Remove Infrastructure Remove Traffic Remove Tuning
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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. This insight led us to build Edgar: a distributed tracing infrastructure and user experience. Our distributed tracing infrastructure is grouped into three sections: tracer library instrumentation, stream processing, and storage.

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Title Launch Observability at Netflix Scale

The Netflix TechBlog

This approach provides a few advantages: Low burden on existing systems: Log processing imposes minimal changes to existing infrastructure. 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!

Traffic 170
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RabbitMQ vs. Kafka: Key Differences

Scalegrid

Optimizing RabbitMQ requires clustering, queue management, and resource tuning to maintain stability and efficiency. However, performance can decline under high traffic conditions. The choice between these authentication methods depends on an organizations security infrastructure. Recording logs for audits and debugging.

Latency 147
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Large scale deployments are easy and cost-effective with network zones (Early Adopter)

Dynatrace

Unnecessary traffic between such data centers can result in wasted resources, unpredictable downtimes, and lost business. By minimizing bandwidth and preventing unrelated traffic between data centers, you can maintain healthy network infrastructure and save on costs. optimizing traffic routing. What’s next.

Network 240
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Migrating Netflix to GraphQL Safely

The Netflix TechBlog

The control group’s traffic utilized the legacy Falcor stack, while the experiment population leveraged the new GraphQL client and was directed to the GraphQL Shim. This helped us successfully migrate 100% of the traffic on the mobile homepage canvas to GraphQL in 6 months.

Traffic 356
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Kubernetes vs Docker: What’s the difference?

Dynatrace

Think of containers as the packaging for microservices that separate the content from its environment – the underlying operating system and infrastructure. This opens the door to auto-scalable applications, which effortlessly matches the demands of rapidly growing and varying user traffic. What is Docker? Watch webinar now! Networking.

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Best Practices for Scaling RabbitMQ

Scalegrid

Scaling RabbitMQ ensures your system can handle growing traffic and maintain high performance. Optimizing RabbitMQ performance through strategies such as keeping queues short, enabling lazy queues, and monitoring health checks is essential for maintaining system efficiency and effectively managing high traffic loads.