. Similarly, to reduce the load on the origin server, the CDN can be a better option to reduce the need for additional servers and optimize the bandwidth.

• Content management and moderation: AI and ML-based automated tools can be leveraged to detect and mark inappropriate content for moderators’ review. Similarly, the user should be enabled to report inappropriate content for moderators’ review.

• Data privacy and compliance: Users’ data should be protected via encryption mechanisms in both rest and transient form. The system should also be compliant with the GDPRGeneral Data Protection Regulation (GDPR) is a regulation enforced by the European Union (EU) that aims to give users more control over their personal data. standards.

• Security vulnerabilities: To address security issues, regular security audits should be conducted. Similarly, services such as rate limiting, AWS Shield, or Cloudflare should be in place to avoid DDoS attacks. Furthermore, an intrusion detection system should be installed to monitor suspicious activities.

Data center failovers, regional backups, and disaster recovery plans#

Proper strategies for data center failovers, regional backups, and disaster recovery should be implemented to design a robust and resilient YouTube system. This will enable the platforms to remain available, reliable, and quickly recoverable from failures.

Before proceeding, let’s differentiate between regions, availability zones, and data centers. A region comprises one or more availability zones, whereas an availability zone comprises one or more standalone data centers. Different availability zones are connected via different links to make them available during a disaster or data center failure.

Let’s expand on different data center failovers, regional backups, and disaster recovery strategies.

• Data center failovers: For a YouTube-like system, service availability is crucial for a good user experience. A data center failover mechanism is when a data center fails; there should be a mechanism in place to direct the traffic to another backup data center. This can be achieved by replicating crucial components such as web servers, application servers, and other services across multiple data centers. Also, there should be a regional load balancer to direct traffic to another healthy data center within a region. Similarly, the monitoring system should be placed to assess the status of the data center’s health, and in the event of failure, the load balancer should redirect the traffic seamlessly. Furthermore, to avoid data loss in the data center switching process, the infrastructure should have data synchronization support to ensure data consistency and minimize data loss.

• Regional backups: Regional backups are especially important during catastrophic events that can affect an entire geographic area. It involves having redundant databases and storage servers in multiple regions. A YouTube system must have regional backups to ensure that user-generated content, metadata, and other data are preserved if the primary region faces any disaster.
  The actual realization of regional backups is by using geographically distributed data centers where replication of data is done in real time or almost near real time. Advanced replication technologies, such as cross-replication within regions of cloud storage services, achieve these. These backups are stored in regions that are quite often located in vastly separated regions to mitigate risks from natural disasters, especially regional ones, regional power outages, or any other localized disruption. Regular integrity and recoverability testing needs to be done to ensure that the data can be relied on in an emergency.

• Disaster recovery plans (DRPs): DRPs are comprehensive tactics meant to get a system back to its old functionality and recover data in case of a serious occurrence, such as hardware failures, cyber attacks, and natural disasters. In the case of a YouTube-like system, a DRP serves as a clear outline of a system’s recovery from these occurrences. Also, it outlines the necessary procedures and processes to recover from these events. The DRPs include risk assessment and business impact analysis (BIA), which entails the process of the identification of all the possible risks that could happen and the estimation of the damage that could be made by different disaster scenarios. Recovery Time Objective (RTO)The acceptable downtime during which the data might be lost. and Recovery Point Objective (RPO)The maximum period during which data might be lost. are the two most important factors that guide the development of recovery strategies.
  Together, the specifics and essentials verify that the business should have a DRP, which consists of backing up data to a secure location as well as off-site backup. This permits users to extract data and configure failover mechanisms and redundancy that form the stability of the most critical services in a catastrophic event. Regular testing and drills are also necessary to provide advantages of the DRP to the recovery team of the company as well as make them familiar with their responsibilities and roles and inform them of any gaps or weaknesses in the plan.

Analyze usage patterns and manage peak loads#

A system like YouTube needs analysis and usage patterns of user behavior and predicting traffic spikes. This can be achieved via advanced analytics, where metrics such as peak viewing times, popular content, and regional user distribution are continuously monitored. Furthermore, through big data analytics technology, traffic patterns can be predicted and prepare the system for potential surges. Simultaneously, monitoring systems can provide great insight into current usage and can provide better resource management.

