How Disney Hotstar Achieved its Impressive Live Streaming Feat?

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By Rahul Miglani , VP Engineering , DevOps

What did they Do?
More than 1.3 Crores people were watching India vs Pakistan game live without any lag, buffering or frame loss. They prepared for this event & made a lot of efforts in managing user traffic & load where other applications from the shared resource pool are also trying to pull cloud resources. Big applause to Hotstar’s DevOps team who managed to live stream the match to 10 crore people.

Cloud Infrastructure
Hotstar deployed AWS Elastic Compute instances of type c5 9Xlarge which consists of 36 CPUs & 72 GB RAM & has 10 Gbps of network bandwidth. Thousands of EC2 servers were managed along with S3 storage for data storing regionally. Big thumbs-up for CDN partner Akamai for providing seamless content delivery for nearest end-users to reduce the latency & enhance watching experience.

Load Testing
To simulate & mimic the huge traffic load hotstar DevOps team spun up a large number of EC2 instances running at 75% utilization.
Team started to follow a simple load testing model in which they hit endpoints with a certain ratio of requests per second keeping in mind some users already have signed up & some will newly sign up. Then to mimic the event, the team made simulation scripts & ran on AWS infrastructure for 2-3 crores users.


Automation & Scaling
Auto Scaling is done on two models: traffic-based-scaling & Ladder based scaling. With increasing number of requests more resources added in the pool & to reduce delay team has pre-provisioned buffer. It helped to handle unexpected traffic spikes without delay or disruption.

Written by 

Rahul Miglani is Vice President at Knoldus and heads the DevOps Practice. He is a DevOps evangelist with a keen focus to build deep relationships with senior technical individuals as well as pre-sales from customers all over the globe to enable them to be DevOps and cloud advocates and help them achieve their automation journey. He also acts as a technical liaison between customers, service engineering teams, and the DevOps community as a whole. Rahul works with customers with the goal of making them solid references on the Cloud container services platforms and also participates as a thought leader in the docker, Kubernetes, container, cloud, and DevOps community. His proficiency includes rich experience in highly optimized, highly available architectural decision-making with an inclination towards logging, monitoring, security, governance, and visualization.