Netflix on AWS

Netflix is the world’s leading internet television network, with more than 100 million members in more than 190 countries enjoying 125 million hours of TV shows and movies each day. Netflix uses AWS for nearly all its computing and storage needs, including databases, analytics, recommendation engines, video transcoding, and more—hundreds of functions that in total use more than 100,000 server instances on AWS.

Netflix Uses Close to 1,000 Amazon Kinesis Shards in Parallel to Process Billions of Traffic Flows


Netflix’s Amazon Kinesis Streams-based solution has proven to be highly scalable, each day processing billions of traffic flows. Typically, about 1,000 Amazon Kinesis shards work in parallel to process the data stream. “Amazon Kinesis Streams processes multiple terabytes of log data each day, yet events show up in our analytics in seconds,” says John Bennett, senior software engineer at Netflix. “We can discover and respond to issues in real time, ensuring high availability and a great customer experience.”

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Amazon Kinesis Streams processes multiple terabytes of log data each day, yet events show up in our analytics in seconds. We can discover and respond to issues in real time, ensuring high availability and a great customer experience."

John Bennett
Senior Software Engineer, Netflix

How Netflix Uses AWS Lambda to Build Rule-Based, Self-Managing Infrastructure to Increase Efficiency


Netflix is one of the world's largest online media streaming providers, delivering almost 7 billion hours of videos to nearly 50 million customers in 60 countries per quarter. The company is planning to use AWS Lambda to build rule-based, self-managing infrastructure and replace inefficient processes to reduce the rate of errors and save valuable time. Watch Neil Hunt, Netflix’s chief product officer, explain how the company can use event-based triggers to help automate the encoding process of media files, the validation of backup completions and instance deployments at scale, and the monitoring of AWS resources used by the organization.

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Netflix Gains New Efficiencies Using AWS Lambda

Netflix Realizes Multi-Region Resiliency Using Amazon Route 53

What happens when you need to move 89 million viewers to a different AWS region? Netflix's infrastructure, built on AWS, makes it possible to be extremely resilient, even when the company is running services in many AWS Regions simultaneously. In this episode of This is My Architecture, Coburn Watson, director of performance and reliability engineering at Netflix, walks through the company's DNS architecture—built on Amazon Route 53 and augmented with Netflix's Zuul—that allows the team to evacuate an entire region in less than 40 minutes.

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How Netflix Encodes at Scale
In this session, Netflix explores the various strategies employed by the encoding service to automate management of a heterogeneous collection of Amazon EC2 Reserved Instances, resolve compute contention, and distribute instances based on priority and workload. The Netflix encoding team is responsible for transcoding different types of media sources to a large number of media formats to support all Netflix devices. Transcoding these media sources has compute needs ranging from running compute-intensive video encodes to low-latency, high-volume image and text processing. The encoding service may require hundreds of thousands of compute hours to be distributed at moment's notice where they are needed most.
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How Netflix Tunes Amazon EC2 Instances for Performance
Netflix uses Amazon EC2 instance types and features to create a high- performance cloud, achieving near-bare-metal speed for its workloads. This session summarizes the configuration, tuning, and activities for delivering the fastest possible Amazon EC2 instances. Brendan Gregg, a member of the performance and OS engineering team at Netflix, shows how to choose Amazon EC2 instance types, how to choose between Xen modes (HVM, PV, or PVHVM), and the importance of Amazon EC2 features such SR-IOV for bare-metal performance. He also covers basic and advanced kernel tuning and monitoring, including the use of Java and Node.js flame graphs and performance counters.
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