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Netflix’s Distributed Counter Abstraction

The Netflix TechBlog

By: Rajiv Shringi , Oleksii Tkachuk , Kartik Sathyanarayanan Introduction In our previous blog post, we introduced Netflix’s TimeSeries Abstraction , a distributed service designed to store and query large volumes of temporal event data with low millisecond latencies. Today, we’re excited to present the Distributed Counter Abstraction.

Latency 251
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

This decoupling simplifies system architecture and supports scalability in distributed environments. Kafka stores and distributes data through a partitioned log system, which spans multiple brokers to provide fault tolerance and scalability. Apache Kafka uses a custom TCP/IP protocol for high throughput and low latency.

Latency 147
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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Now let’s look at how we designed the tracing infrastructure that powers Edgar. If we had an ID for each streaming session then distributed tracing could easily reconstruct session failure by providing service topology, retry and error tags, and latency measurements for all service calls.

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The Power of Caching: Boosting API Performance and Scalability

DZone

Benefits of Caching Improved performance: Caching eliminates the need to retrieve data from the original source every time, resulting in faster response times and reduced latency. Reduced server load: By serving cached content, the load on the server is reduced, allowing it to handle more requests and improving overall scalability.

Cache 246
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Title Launch Observability at Netflix Scale

The Netflix TechBlog

The Challenge of Title Launch Observability As engineers, were wired to track system metrics like error rates, latencies, and CPU utilizationbut what about metrics that matter to a titlessuccess? The complexity of these operational demands underscored the urgent need for a scalable solution.

Traffic 172
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Spring WebFlux: publishOn vs subscribeOn for Improving Microservices Performance

DZone

With the rise of microservices architecture , there has been a rapid acceleration in the modernization of legacy platforms, leveraging cloud infrastructure to deliver highly scalable, low-latency, and more responsive services. Why Use Spring WebFlux?

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Foundation Model for Personalized Recommendation

The Netflix TechBlog

Yet, many are confined to a brief temporal window due to constraints in serving latency or training costs. These insights have shaped the design of our foundation model, enabling a transition from maintaining numerous small, specialized models to building a scalable, efficient system.

Tuning 200