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Tobi Lutke wrote the first line of code for Shopify nearly 10 years ago to power his own Snowboard shop. Two years later Shopify launched to the public on a single webserver using Rails 0.13.1. Today Shopify powers over 40k online stores and processes up to half a million product sales per day across the platform. Over 30 people actively work on Shopify which makes it the longest developed and likely largest Rails code base out there.

This is the story of how Shopify has evolved to handle its immense growth over the years. This is what getting big is all about: evolving to meet the needs of your customers. You don't start out with a system and infrastructure that can handle a billion dollar in GMV. You evolve to it. You evolve by adding caching layers, hardware, queuing systems and splitting your application to services.

This is the story of how we have tackled the various scaling pain points that Shopify has hit and what we have done to surpass them, what we are doing to go even further.

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http://amara.org/v/FGdU/

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The video titled "How Shopify Scales Rails" features John Duff discussing the evolution of Shopify from its inception to its current state as a leading e-commerce platform powered by Ruby on Rails. Shopify, founded nearly ten years ago, has grown to support over 40,000 online stores and processes up to half a million product sales daily. The video outlines the extensive scaling challenges Shopify has faced and how the company has evolved to handle these issues effectively through various strategies. 
 
Key points discussed in the video include:
- **Initial Setup and Evolution**: Shopify's journey began in 2004 with a simple code base that has since expanded significantly while still using the same foundational architecture. Initial versions used Ruby 1.8.2 and a pre-1.0 version of Rails, which have evolved substantially over the years.
- **Current Technical Stack**: The current stack incorporates Ruby 1.9.3, Rails 3.2, MySQL with performance enhancements, and enterprise load handling with Unicorn and caching layers using Memcache and Redis. The system supports thousands of requests per minute while maintaining low response times.
- **Scaling Challenges**: Duff emphasizes the importance of understanding system constraints, optimizing for the storefront, and the prioritization necessary for effective scaling practices. Monitoring tools such as New Relic and Splunk play vital roles in performance management.
- **Caching Strategies**: The adoption of caching technologies like Cachable and Identity Cache has allowed Shopify to increase efficiency by reducing unnecessary database hits and improving response times.
- **Optimizing Background Jobs**: Transitioning to a Redis-backed system for background job processing enhanced performance significantly.
- **Database Optimization**: Utilizing high-performance hardware alongside query optimizations and adjusting MySQL configurations ensures efficient data handling.
- **Service Segmentation**: Separating services based on system metrics has enabled Shopify to independently scale components like image handling, ultimately improving performance.

In conclusion, John Duff highlights that adaptability and a data-driven approach to scaling have been essential to Shopify's success. By continuously monitoring performance metrics and adjusting strategies accordingly, businesses can effectively navigate growth challenges in complex systems.

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