Three key steps are of paramount importance to prevent outages in microservice applications, especially those that depend on cloud services: Identify the potential causes for system failure, prepare for them, and test countermeasures before failure occurs.
Category Archives: engineering
Building the Model Behind DoorDash’s Expansive Merchant Selection
An optimized merchant selection strategy has been one of the key factors that has enabled DoorDash to become an industry leader in US food delivery service.
6 questions with DoorDash’s New VP of Engineering, Liangxiao Zhu
Welcome Liangxiao to DoorDash!
3 Principles for Building an ML Platform That Will Sustain Hypergrowth
Taking full advantage of a large and diverse set of machine learning (ML) use cases calls for creating a centralized platform that can support new business initiatives, improve user experiences, enhance operational efficiency, and accelerate overall ML adoption.
Using Gamma Distribution to Improve Long-Tail Event Predictions
For DoorDash, being able to predict long-tail events related to delivery times is critical to ensuring consumers’ orders arrive when expected.
Building a Common Web Library for Fast Implementations of Risk Frictions
To safeguard our users’ accounts and prevent fraud, we sometimes ask users to verify their identity or confirm a transaction by completing a “user friction” such as two-factor authentication.
Improving Web Page Performance at DoorDash Through Server-Side Rendering with Next.JS
Large e-commerce companies often face the challenge of displaying enticing product images while also ensuring fast loading speeds on high-traffic pages of their website.
How to leverage functional programming in Kotlin to write better, cleaner code
As DoorDash transitioned from Python monolith to Kotlin microservices, our engineering team was presented with a lot of opportunities to improve operational excellence and continue our obsession with reliability.
Using a Multi-Armed Bandit with Thompson Sampling to Identify Responsive Dashers
Maintaining Dasher supply to meet consumer demand is one of the most important problems for DoorDash to resolve in order to offer timely deliveries.
Ship to Production, Darkly: Moving Fast, Staying Safe with ML Deployments
At DoorDash, machine learning (ML) models are invoked many millions of times each day.