How Facebook & YouTube Handle BILLIONS of Likes & Views! скачать в хорошем качестве

How Facebook & YouTube Handle BILLIONS of Likes & Views!

Counting seems simple—until you're dealing with billions of likes, views, and interactions across platforms like Facebook, YouTube, and Twitter. A single database can’t handle that scale without crashing. Distributed counters solve this by spreading the counting workload across multiple servers, ensuring speed, scalability, and fault tolerance. In this video, we’ll break down sharded counters, event-driven aggregation, real-time updates using Kafka, and how big tech optimizes counting at scale. Whether you're an engineer working with high-traffic APIs or just curious about how social media platforms keep their numbers accurate, this deep dive into distributed counters is for you! 💬 Have questions about Distributed Systems? Drop them in the comments! 🔔 Like & Subscribe for more deep dives My LinkedIn:   / bytemonk   📌 Timestamps 🔹 00:00 – Introduction: Why Counting at Scale is Hard 🔹 00:43 – The Problem with Single Database Counters 🔹 02:07 – Sharded Counters: Breaking the Load Across Nodes 🔹 03:18 – HyperLogLog: Approximate Counting for Huge Datasets 🔹 06:07 – Using Kafka & Event Streams for Real-Time Counting 🔹 06:40 – How Big Tech (Facebook, YouTube, Twitter) Handles Counters 🔹 07:30 – Final Thoughts & Optimizing for Scalability    • System Design Interview Basics      • System Design Questions      • LLM      • Machine Learning Basics      • Microservices      • Emerging Tech   AWS Certification: AWS Certified Cloud Practioner:    • How to Pass AWS Certified Cloud Pract...   AWS Certified Solution Architect Associate:    • How to Pass AWS Certified Solution Ar...   AWS Certified Solution Architect Professional:    • How to Pass AWS Certified Solution Ar...   #distributedsystems #systemdesign #Distributedcounter