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.
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.
This results in an extremely complex and dynamic networking environment where applications are constantly communicating inside AWS and across the Internet. Monitoring and optimizing its network is critical for Netflix to continue improving customer experience, increasing efficiency, and reducing costs. In particular, Netflix needed a solution for ingesting, augmenting, and analyzing the multiple terabytes of data its network generates daily in the form of virtual private cloud (VPC) flow logs. This would enable Netflix to identify performance-improvement opportunities, such as identifying apps that are communicating across regions and collocating them. The company would also be able to increase uptime by quickly detecting and mitigating application downtime.
Each log record carries information about the communications between two IP addresses. However, in a dynamic environment like the one at Netflix, where an IP address can float between applications from day to day or even minute to minute, IP addresses alone don’t have much meaning. “The data sources we had before we took on this initiative were one sided,” says John Bennett, senior software engineer at Netflix. “We’d know an application was connecting to others, but we didn’t know both sides of the conversation and how to optimize those communications or the placement of the applications on the network.”
Netflix set out to establish a new data source that could give it more insight into communication among applications and regions by combining VPC flow logs with application metadata.
This results in an extremely complex and dynamic networking environment where applications are constantly communicating inside AWS and across the Internet. Monitoring and optimizing its network is critical for Netflix to continue improving customer experience, increasing efficiency, and reducing costs. In particular, Netflix needed a solution for ingesting, augmenting, and analyzing the multiple terabytes of data its network generates daily in the form of virtual private cloud (VPC) flow logs. This would enable Netflix to identify performance-improvement opportunities, such as identifying apps that are communicating across regions and collocating them. The company would also be able to increase uptime by quickly detecting and mitigating application downtime.
Each log record carries information about the communications between two IP addresses. However, in a dynamic environment like the one at Netflix, where an IP address can float between applications from day to day or even minute to minute, IP addresses alone don’t have much meaning. “The data sources we had before we took on this initiative were one sided,” says John Bennett, senior software engineer at Netflix. “We’d know an application was connecting to others, but we didn’t know both sides of the conversation and how to optimize those communications or the placement of the applications on the network.”
Netflix set out to establish a new data source that could give it more insight into communication among applications and regions by combining VPC flow logs with application metadata.
From the outset, AWS enabled Netflix to experiment with
different approaches to analyzing its network data. “Early in the design
process, the flexibility to try different ways of processing the data
was important,” says Bennett. “We experimented with multiple designs and
used many AWS products to get here.”
The solution Netflix ultimately deployed—known internally as Dredge—centralizes flow logs using Amazon Kinesis Streams. The application reads the data from Amazon Kinesis 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.”
The enriched data lands in an open-source analytics application called Druid. Netflix uses the OLAP querying functionality of Druid to quickly slice data into regions, availability zones, and time windows to visualize it and gain insight into how the network is behaving and performing.
AWS was the logical choice for Dredge in part because the data was already resident in the AWS Cloud. “It would have been daunting to publish, stream, and consume that much information from an external system such as Kafka,” says Bennett. “It took just a few API calls to centralize multiple terabytes of flow logs into Amazon Kinesis Streams. Now we can focus on getting insights from the data rather than simply getting access to it.”
The scalability of Amazon Kinesis Streams was a good fit for the Dredge application because of the cyclical and elastic nature of network usage at Netflix. “When it comes to our networking data, it’s more cost efficient to be able to scale up and down, which is not as easy to do with alternatives to Amazon Kinesis,” says Bennett.
The solution Netflix ultimately deployed—known internally as Dredge—centralizes flow logs using Amazon Kinesis Streams. The application reads the data from Amazon Kinesis 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.”
The enriched data lands in an open-source analytics application called Druid. Netflix uses the OLAP querying functionality of Druid to quickly slice data into regions, availability zones, and time windows to visualize it and gain insight into how the network is behaving and performing.
AWS was the logical choice for Dredge in part because the data was already resident in the AWS Cloud. “It would have been daunting to publish, stream, and consume that much information from an external system such as Kafka,” says Bennett. “It took just a few API calls to centralize multiple terabytes of flow logs into Amazon Kinesis Streams. Now we can focus on getting insights from the data rather than simply getting access to it.”
The scalability of Amazon Kinesis Streams was a good fit for the Dredge application because of the cyclical and elastic nature of network usage at Netflix. “When it comes to our networking data, it’s more cost efficient to be able to scale up and down, which is not as easy to do with alternatives to Amazon Kinesis,” says Bennett.
read more: https://aws.amazon.com/fr/solutions/case-studies/netflix-kinesis-streams/
https://www.slideshare.net/AmazonWebServices/how-netflix-uses-amazon-kinesis-streams-to-
monitor-and-optimize-largescale-networks-in-realtime
https://www.slideshare.net/AmazonWebServices/how-netflix-uses-amazon-kinesis-streams-to-monitor-and-optimize-largescale-networks-in-realtime
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