DoorDash engineers built Infra Prober, a new monitoring tool, to continually look for component failures and provide accurate alerts.
Category Archives: engineering
Building Flexible Ensemble ML Models with a Computational Graph
DoorDash extended its machine learning platform to support ensemble models.
Improving Scalability, Reliability, and Efficiency of a Python Service with Gevent
When trying to scale a distributed system a common obstacle is not that there aren‚Äôt enough resources available, it’s that they are not being used efficiently.
Wanted: Data Scientists with Technical Brilliance AND Business Sense
DoorDash seeks data scientists who prioritize the business impacts of their work.
Building a gRPC Client Standard with Open Source to Boost Reliability and Velocity
In a microservice architecture, cross-service communication happens under a set of global rules that are hard to effectively enforce across all services without standardizing client-service communication.
Platform Optimization Through Better API Design
As DoorDash migrated to a microservices architecture, we found an opportunity to redesign our APIs, resulting in better overall client performance.
Tailoring Gradle and Docker for Rapid Local Development
As technology companies race to release their next features, any delay in productivity can be extremely detrimental, making an efficient development build process essential.
Companies that use Kubernetes and Docker in production environments most likely use Docker for local development.
2020 Hindsight: Building Reliability and Innovating at DoorDash
DoorDash recaps a number of its engineering highlights from 2020, including its microservices architecture, data platform, and new frontend development.
Implementing Theming in DoorDash’s Design Language System
Adding the concept of Theming to DoorDash’s design language system made it easier for engineers to use standardized design elements in all of our products.
Things Not Strings: Understanding Search Intent with Better Recall
For every growing company using an out-of-the-box search solution there comes a point when the corpus and query volume get so big that developing a system to understand user search intent is needed to consistently show relevant results.
We ran into a similar problem at DoorDash where, after we set up a basic “out-of-the-box” search engine, the team focused largely on reliability.