Remove Availability Remove Latency Remove Tuning
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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 248
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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. It facilitates the distribution of these learnings to other models, either through shared model weights for fine tuning or directly through embeddings.

Tuning 157
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Introducing Impressions at Netflix

The Netflix TechBlog

This dual-path approach leverages Kafkas capability for low-latency streaming and Icebergs efficient management of large-scale, immutable datasets, ensuring both real-time responsiveness and comprehensive historical data availability. million impression events globally every second, with each event approximately 1.2KB in size.

Tuning 165
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Its design prioritizes high availability and efficient data transfer with minimal overhead, making it a practical choice for handling real-time data pipelines and distributed event processing. It follows a push-based approach, ensuring messages are distributed to consumers as soon as they become available.

Latency 147
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Why applying chaos engineering to data-intensive applications matters

Dynatrace

Stream processing systems, designed for continuous, low-latency processing, demand swift recovery mechanisms to tolerate and mitigate failures effectively. This significantly increases event latency. Spark Structured Streaming can also provide consistent fault recovery for applications where latency is not a critical requirement.

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

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 1 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Hundreds of millions of customers tune into Netflix every day, expecting an uninterrupted and immersive streaming experience. Logging is selective to cases where the old and new responses do not match.

Traffic 345
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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. Classic queues can be used in clusters, emphasizing their behavior during node failures, particularly regarding durability and availability.