DoorDash engineers used a pipeline design pattern to make our recommendation page more efficient and flexible.
Category Archives: engineering
Managing Supply and Demand Balance Through Machine Learning
At DoorDash, we want our service to be a daily convenience offering timely deliveries and consistent pricing.
Overcoming Rapid Growth Challenges for Datasets in Snowflake
A proper optimization framework for data infrastructure streamlines engineering efforts, allowing platforms to scale.
Our June 19th Outage Explained
Between 16:30 PDT and 18:40 PDT on June 19th 2021, DoorDash experienced a system-wide failure for approximately two hours that saddled merchants with undelivered meals, rendered Dasher’s unable to accept new deliveries or check in for new shifts, and left consumers unable to order food or receive their placed orders in a timely fashion via our platform.
Leveraging OpenTelemetry For Custom Context Propagation
The ability to attach auxiliary metadata to requests within a large microservice architecture enables powerful use cases, such as infrastructure-level sharding, language localization, and testing-in-production.
Overcoming Localization Challenges for International Expansions
DoorDash defined four key challenges to getting its platform ready for an international launch.
Building Chat Into the DoorDash App to Improve Deliveries
Every delivery enabled by the DoorDash platform is different.
Rebuilding and Migrating a Session Management System with Zero Downtime
Migrating DoorDash’s business-critical session management system in a disruption-free manner required careful planning and monitoring.
Maintaining Machine Learning Model Accuracy Through Monitoring
Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.
DoorDash Expands International Presence with Toronto Tech Hub
DoorDash announces the opening of its newest and first international engineering office in Toronto.