Remove Architecture Remove Availability Remove Latency
article thumbnail

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
article thumbnail

RabbitMQ vs. Kafka: Key Differences

Scalegrid

This article outlines the key differences in architecture, performance, and use cases to help determine the best fit for your workload. RabbitMQ follows a message broker model with advanced routing, while Kafkas event streaming architecture uses partitioned logs for distributed processing. What is RabbitMQ? What is Apache Kafka?

Latency 147
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Single-core memory bandwidth: Latency, Bandwidth, and Concurrency

John McCalpin

Latency” is the duration from the execution of a load instruction (to an address that misses in all the caches), and the completion of that load instruction when the data is returned from memory. The example below is for a 2005-era processor with 60 ns memory latency and 6.4 cache lines -> 5.6 cache lines -> 5.6

Latency 71
article thumbnail

Introducing Impressions at Netflix

The Netflix TechBlog

Architecture Overview The first pivotal step in managing impressions begins with the creation of a Source-of-Truth (SOT) dataset. This dual availability ensures immediate processing capabilities alongside comprehensive long-term data retention. Thus, all data in one region is processed by the Flink job deployed within thatregion.

Tuning 166
article thumbnail

Best Practices for Scaling RabbitMQ

Scalegrid

Implementing clustering and quorum queues in RabbitMQ significantly improves load distribution and data redundancy, ensuring high availability and fault tolerance for messaging services. This decoupling is crucial in modern architectures where scalability and fault tolerance are paramount.

article thumbnail

Foundation Model for Personalized Recommendation

The Netflix TechBlog

This scenario underscored the need for a new recommender system architecture where member preference learning is centralized, enhancing accessibility and utility across different models. Yet, many are confined to a brief temporal window due to constraints in serving latency or training costs.

Tuning 165
article thumbnail

Migrating Critical Traffic At Scale with No Downtime?—?Part 1

The Netflix TechBlog

When undertaking system migrations, one of the main challenges is establishing confidence and seamlessly transitioning the traffic to the upgraded architecture without adversely impacting the customer experience. It provides a good read on the availability and latency ranges under different production conditions.

Traffic 347