--- title: "Design YouTube" description: "Upload, transcode and stream video at scale with blob storage, a DAG transcoding pipeline, and CDNs." icon: video sidebar: label: "YouTube" --- ## Introduction YouTube is a massive video streaming platform supporting video uploads, playback, and various interactions. This chapter focuses on designing a scalable video streaming system with the following core features: - **Fast video uploads** - **Smooth video streaming** - **Ability to change video quality** - **Low infrastructure cost** - **High availability and reliability** ### Key Statistics (2020) - **2 billion monthly active users** - **5 billion videos watched per day** - **37% of mobile internet traffic comes from YouTube** - Available in **80 languages** - **$15.1 billion ad revenue** in 2019 --- ## Step 1: Understand the Problem and Scope ### Core Functionalities 1. Upload videos 2. Watch videos ### Supported Platforms - Mobile apps, web browsers, and smart TVs ### Assumptions - **Daily Active Users (DAU):** 5 million - **Average Video Size:** 300 MB - **Upload Limits:** Max 1 GB per video - **Daily Storage Need:** 150 TB - **CDN Costs:** 5 million * 5 videos * 0.3GB * $0.02 = $150,000/day (using Amazon CloudFront) --- ## Step 2: High-Level Design ### Components ![High Level Design](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/high-level-design.png) High Level Design 1. **Client:** Devices like smartphones, computers, and TVs. 2. **CDN (Content Delivery Network):** Stores and streams videos. 3. **API Servers:** Handles all user interactions except video streaming (e.g., uploads, metadata updates). 4. **Metadata Database:** Stores video metadata (e.g., title, description, size). 5. **Original Storage:** Blob storage for uploaded videos. 6. **Transcoding Servers:** Convert videos into multiple resolutions and formats. 7. **Transcoded Storage:** Blob storage for transcoded videos. --- ### Core Workflows #### 1. Video Uploading Flow - **Parallel Processes:** 1. Upload video to original storage. 2. Update video metadata in the database. - **Video Upload (Steps):** ![Video Upload Flow](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/video-uploading-flow.png) Video Upload Flow - [1] Videos are uploaded to blob storage. - [2] Transcoding servers convert videos to multiple formats. - [3] One trasncoding is complete, following two steps are exectued in parallel. - [3a] Transcoded videos are sent to transcoded storage. - [3b] Transcoding completion events are queued in the completion queue. - [3a.1] Videos are distributed to the CDN. - [3b.1] Completion handlers update metadata and inform users. - **Metadata Upload (Steps):** ![Metadata Upload](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/metadata-upload.png) Metadata Upload - The client in parallel sends a request to update the video metadata - The request contains video metadata, including file name, size, format, etc. #### 2. Video Streaming Flow ![Video Streaming Flow](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/video-streaming-flow.png) Video Streaming Flow - Videos are streamed directly from the CDN using edge servers to minimize latency. - Some of te popular streaming protocols are MPEG_DASH, Apple HLS, Adobe HDS. - *Different streaming protocols support different video encodings and playback players.* --- ## Step 3: Design Deep Dive ### Video Transcoding #### Importance 1. Raw video consumes large amounts of storage space. It Reduces storage space. 2. Ensures compatibility across devices and browsers. 3. Adapts video quality to network conditions. #### Components - **Container:** Encapsulates video, audio, and metadata (e.g., MP4, AVI). - **Codecs:** Compression and Decompression algorithms (e.g., H.264, VP9). #### Directed Acyclic Graph (DAG) Model ![DAG Video Transcoding](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/dag-video-transcoding.png) DAG Video Transcoding - Transcoding a video is computationally expensive and time-consuming. - DAG Model defines tasks like encoding, thumbnail generation, and watermarking. - Allows high parallelism in video processing. - The original video is split into video, audio, and metadata. - Video encodings: Videos are converted to support different resolutions, codec, bitrates. - Thumbnail: It can either be uploaded by a user or automatically