DoorDash Engineering Blog

Learn about high impact projects that power our velocity, reliability, and innovation.

Featured Article

Data 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. Machine learning (ML) models play a big role in improving the experience on our platform, but models can only be as powerful as their underlying features. As a result, building ...

Allen Wang

Kunal Shah

17 Minute Read

Mobile

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.

David Phan 15 Minute Read

General

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.

Preetha Vijaya Saraswathi

Derrick Hu

Kevin Nguyen 8 Minute Read

Machine Learning

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. We need to ensure there are enough Dashers, our name for delivery drivers, in each market for timely order delivery. And even though it seems like people’s demand for food delivery ...

Brian Seo 10 Minute Read

Culture

How I Found Career Growth and a Work-Life Blend as a Mother in Tech

In September of 2019 I had one child and another on the way. At the same time, I was working as a software engineer, a career often notable for late nights and weekend work. In addition, my focus in supporting infrastructure usually requires a rotating on-call, where I might need to troubleshoot an outage outside ...

Manushree Ankapura 14 Minute Read

Backend

How to Make Kafka Consumer Compatible with Gevent in Python

Asynchronous task management using Gevent improves scalability and resource efficiency for distributed systems. However, using this tool with Kafka can be challenging. At DoorDash, many services are Python-based, including the technologies RabbitMQ and Celery, which were central to our platform’s asynchronous task-queue system. We also leverage Gevent, a coroutine-based concurrency library, to further improve the ...

Jessica Zhao

Boyang Wei 9 Minute Read

Data Machine Learning

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. Part of the DoorDash Analytics team’s success comes from its ability to communicate actionable insights to key stakeholders, not just identify and measure them. Many ...

James Williams

Lokesh Bisht 11 Minute Read

Backend Web

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.

Daniel Shafer 7 Minute Read

Data Machine Learning

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. While data scientists frequently run experiments, such as A/B tests, on new features, the methodology and results may not seem so clear when applied to digital ...

Yingying Chen

Heming Chen 16 Minute Read

Backend

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. This effort, part of our migration from a monolithic codebase to a microservices architecture, increases our platform’s performance, reliability, and scalability. The consumer checkout flow is one of the most critical ...

Yimin Wei

Zhengli Sun

Amiraj Dhawan 11 Minute Read

Backend

Using Active Probing to Detect Infrastructure Failures

DoorDash engineers built Infra Prober, a new monitoring tool, to continually look for component failures and provide accurate alerts.

Shannon Li 11 Minute Read

Data Machine Learning

Building Flexible Ensemble ML Models with a Computational Graph

DoorDash extended its machine learning platform to support ensemble models.

Hebo Yang

Arbaz Khan

Param Reddy 14 Minute Read

Backend

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’t enough resources available, it’s that they are not being used efficiently. At DoorDash we found a similar opportunity when working to scale our point-of-sale system (POS). We were experiencing outages because our POS system could not scale to meet peak ...

Simone Restelli

Devavrath Subramanyam 9 Minute Read