Remove Database 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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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Investigating a video streaming failure consists of inspecting all aspects of a member account. If we had an ID for each streaming session then distributed tracing could easily reconstruct session failure by providing service topology, retry and error tags, and latency measurements for all service calls.

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

The Netflix TechBlog

By collecting and analyzing key performance metrics of the service over time, we can assess the impact of the new changes and determine if they meet the availability, latency, and performance requirements. They enable us to further fine-tune and configure the system, ensuring the new changes are integrated smoothly and seamlessly.

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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.

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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

REST APIs, authentication, databases, email, and video processing all have a home on serverless platforms. When an application is triggered, it can cause latency as the application starts. Security, databases, and programming languages effortlessly remain up to date and secure in the serverless model.

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Achieving observability in async workflows

The Netflix TechBlog

Once you finally find useful identifiers, you may begin writing SQL queries against your production database to find out what went wrong. We are expected to process 1,000 watermarks for a single distribution in a minute, with non-linear latency growth as the number of watermarks increases.

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