使用微服务重建Netflix视频处理流水线

Liwei Guo, Anush Moorthy, Li-Heng Chen, Vinicius Carvalho, Aditya Mavlankar, Agata Opalach, Adithya Prakash, Kyle Swanson, Jessica Tweneboah, Subbu Venkatrav, Lishan Zhu

This is the first blog in a multi-part series on how Netflix rebuilt its video processing pipeline with microservices, so we can maintain our rapid pace of innovation and continuously improve the system for member streaming and studio operations. This introductory blog focuses on an overview of our journey. Future blogs will provide deeper dives into each service, sharing insights and lessons learned from this process.

这是Netflix关于如何通过微服务重建其视频处理流水线的多部分系列博客的第一篇，以便我们能够保持快速创新的步伐，并不断改进会员流媒体和工作室运营系统。本介绍性博客侧重于我们的旅程概述。未来的博客将深入探讨每个服务，并分享从这个过程中获得的见解和经验。

The Netflix video processing pipeline went live with the launch of our streaming service in 2007. Since then, the video pipeline has undergone substantial improvements and broad expansions:

Netflix视频处理流程于2007年我们的流媒体服务上线时启动。从那时起，视频流程经历了大量的改进和广泛的扩展：

Starting with Standard Dynamic Range (SDR) at Standard-Definitions, we expanded the encoding pipeline to 4K and High Dynamic Range (HDR) which enabled support for our premium offering.
从标准动态范围（SDR）开始，在标准定义下，我们将编码流程扩展到4K和高动态范围（HDR），从而支持我们的高级服务。
We moved from centralized linear encoding to distributed chunk-based encoding. This architecture shift greatly reduced the processing latency and increased system resiliency.
我们从集中式线性编码转向分布式基于块的编码。这种架构转变极大地减少了处理延迟并增加了系统的弹性。
Moving away from the use of dedicated instances that were constrained in quantity, we tapped into Netflix’s internal trough created due to autoscaling microservices, leading to significant improvements in computation elasticity as well as resource utilization efficiency.
摆脱了数量受限的专用实例的使用，我们利用了Netflix的内部市场，这是由于微服务的自动扩展而产生的，从而显著提高了计算弹性和资源利用效率。
We rolled out encoding innovations such as per-title and per-shot optimizations, which provided signi...