generated bythe system. - Watermark: Image overlay on top of your video contains identifying information about the video. --- ### Video Transcoding Architecture ![Video Transcoding](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/video-transcoding-architecture.png) Video Transcoding 1. **Preprocessor:** Splits videos into smaller chunks (GOP alignment). It has 4 responsibilities. ![DAG Config](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/dag-config.png) DAG Config - Video splitting: Video stream is split or further split into smaller Group of Pictures (GOP) alignment. - It split videos by GOP alignment for old clients. - It generates DAG based on configuration files client programmers write. - It stores GOPs and metadata in temporary storage in case the encoding fails, the system could use persisted data for retry operations. 2. **DAG Scheduler:** Organizes tasks into sequential or parallel stages. ![DAG Scheduler](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/dag-scheduler.png) DAG Scheduler - It splits a DAG graph into stages of tasks and puts them in the task queue in the resource manager. - Stage 1: video, audio, and metadata. - The video file is further split into two tasks in stage 2: video encoding and thumbnail. 3. **Resource Manager:** Responsible for managing the efficiency of resource allocation.It contains 3 queues and a task scheduler. ![Resource Manager](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/resource-manager.png) Resource Manager - Task queue: priority queue that contains tasks to be executed. - Worker queue: priority queue that contains worker utilization info. - Running queue: contains currently running tasks and workers running the tasks. - Task scheduler: picks the optimal task/worker, and instructs the chosen task worker to execute the job. 4. **Task Workers:** Perform transcoding and other operations. ![Task Worker](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/task-worker.png) Task Worker - Different task workers may run different tasks 5. **Temporary Storage:** Stores intermediate data for retries. - The choice of storage system depends on factors like data type, data size, access frequency, data life span, etc. 6. **Output:** Transcoded videos ready for distribution. --- ## System Optimizations ### Speed Optimizations 1. **Parallel Video Uploads:** Split videos into smaller chunks for faster, resumable uploads. ![Video Split](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/video-split.png) Video Split 2. **Distributed Upload Centers:** Use CDNs as upload hubs close to users. 3. **Parallel Processing:** Decouple modules using message queues for high parallelism. ![Message Queue](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/message-queue1.png) Message Queue ![Message Queue](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/message-queue2.png) Message Queue ### Safety Optimizations 1. **Pre-Signed URLs:** Restrict video uploads to authorized users. ![Pre Signed](/blume-assets/content/content/system-design/(02-core-designs)/images/youtube/pres-signed-urls.png) Pre Signed 2. **Protect Videos:** - **DRM Systems** (e.g., Apple FairPlay, Google Widevine). - **AES Encryption.** - **Watermarking.** ### Cost-Saving Optimizations 1. Serve only popular videos via CDN; less popular ones from high-capacity servers. 2. Encode on-demand for rarely accessed videos. 3. Regionalize video distribution based on popularity. 4. Build custom CDNs and partner with ISPs to reduce bandwidth costs. --- ## Error Handling ### Recoverable Errors - Retry failed uploads, transcoding, or resource allocation tasks. ### Non-Recoverable Errors - Stop malformed video processing and return error codes. ## Further reading - [YouTube Architecture](http://highscalability.com/youtube-architecture) - [YouTube scalability 2012](https://www.youtube.com/watch?v=w5WVu624fY8) - [Transcoding Videos at Scale](https://www.egnyte.com/blog/2018/12/transcoding-how-we-serve-videos-at-scale/) - [Facebook Video Broadcasting](https://engineering.fb.com/ios/under-the-hood-broadcasting-live-video-to-millions/) - [Netflix Video Encoding at Scale](https://netflixtechblog.com/high-quality-video-encoding-at-scale-d159db052746) - [Netflix Shot based encoding](https://netflixtechblog.com/optimized-shot-based-encodes-now-streaming-4b9464204830)