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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!

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📌 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

https://www.youtube.com/playlist?list=PLJq-63ZRPdBt423WbyAD1YZO0Ljo1pzvY

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https://www.youtube.com/playlist?list=PLJq-63ZRPdBslxJd-ZT12BNBDqGZgFo58

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#distributedsystems #systemdesign #Distributedcounter