AWS Case Study: McDonald's Home Delivery

Summary

In this talk, McDonald's discusses its innovative home delivery platform built for massive scalability, speed to market, and reliability. The system, leveraging AWS services like ECS, microservices, and auto-scaling, handles 250,000 to 500,000 transactions per hour with sub-100 millisecond response times. The architecture ensures high performance with cost-sensitive operations, secure containers, and DevOps integration. Challenges faced, including memory errors and network routing issues, were overcome with custom solutions. The success of the platform highlights the importance of microservices, containerization, and cloud technologies in achieving large-scale digital transformation.

Takeaways

• 😀 **Rapid Development Cycle:** The platform was developed in just four months, enabling McDonald’s to quickly launch its home delivery service across multiple countries with varying business requirements.
• 😀 **Scalability is Key:** The platform was designed to handle massive scalability, supporting up to 500,000 transactions per hour and peak loads of 20,000 TPS while maintaining response times of under 100ms.
• 😀 **Microservices Architecture:** The use of AWS ECS to orchestrate microservices allowed McDonald’s to manage and scale the platform effectively, ensuring reliability and flexibility across its global infrastructure.
• 😀 **Containerization Benefits:** Migrating to containers simplified deployment, reduced complexity, and accelerated the development-to-production pipeline, making it easier to manage and scale services.
• 😀 **Seamless Third-Party Integration:** The platform integrates easily with third-party services, such as Uber Eats, through REST APIs and API Gateway, supporting McDonald’s diverse set of delivery partners.
• 😀 **Network Customization:** The team faced network routing challenges that required custom solutions to route traffic through secondary network interfaces, which AWS later addressed with the AWS VPC solution.
• 😀 **Memory Management Challenges:** Containers initially struggled with memory allocation due to improper memory limit recognition, leading to out-of-memory errors. The issue was addressed by virtualizing the container memory using LS CFS files.
• 😀 **Auto-Scaling Efficiency:** AWS auto-scaling policies for ECS instances and tasks allowed the platform to adjust resource allocation dynamically based on traffic load, ensuring both cost-efficiency and optimal performance.
• 😀 **DevOps Pipeline Automation:** Jenkins and Terraform were used for continuous integration and deployment, automating tasks like testing, image building, and deployment to ECS, thus improving the speed and quality of releases.
• 😀 **Monitoring and Logging with ELK Stack:** Logs and telemetry data are aggregated and visualized through the ELK stack (Elasticsearch, Logstash, Kibana), providing real-time insights into system performance and enabling quick issue detection.

Q & A

What was the primary business challenge McDonald's faced with their home delivery platform?

McDonald's needed a solution to scale their home delivery platform to handle a very high volume of transactions, ranging from 250,000 to 500,000 transactions per hour, while maintaining low latency (under 100 milliseconds) for each request.

How did McDonald's address the need for scalability in their platform?

McDonald's adopted a microservices architecture deployed on Amazon ECS, leveraging ECS's auto-scaling and task placement strategies to dynamically adjust resources based on demand. This approach helped achieve the required scalability while maintaining reliability.

What AWS services were used to support McDonald's home delivery platform?

McDonald's used a variety of AWS services including ECS (Elastic Container Service), API Gateway, SQS (Simple Queue Service), Redis (via ElastiCache), RDS (Relational Database Service), and S3 (Simple Storage Service), among others, to handle different aspects of scalability, security, and data storage.

How did McDonald's ensure low-latency and high-speed transactions in their platform?

McDonald's implemented distributed caching using Redis and ElastiCache, which significantly reduced latency and ensured fast response times. Additionally, the ECS infrastructure allowed for rapid scaling to handle large transaction volumes without performance degradation.

What challenge did McDonald's face with container memory management, and how was it resolved?

McDonald's encountered out-of-memory errors in containers because the application runtime inside the container wasn't correctly recognizing the container's memory limits. They initially worked around this issue using the LSCFS filesystem to virtualize C groups and ensure proper memory management. Later, ECS released a solution that directly bound containers to network interfaces, which improved memory management.

Why did McDonald's need to customize their Docker bridge networking setup?

McDonald's needed to route container traffic through a secondary elastic network interface, which was not supported by the default Docker bridge network. They implemented custom configurations to route traffic properly, but AWS later released an updated solution called AWS VPC Container Networking, which resolved the issue by allowing direct binding of network interfaces to containers.

What role did AWS VPC Container Networking play in McDonald's architecture?

AWS VPC Container Networking allowed McDonald's to bind each container to a specific elastic network interface, enabling granular control over network traffic and enhancing security. This solution provided more flexibility and made it easier to meet security and routing requirements for their platform.

How did McDonald's approach security in their containerized platform?

McDonald's applied the principle of least privilege to their containerized platform by ensuring that each service only had the permissions it needed to function. They also implemented regular patching of ECS instances and used AWS optimized AMIs for security compliance.

What monitoring tools did McDonald's use to ensure the performance of their platform?

McDonald's used AWS CloudWatch for infrastructure monitoring and integrated it with the Elastic Stack (ELK) for log aggregation, analysis, and visualization. These tools provided real-time insights into system performance and helped with troubleshooting.

What were the key lessons McDonald's learned from their experience with AWS ECS?

McDonald's learned the importance of a solid microservice strategy, which enabled scalability and reliability. They also discovered that containerization simplifies deployment and management, significantly improving speed to market and reducing complexity. ECS's built-in features, like auto-scaling and seamless integration with other AWS services, played a critical role in the platform's success.