Case Studies > Enabling 2M + Concurrent Users at Twitch with Scale, Simplicity and High Availability

Enabling 2M + Concurrent Users at Twitch with Scale, Simplicity and High Availability

Company Size
1,000+
Region
  • America
Country
  • United States
Product
  • Redis Labs Enterprise Cluster
  • Redis
  • Cassandra
  • Elasticsearch
  • DynamoDB
Tech Stack
  • Redis
  • Cassandra
  • Elasticsearch
  • DynamoDB
  • Rails
Implementation Scale
  • Enterprise-wide Deployment
Impact Metrics
  • Customer Satisfaction
  • Digital Expertise
  • Productivity Improvements
Technology Category
  • Analytics & Modeling - Real Time Analytics
  • Application Infrastructure & Middleware - Data Exchange & Integration
  • Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
  • Software
  • Telecommunications
Applicable Functions
  • Business Operation
  • Quality Assurance
Use Cases
  • Predictive Maintenance
  • Real-Time Location System (RTLS)
  • Remote Collaboration
Services
  • Cloud Planning, Design & Implementation Services
  • Software Design & Engineering Services
  • System Integration
About The Customer
Twitch is the world’s leading social video platform and community for gamers, bringing together over 100 million community members. The platform allows users to watch and talk about video games from over 1.7 million broadcasters. Twitch's engineering team is dedicated to providing extremely fast response times, high availability, and reliability for their web application. The platform is architected to handle incredible scale, with over 2 million concurrent viewers at peak times. Twitch uses several open-source technologies to manage their data backend, including Redis, Cassandra, Elasticsearch, and DynamoDB.
The Challenge
Twitch, the world’s leading social video platform and community for gamers, faced the challenge of managing an extremely high volume of concurrent users. With over 100 million community members and up to 2 million concurrent visitors, Twitch needed a robust solution to handle their website-wide chat functionality. The chat rooms often scaled up to 400,000+ users, requiring low latency and high availability to ensure a seamless user experience. Additionally, Twitch's engineering team sought operational simplicity and reliability to focus on delivering the best possible experience to their users.
The Solution
To address their challenges, Twitch chose Redis Labs Enterprise Cluster (RLEC) to power their chat application. Redis was initially selected for its blazing fast performance, operational simplicity, and optimized data structures for rapid, high-scale data processing. As Twitch's infrastructure became more complex, they moved additional functionalities such as token caching and view counting from Cassandra to Redis. Redis Labs provided a managed service with VPC peering to Twitch’s infrastructure, which runs extensively in Amazon Web Services. This setup eliminated the need for Twitch to build operational expertise to manage Redis in production. Redis Labs Enterprise Cluster offered high availability, reliability, and minimal operational overhead, allowing Twitch to focus on enhancing their user experience.
Operational Impact
  • Redis Labs Enterprise Cluster provided Twitch with zero operational hassle and no maintenance worries.
  • The solution ensured true high availability with no outages or latency issues, critical for Twitch's high-volume chat application.
  • Twitch's engineering team experienced low engineering effort required to manage Redis, allowing them to focus on other critical tasks.
  • The managed service setup with VPC peering to Twitch’s infrastructure simplified operations and reduced the need for in-house operational expertise.
  • Redis Labs' solution helped Twitch achieve their goal of growing confidence in their infrastructure's ability to handle scale.
Quantitative Benefit
  • Over 100 million community members and up to 2 million concurrent visitors managed seamlessly.
  • Chat rooms scaled up to 400,000+ users with low latency and high availability.
  • Redis Labs Enterprise Cluster eliminated the need for client-side sharding, clustering, and scaling.

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