Given the importance of time in our services and the need to scale, java.time works much better than primitives.
Category Archives: engineering
Building Riviera: A Declarative Real-Time Feature Engineering Framework
In a business with fluid dynamics between customers, drivers, and merchants, real-time data helps make crucial decisions which grow our business and delights our customers.
Building Multiple Distinctly Branded iOS Apps from a Single Codebase
A scalable solution for supporting multiple iOS apps means leveraging a common app library and design language system.
Using a Decision Engine to Power a First Class Customer Experience
DoorDash’s decision engine empowers customer service agents to deliver consistent, effective solutions for customer issues.
Why Good Forecasts Treat Human Input as Part of the Model
At DoorDash, getting forecasting right is critical to the success of our logistics-driven business, but historical data alone isn’t enough to predict future demand.
How to Make Kafka Consumer Compatible with Gevent in Python
Asynchronous task management using Gevent improves scalability and resource efficiency for distributed systems.
How to Drive Effective Data Science Communication with Cross-Functional Teams
Analytics teams focused on detecting meaningful business insights may overlook the need to effectively communicate those insights to their cross-functional partners who can use those recommendations to improve the business.
Serving Multiple Websites and Business Logic From a Single Platform
Building flexibility into the DoorDash platform lets us scale to serve a variety of retailers.
Running Experiments with Google Adwords for Campaign Optimization
Running experiments on marketing channels involves many challenges, yet at DoorDash, we found a number of ways to optimize our marketing with rigorous testing on our digital ad platforms.
Building a More Reliable Checkout Service at Scale with Kotlin
In 2020, DoorDash engineers extracted the consumer order checkout flow out of our monolithic service and reimplemented it in a new Kotlin microservice service.