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Chapter 14: Design 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

  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

    • [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

    • 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

  • 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

  • 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

  1. Preprocessor: Splits videos into smaller chunks (GOP alignment). It has 4 responsibilities.

    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

    • 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

    • 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

    • 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

  2. Distributed Upload Centers: Use CDNs as upload hubs close to users.

  3. Parallel Processing: Decouple modules using message queues for high parallelism.

    Message QueueMessage Queue

Safety Optimizations ​

  1. Pre-Signed URLs: Restrict video uploads to authorized users.

    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.