Learn how Shopify scaled its inventory reservations by replacing Redis with MySQL, achieving improved performance and reliability.
Shopify’s decision-making-for-retail-success/">inventory management system has undergone a significant transformation. With the need to seamlessly manage high sales volumes, especially during peak times, the company decided to transition from using Redis to MySQL for handling inventory reservations. This shift not only improved the performance of their reservation system but also ensured greater reliability and accuracy—crucial factors in e-commerce where every millisecond counts.
At Shopify, oversell protection is vital for maintaining technology/">customer satisfaction and operational integrity. When buyers finalize a purchase, it’s critical that the items they are attempting to buy are still available. Mismanaging inventory can lead to overselling, where multiple customers attempt to purchase the same item, resulting in canceled orders and unhappy customers. Conversely, inaccurately marking items as sold out leads to lost sales opportunities.
During peak periods like Black Friday, when transactions peak at extraordinary levels, the need for robust oversell protection grows. For instance, in Black Friday 2025, merchants on the Shopify platform processed an astonishing $5.1 million in sales per minute at peak times. Every transaction relies on accurate inventory checks, making the resiliency of their reservation system paramount.
Initially, Shopify relied on Redis for inventory reservations, but the dual-system approach—running inventory in Redis while maintaining the ledger in MySQL—proved cumbersome. The challenge was that both operations (reserving items and updating inventory) couldn't be wrapped in a single atomic transaction, leading to either overselling or underselling during peak loads.
Despite facing challenges with MySQL in the past, Shopify decided to leverage the new SKIP LOCKED feature in MySQL 8, which allowed for a new design approach that utilized one row per sellable unit rather than one row per item. This strategy was inspired by 37signals' database-backed load distribution methodology, paving the way for high throughput achieve during intense traffic.
The fundamental idea was to use composite primary keys, which enabled fewer locks per row, as opposed to a single row containing a quantity column. This shift led to a reduction in lock contention—a critical improvement at the high-volume transactions that Shopify processes. The composite key structure included fields like shop_id and inventory_item_id to ensure efficiency and minimize locking issues.
Implementing a READ COMMITTED isolation level ensured that no gap locks would impede replenishment transactions, thus helping maintain operational integrity. This operational redesign formed the backbone of their new inventory reservation approach, ensuring that processing speed did not compromise transactional accuracy.
While optimizing their new MySQL-based system, the Shopify team encountered unexpected bottlenecks—not in CPU usage or query performance, but in connection handling. Through meticulous monitoring, they gained insights into connection usage patterns among various business processes, revealing that many longer-running transactions were inadvertently exhausting database connections.
The incorporation of connection visibility allowed them to tag every SQL statement, making it simpler to track which operations held connections longest. Armed with this granular data, the team discovered that certain processes were using a disproportionate amount of connection time, ultimately impacting the reservation system’s efficiency without it being the primary issue across the whole checkout path.
In response, the development team initiated a cleanup of the checkout procedures, which translated into significant reductions in database reads and transactions—decreasing read traffic to the primary database by 50% and transactions by 33%. Adjustments to MySQL’s configuration, specifically increasing thread concurrency, further alleviated some of the constraints they faced, enabling them to scale effectively.
Shopify did not switch from Redis to MySQL overnight. The company operated the two systems concurrently in a shadow mode. During this period, every reservation was written to both Redis and MySQL, using Redis as the source of truth while they validated the performance and accuracy of MySQL against real production traffic.
This parallel operation allowed Shopify to assess real-time performance without the risks of losing data or compromising customer experience. Once satisfied, they gradually transitioned the source of truth to MySQL, ensuring that if any discrepancies arose, they could revert operations seamlessly.
The rollout strategy was methodical, starting from lower-traffic pods to the busiest ones, minimizing disruptions to merchant sales processes. This attentive approach made the eventual switch successful, providing a robust and reliable operational framework that directly improved inventory management at scale.
The journey from Redis to MySQL came with valuable lessons. One significant takeaway was the realization that MySQL, equipped with new capabilities like SKIP LOCKED, is capable of handling workloads traditionally considered suitable only for specialized architectures. Additionally, the importance of revisiting long-standing configurations in light of evolving workloads cannot be overstated—what may have been deemed impractical several years ago could well be a viable solution today.
As e-commerce continues to grow, understanding the underlying mechanics of inventory systems and effectively optimizing connection usage will remain critical for companies like Shopify. More reliable reservations mean fewer oversells, enhanced customer satisfaction, and better sales outcomes for merchants. The operational adjustments made during the transition have not only set the stage for current workload management but also paved the way for future growth.
What are the benefits of using MySQL over Redis for inventory reservations?
MySQL's features like SKIP LOCKED help manage high volumes of transactions without contention and allow for atomic transactions, enhancing accuracy in inventory management.
How did Shopify ensure a smooth transition from Redis to MySQL?
Shopify operated both systems in parallel (shadow mode) while validating performance and accuracy before fully transitioning the source of truth to MySQL.
What challenges did Shopify encounter during the transition?
The primary challenges included managing database connections, optimizing query performance, and ensuring accurate inventory counts amidst peak transaction demands.