The Evolution of Scaling at Netflix Reference: netflixtechblog. com

A Distributed Counter is a system where the responsibility of counting events is spread across multiple servers or nodes in a network. Netflix needs to track and measure multiple user interactions to make real-time decisions and optimize its infrastructure. For this reason, they built a Distributed Counter Abstraction. Netflix’s Distributed Counter Abstraction operates in four main layers, ensuring high performance, scalability, and eventual consistency. 1 - Client API Layer Users interact with the system by sending AddCount, GetCount, or ClearCount requests. The Netflix Data Gateway efficiently processes and routes these requests. 2 - Event Logging and TimeSeries Storage Events are stored in Netflix TimeSeries Abstraction for scalability. Each event is tagged with an Event ID to ensure idempotency. To avoid database contention, events are grouped into time partitions known as buckets. Data is stored in Cassandra. 3 - Rollup Pipeline or Aggregation Rollup Queues collect event changes and process them in batches. Aggregation occurs in immutable time windows, ensuring accurate rollup calculations. Data is stored in the Cassandra Rollup Store for eventual consistency. 4 - Read Optimization (Cache & Query Handling) Aggregated counter values are cached in EVCache for ultra-fast reads. If a cache value is stale, a background rollup refresh updates it. This model allows Netflix to process 75K requests per second with single-digit millisecond latency. Reference: netflixtechblog. com/netflixs-distributed-counter-abstraction-8d0c45eb66b2 -- Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages): bit.ly/bbg-social

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