Remove Efficiency Remove Latency Remove Tuning Remove Video
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Rebuilding Netflix Video Processing Pipeline with Microservices

The Netflix TechBlog

The Netflix video processing pipeline went live with the launch of our streaming service in 2007. This architecture shift greatly reduced the processing latency and increased system resiliency. For example, in Reloaded the video quality calculation was implemented inside the video encoder module.

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Bending pause times to your will with Generational ZGC

The Netflix TechBlog

More than half of our critical streaming video services are now running on JDK 21 with Generational ZGC, so it’s a good time to talk about our experience and the benefits we’ve seen. Reduced tail latencies In both our GRPC and DGS Framework services, GC pauses are a significant source of tail latencies.

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Netflix Video Quality at Scale with Cosmos Microservices

The Netflix TechBlog

Moorthy and Zhi Li Introduction Measuring video quality at scale is an essential component of the Netflix streaming pipeline. Perceptual quality measurements are used to drive video encoding optimizations , perform video codec comparisons , carry out A/B testing and optimize streaming QoE decisions to mention a few.

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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently. REST APIs, authentication, databases, email, and video processing all have a home on serverless platforms.

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Netflix Cloud Packaging in the Terabyte Era

The Netflix TechBlog

After content ingestion, inspection and encoding, the packaging step encapsulates encoded video and audio in codec agnostic container formats and provides features such as audio video synchronization, random access and DRM protection. Uploading and downloading data always come with a penalty, namely latency.

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The Netflix Cosmos Platform

The Netflix TechBlog

It supports both high throughput services that consume hundreds of thousands of CPUs at a time, and latency-sensitive workloads where humans are waiting for the results of a computation. In the diagram below of a typical Cosmos service, clients send requests to a Video encoder service API layer. debian packages).

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Bulldozer: Batch Data Moving from Data Warehouse to Online Key-Value Stores

The Netflix TechBlog

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. The data warehouse is not designed to serve point requests from microservices with low latency. Moving data with Bulldozer at Netflix.

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