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Migrating Critical Traffic At Scale with No Downtime?—?Part 2

The Netflix TechBlog

Migrating Critical Traffic At Scale with No Downtime — Part 2 Shyam Gala , Javier Fernandez-Ivern , Anup Rokkam Pratap , Devang Shah Picture yourself enthralled by the latest episode of your beloved Netflix series, delighting in an uninterrupted, high-definition streaming experience. This is where large-scale system migrations come into play.

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Efficient SLO event integration powers successful AIOps

Dynatrace

For instance, consider how fine-tuned failure rate detection can provide insights for comprehensive understanding. Please refer to How to fine-tune failure detection (dynatrace.com) for further information. SLOs must be evaluated at 100%, even when there is currently no traffic. What characterizes a weak SLO?

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

Accurately Reflecting Production Behavior A key part of our solution is insights into production behavior, which necessitates our requests to the endpoint result in traffic to the real service functions that mimics the same pathways the traffic would take if it came from the usualcallers. We call this capability TimeTravel.

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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. This integration will not only optimize performance but also ensure more efficient resource utilization.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

This led to a suite of fragmented scripts, runbooks, and ad hoc solutions scattered across teamsan approach that was neither sustainable nor efficient. To detect issues proactively, we need to simulate traffic and predict system behavior in advance. Stay tuned for a closer look at the innovation behind thescenes!

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Prevent potential problems quickly and efficiently with Davis exploratory analysis

Dynatrace

This is done without the need to create custom dashboards and is complemented by efficient analysis capabilities that automatically guide SREs to potential root causes of anomalies, enabling more efficient work and freeing up time for essential workflows.

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Best Practices for Scaling RabbitMQ

Scalegrid

Scaling RabbitMQ ensures your system can handle growing traffic and maintain high performance. This guide will cover how to distribute workloads across multiple nodes, set up efficient clustering, and implement robust load-balancing techniques. Configuring quorum queues achieves high data safety and reliability in your RabbitMQ setup.