As a former TikTok Backend Engineer, I will reveal some of TikTok's internal technical details and architecture related to the live streaming feature.
Table of Contents
- Requirement Analysis
- Technology Selection
- System Design
- Front-end Development
- Back-end Development
- Real-time Video Stream Processing
- Testing
- Deployment
- Operations and Monitoring
- Iterative Optimization
- Redis in Live Streaming
- Microservices Architecture Components
- Applications of Java Dynamic Proxy
- Redis Master-Slave Replication and High Availability
- Comment System Design
- High Concurrency and High Availability in Message Queues
- Distributed Cache and Message Sending
- B-tree vs. B+ tree Comparison
- Synchronizing User Login State Across Services and Cross-domain Token Handling
Requirement Analysis
First, define the core functionality requirements for the live streaming feature:
- Video Streaming: Support for broadcasters to stream video and viewers to watch these streams.
- Real-time Communication: Provide a chat room feature for audience interaction.
- Gift System: Allow viewers to send virtual gifts to broadcasters, with animations and statistics.
- Audience Management: Display an online audience list and support notifications for audience joining and leaving.
- User Permission Management: Differentiate between permissions for normal viewers, broadcasters, and administrators.
- Content Moderation: Implement real-time or delayed content moderation to prevent inappropriate content.
Technology Selection
Front-end
- Tech Stack: Use React.js or Vue.js to build dynamic user interfaces.
- Video Playback: Utilize HLS.js or Video.js for video stream playback in browsers.
Back-end
- Language and Framework: Use Java with the Spring Boot framework to build RESTful APIs.
- Real-time Communication: Implement WebSocket for real-time chat and interactions.
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Database:
- MySQL for persistent storage.
- Redis for caching and session storage.
- Message Queue: Use RabbitMQ or Kafka to handle real-time messages (e.g., chat and gifts).
Real-time Video Streaming
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Technology:
- Use WebRTC for peer-to-peer video streaming.
- Alternatively, integrate third-party live streaming SDKs (e.g., Tencent Cloud, NetEase Cloud).
- CDN: Deploy a Content Delivery Network (CDN) to enhance the speed and stability of video streaming.
System Design
- Database Design: Create a robust data model, including entities like users, live rooms, gifts, and chat records.
- API Design: Define clear API interfaces to support user management, live room management, and message processing.
- Caching Strategy: Utilize Redis to cache frequent data to reduce database load.
Front-end Development
- UI Design: Design a user-friendly interface with responsive layouts.
- Feature Implementation: Implement video playback, chat functions, and gift-sending features.
- API Integration: Interact with back-end APIs to display and manipulate dynamic data.
Back-end Development
- User Management: Implement user registration, login, authentication, and authorization.
- Live Streaming Management: Implement live room creation, starting, stopping, and status management.
- Message Processing: Handle chat message sending and receiving, gift statistics, and more.
Real-time Video Stream Processing
- Video Capture and Streaming: Integrate WebRTC or use third-party SDKs for video capture and streaming.
- Video Playback: Support multi-platform video playback for a smooth viewing experience.
Testing
- Unit Testing: Test individual modules to ensure basic functionality works correctly.
- Integration Testing: Test interactions between system modules to ensure overall functionality.
- Stress Testing: Simulate high concurrent scenarios to test system performance under load.
Deployment
- Server Deployment: Deploy front-end and back-end applications on cloud servers.
- CDN Configuration: Configure CDN to accelerate video stream distribution.
- Load Balancing: Use Nginx or other load balancers to optimize traffic distribution.
Operations and Monitoring
- System Monitoring: Monitor server load, network traffic, and system health in real-time.
- Log Management: Analyze runtime logs to quickly locate and resolve issues.
Iterative Optimization
- User Feedback: Continuously optimize user experience based on feedback.
- Feature Expansion: Add new features or improve existing ones based on user needs and market trends.
Redis in Live Streaming
- Caching: Use Redis to cache user data and live room information to improve access speed.
- Distributed Lock: Implement distributed locks with Redis in high concurrency environments to ensure data consistency.
- Message Queue: Utilize Redis's publish/subscribe feature for simple message queue functionality.
Microservices Architecture Components
- Service Discovery and Registration: Use Eureka or Consul to manage microservice instances.
- API Gateway: Implement Spring Cloud Gateway or Zuul for request routing.
- Configuration Management: Use Spring Cloud Config for centralized configuration management.
- Service Communication: Utilize HTTP/REST or gRPC for communication between services.
Applications of Java Dynamic Proxy
- AOP Implementation: Add logging, permission checks, etc., around method execution.
- Remote Service Calls: Implement RPC calls through dynamic proxy.
- Lazy Loading: Load actual objects only when accessed for the first time.
Redis Master-Slave Replication and High Availability
- Master-Slave Replication: Configure Redis for master-slave replication to enhance read-write separation.
- Sentinel: Use Redis Sentinel for failover detection and automatic failover.
- Cluster Mode: Deploy Redis Cluster for horizontal scaling and high availability.
Comment System Design
For comment system design, use Redis's ZSET to implement sorting by likes and pagination:
- Data Structure: Use ZSET to store comment IDs and their corresponding like counts.
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Like Update: Use the
ZINCRBYcommand to atomically increase like counts. -
Sorting and Pagination: Use the
ZRANGEcommand to retrieve comments sorted by likes and implement pagination.
High Concurrency and High Availability in Message Queues
- Distributed Architecture: Use a distributed message queue system like Kafka for high concurrent access.
- Replication Mechanism: Ensure message high availability through replication.
- Batch Operations: Implement batch production and consumption of messages to improve throughput.
Distributed Cache and Message Sending
- Distributed Cache: Utilize Redis Cluster to shard and store cache data, supporting high concurrency and large-scale data.
- Message Sending: Use Kafka or similar message queue systems to handle message distribution to a large number of users.
B-tree vs. B+ tree Comparison
- B+ Tree: Suitable for database indexing because its leaf nodes form a linked list, supporting efficient range queries and sequential scanning.
- B-tree: Suitable for file systems like ReiserFS because its internal nodes also store data, reducing lookup times.
Synchronizing User Login State Across Services and Cross-domain Token Handling
- Session Sharing: Use Redis to store session information centrally, ensuring user state sharing across multiple services.
- JWT: Implement JWT for distributed authentication, where the client carries the JWT to access different services.
- Cross-domain Token: Ensure security for cross-domain requests by configuring CORS settings or using HTTP headers to carry the token.
Additional Enhancements
To further improve the documentation and facilitate better understanding, consider the following enhancements:
- Diagrams: Incorporate architecture diagrams to visualize system components and their interactions.
- Code Snippets: Add relevant code examples to illustrate implementations, especially for complex features like WebRTC integration or Redis configurations.
- Tables: Use tables to compare technologies or outline feature specifications clearly.
- Links: Provide links to relevant resources, libraries, or documentation for deeper insights.
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