Requirements

Functional Requirements:

• Count views for each video accurately, ensuring every legitimate view is counted, even under high traffic.
• Display real-time or near-real-time view counts to users and content creators.
• Prevent fraudulent or bot views using techniques like watch duration thresholds, rate limiting, IP analysis, CAPTCHA, or device fingerprinting.
• Provide content creators with view count trends broken down by time period (daily, weekly, monthly) for analytics and insights.

Non-Functional Requirements:

List the key non-functional requirements (eg low latency, scalability, reliability, etc.)...

API Design

Define the APIs expected from the system. This is your chance to analyze and define the read and write paths so that you can come up with the high-level design...

read -- getViews ( videoId )

write -- addView ( userId, ipId, deviceId )

High-Level Design

Describe the overall system architecture. Identify the main components needed to solve the problem end-to-end. Use the diagramming tool to create a block diagram.

Since views are high throughput data, we need to have fast view updates.. for that reason, instead of bombarding database, we use redis which is fast and handle huge load too. To make redis fault tolerant, we simultaneously write to kafka topics for durability incase of redis crash. Then a kafka consumer updates db of the views as a batch with sum of counts in single operation instead of huge number of update commands.

For db, we use postgress.

As number of videos are higher and to improve scalability of view updates. we shard redis nodes using consistent hashing based on videoId. we also have replication with a factor of 2 to have high availability and not loose counts when redis goes down.

Detailed Component Design

Deep dive into 2-3 key components. Explain how they work, how they scale, discuss tradeoffs, capacity, and any relevant algorithms or data structures.