To cope with peak loads, auto-scaling systems that adapt the number of servers to the demand need to be introduced. Cloud platforms such as AWS, Google Cloud, or Azure come with auto-scaling groups that add or remove instances automatically whenever there is a change in traffic. The combination of predictive analytics with reactive scaling and load balancing will enable the system to manage peak loads optimally without sacrificing performance.

Handling DDoS attacks and ensuring graceful service degradation#

Dealing with DDoS threats is the most fundamental issue in maintaining the availability and reliability of a system like YouTube. Applications like Cloudflare, AWS Shield, and Google Cloud Armor are built to guard against DDoS attacks. They run automatic methods to filter traffic designed for attacks and only allow real user requests. Similarly, the use of rate limiting and traffic analysis tools can help identify abnormalities and block them to prevent damaging the system.

Implementing graceful service degradation is essential when the servers are overloaded or partially fail. One technique for graceful degradation is circuit breakers, which avoid cascaded failure by temporarily blocking the requests to a failed service. Similarly, the fallback mechanisms can be provided to direct traffic toward secondary healthy servers when the primary fails. In the case of the YouTube system, the ABR can be leveraged to provide a smooth video playback experience with lower resolution.

Impact of security breaches and service outages#

Security violations and service unavailability are crucial to the company’s reputation and SLA expectations. In a YouTube-like system, security breach incidents can result in a lack of user trust and a decrease in active users, as well as potentially inflicting the company with lawsuits. Furthermore, in an environment where users and content creators expect their data and content to be secure, breaches can discourage content creation and sharing, directly impacting the platform’s vitality and growth.

On the other hand, service outages interfere with the user experience, as the video playback and live streams can’t be accessed. Frequent service outages for long periods will frustrate users and eventually make people choose an alternative platform. This affects not only user satisfaction but also advertisers who depend on the system’s availability for their campaigns, which may lead to economic loss and damaged business relationships. The service providers need to use methods such as failover strategies and redundant systems to provide high availability. Also, transparent communication with the users is crucial during critical situations to manage the SLA expectations. Other beneficial actions, like predefining realistic SLAs and robust recovery plans backed by quick and effective incident resolution techniques, bring a positive perception of the platform.

Detailed design from a principal engineer’s perspective#

Here I present the detailed design from the principal engineer’s perspective, which should at least include the following points:

• Multi-region setup and regional backup system and provide a disaster recovery plans in the event of failure or catastrophic events

• Utilizing regional and global load balancers to distribute traffic across multiple data centers and regions

• Data synchronization system across multiple regions

• Monitoring system to check the health of data centers and regions

• Sharding databases to reduce the burden on the databases and decrease the data retrieval time

• Rate limiter or AWS Shield to prevent DDoS attacks

• Content management and moderation services

• Intrusion detection system to prevent the system from unauthorized access

Based on the above key points, the resultant detailed design from a principal engineer’s perspective should look like as below:

Conclusion #

In this guide, I presented a guide to YouTube's System Design, scoped differently for different SWE roles. A quick recap of expectations for each level:

• Junior engineer: Provide a detailed design with basic components and workflow, along with some trade-offs.

• Senior engineer: Provide a detailed design, align trade-offs with product goals, and identify critical components, choke points, and scaling issues.

• Principal engineer: Design a solution with future scalability considerations, impact on SLAs, and user experience during the failure of a deployed solution. (Note: principal engineers will also be expected to discuss data center failovers, regional backups, recovery plans, and mitigating peak loads and DDoS attacks).

What’s next: Ace your System Design Interview#

I’m hoping this guide helped you feel more confident about what to expect from your upcoming System Design Interviews. Having been on both sides of the interview table hundreds of times, I can assure you that there is no substitute for structured interview prep. Competition is high, and preparation is your biggest guarantor of success.

Educative offers dozens of System Design courses written by industry experts, PhDs, and ex-FAANG engineers, wherein you can get a stronger understanding of System Design fundamentals and hands-on practice with real-world problems. I have added links to some of my favorites below.

Happy learning!