Netflix is the world’s leading internet television network, with more than 100 million members worldwide enjoying 125 million hours of TV shows and movies each day, including original series, documentaries, and feature films. Members can watch as much as they want, anytime, anywhere, on nearly any Internet-connected screen.
“Amazon Kinesis Data 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.”
Senior Software Engineer, Netflix
Netflix uses Amazon Web Services (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 ultimately deployed known internally as Dredge centralizes flow logs using Amazon Kinesis Data Streams. The application reads the data from Amazon Kinesis Data Streams in real time and enriches IP addresses with application metadata to provide a full picture of the networking environment. “Usually, we would put the data into a database, which would build an index to enable faster querying,” says Bennett. “Dredge joins the flow logs with application metadata as it streams and indexes it without using a database, which eliminates a lot of the complexity.
KDS (KINESIS DATA STREAMS)
Amazon Kinesis Data Streams (KDS) is a massively scalable and durable real-time data streaming service. KDS can continuously capture gigabytes of data per second from hundreds of thousands of sources such as website clickstreams, database event streams, financial transactions, social media feeds, IT logs, and location-tracking events. The data collected is available in milliseconds to enable real-time analytics use cases such as real-time dashboards, real-time anomaly detection, dynamic pricing, and more.
→Easy to use.
→High Computational power
KDS with AUTO SCALING
As your streaming information increases, you require a scaling solution to accommodate all requests. If you have a decrease in streaming information, you might use scaling to reduce costs. Currently, you scale an Amazon Kinesis Data Stream shard programmatically. Alternatively, you can use the Amazon Kinesis Scaling Utilities. To do so, you can use each utility manually, or automated with an AWS Elastic Beanstalk environment
As you can see from the diagram, AWS System Manager Parameter Store is also involved. We use Parameter Store to store the desired capacity value that Application Auto Scaling sends to API Gateway to increase or decrease the capacity. (In this scenario, the capacity is the number of shards.) In fact, Application Auto Scaling often invokes API Gateway to get the status of the custom resource, in this case the Kinesis Data Stream. It does so to see if there are actions to be taken and if previous actions were successful. Because Lambda is stateless, we need somewhere to save the desired capacity value communicated by Application Auto Scaling at any point
Application Auto Scaling can scale up and down continuously to make sure that you can meet your demand. However, Kinesis Data Streams have some limitations to consider when configuring Application Auto Scaling. With Kinesis Data Streams, you can’t do the following:
- Scale more than ten times per rolling 24-hour period per stream
- Scale up to more than double your current shard count for a stream
- Scale down below half your current shard count for a stream
- Scale up to more than 10000 shards in a stream
- Scale a stream with more than 10000 shards down unless the result is less than 10000 shards
- Scale up to more than the shard limit for your account
→In this blog you can see how Netflix uses AWS services.
→How KDS working for streaming purpose and also about AWS Lambda.
→Like wise most of the companies gets benefitted from the AWS.
→For all kind of use cases AWS provides services.