Ever wondered how apps like TikTok serve endless video feeds to 100M+ users? I broke it down in simple words.
Introduction
Let’s say you want to build a short-video app that looks and feels like TikTok or Reels. A user opens the app, a video starts right away, a swipe brings the next one in, likes and comments work instantly, and somehow the app knows what each person wants to watch.
Now imagine the same smooth feeling when one hundred million people show up. That is what we are aiming for in this article: a clear, friendly walk-through of how such a system works, from small details all the way to the big picture.
Requirements
Before we think about servers and databases, we should agree on what the app must do and how well it should do it. The app should let people upload short vertical videos and add basic details like a caption or a song. The app should show an endless feed of videos that start fast and never stutter while the user scrolls.
It should remember likes, comments, and shares, and it should give each person a personal feed based on what they watch and enjoy. It should keep working even when traffic spikes, such as during a big event or a viral video.
It should be safe, so we should be able to flag or hold back risky content. And it should be cheap enough to run at scale by using caching, a content delivery network, and background processing to avoid doing heavy work in the middle of a user request.
On the quality side, people care most about speed and stability. Video should start within a few hundred milliseconds. Swipes should feel instant. Uploads should not block the user for long. we can finish the heavy lifting after we have the file.
If one service is slow or a database is under pressure, the app should degrade gracefully, for example by showing a popular backup feed while the recommendation service catches up. Finally, we need a way to observe the system so we can see errors, slow requests, and cache hit rates, and fix issues before users notice.
High-Level Architecture Overview
Here’s how to structure such a system in simple boxes and layers:
- Clients: The mobile or web app users interact with.
- Load Balancer: A traffic director that spreads user requests across many servers.
- Web Servers (API Layer): Handles uploads, likes, comments, and serves the feed.
- Cache: A quick storage layer (like Redis) to fetch popular videos faster.
- Database: Stores metadata user info, video details, likes, comments.
- Video Storage: A place to save the actual video files (e.g., cloud storage).
- CDN (Content Delivery Network): Distributes video content globally to reduce delays.
- Recommendation Engine: Chooses which videos to show each user.
- Message Queue: For tasks done outside of the main request like processing new uploads.
Scaling to 100 Million Users
Load Balancer & Web Servers
- A single server can’t handle all requests. Use many servers behind a load balancer.
- This way, when traffic spikes, you can add more servers to spread the load.
Caching to Speed Things Up
- Cache popular video metadata so the system doesn’t query the database every time.
- Use CDNs to store video content closer to users this cuts loading time and bandwidth.
Storing Videos & Metadata
- Keep video files in scalable storage (like S3 or distributed object storage).
- Use a database (SQL or NoSQL) for metadata. Scale it by sharding splitting data across multiple servers.
Processing Uploads
When someone uploads a video:
- It lands in a message queue.
- A worker takes it from the queue and:
- Transcodes it into formats for various devices.
- Generates a thumbnail.
- Extracts features (length, resolution, poster image).
- Stores the processed file and updates metadata in the database.
This keeps the upload process quick and off the main user-facing path.
Recommendation System: How the Feed Gets Personalized
A TikTok-style feed uses a two-step process:
- Candidate Generation: From millions of videos, pick a few hundred that might interest the user.
- Ranking: Score each candidate by relevance (does the user like similar videos? types of content, watch history), then order them.
This system runs fast using data like watch time, likes, shares, user interests, etc., and ensures users see videos they’re likely to enjoy. As TikTok’s architecture shows, microservices and real-time data processing tools like Kafka help make this system work smoothly.
Real-World Optimization: CDN + Peer-to-Peer (P2P)
Sending all videos from a central server is expensive. ByteDance (TikTok’s company) developed a hybrid system called Swarm it combines traditional CDNs with peer-to-peer delivery. Nearby users can share parts of a video with each other, reducing costs while keeping performance high.
Smart Tricks to Save Bandwidth & Improve Experience
- Prefetching: If users swipe through videos quickly, loading the next one ahead of time helps — but you don’t want to waste data if they skip many. So, apps adaptively prefetch videos based on user behavior.
- Multicast Streaming: Sending a single video stream to multiple users at once (e.g. live events) saves bandwidth, especially useful in wireless networks.
- Multi-Tier Caching: Use edge caches, CDN-level caching, and origin storage with smart policies to reduce delays and server load.
Putting It All Together: A Simple Story Flow
- User opens the app request hits load balancer goes to a web server.
- Web server fetches video metadata from cache; if not in cache, it goes to the database.
- The app requests video playback → served via CDN or Swarm P2P if available.
- For new uploads:
- Request goes to a queue.
- Workers process the video, store it, update metadata.
- Recommendation system fetches user history and video data, generates a ranked list, and returns the feed.
All services are spread across multiple servers, data centers, and use caching, queues, and CDNs/P2P to stay fast, reliable, and cheap.
conclusion
Building a video feed system like TikTok that serves 100 million users might sound like magic, but it boils down to dependable blocks: scaling web servers, smart caching, global video delivery, async video processing, and real-time personalized feeds.