DoorDash Engineering Blog

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

Newest Article

Backend Mobile Web Overcoming Localization Challenges for International Expansions

DoorDash defined four key challenges to getting its platform ready for an international launch.

Noah Zempsky

Ethan Myers

Siddhartha Kakarla

Winston Zhao

Bryan Huang

Xilin Liu

18 Minute Read

Featured Article

Backend Migrating From Python to Kotlin for Our Backend Services

To support our migration to microservices we needed to find a new tech stack. Learn how we compared all the options and chose Kotlin

Matt Anger

14 Minute Read

Mobile Web

Building Chat Into the DoorDash App to Improve Deliveries

Learn how we overcame the technical challenges of implementing chat quickly and efficiently for all our Dasher and consumer applications.

Marina Mukhina 9 Minute Read

Backend Web

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.

Sin Ko

Li Pei 18 Minute Read

Machine Learning

Maintaining Machine Learning Model Accuracy Through Monitoring

Machine learning model drift occurs as data changes, but a robust monitoring system helps maintain integrity.

Swaroop Chitlur

Kornel Csernai 18 Minute Read

Culture

How DoorDash Leverages Hackathons to Drive Innovation

Learn how our three pillar approach to hackathons results in successful events

Kosha Shah

Raza Padhani 7 Minute Read

General

DoorDash Expands International Presence with Toronto Tech Hub

DoorDash announces the opening of its newest and first international engineering office in Toronto.

Qaiser Habib 3 Minute Read

Machine Learning

Improving ETA Prediction Accuracy for Long-tail Events

Learn how we managed to better predict long tail delivery estimations using historical and realtime features as well as custom loss functions

Dawn Lu

Pratik Parekh 12 Minute Read

Web

Managing React State on DoorDash’s Item Modal Using the Class Pattern

Learn how DoorDash created the Class Pattern when building the Item Modal in its web application to increase reliability.

Winston Zhao 33 Minute Read

Backend General

Gradual Code Releases Using an In-House Kubernetes Canary Controller

New service releases deployed into DoorDash’s microservice architecture immediately begin processing and serving their entire volume of production traffic. If one of those services is buggy, however, customers may have a degraded experience or the site may go down completely. Although we currently have a traffic management solution under development for gradual service rollouts as ...

MarcoChirico 15 Minute Read

Culture General

What to Expect from DoorDash’s Technical Interview

Follow these helpful tips when preparing for DoorDash's technical interview.

Kirtan Patel 15 Minute Read

Backend General

Optimizing OpenTelemetry’s Span Processor for High Throughput and Low CPU Costs

When companies move to microservices, they need to address a new challenge of setting up distributed tracing to identify availability or performance issues throughout the platform. While various tools offered on the market or through open-source perform this task, there is often a lack of standardization, making leveraging these tools costly or complicated. As DoorDash ...

Santosh Banda 24 Minute Read

Backend General

Examining Problematic Memory in C/C++ Applications with BPF, perf, and Memcheck

As applications grow in complexity, memory stability is often neglected, causing problems to appear over time. When applications experience consequences of problematic memory implementations, developers may find it difficult to pinpoint the root cause. While there are tools available that automate detecting memory issues, those tools often require re-running the application in special environments, resulting ...

Filip Busic 47 Minute Read

General Machine Learning

Best Practices for Regression-free Machine Learning Model Migrations

Migrating functionalities from a legacy system to a new service is a fairly common endeavor, but moving machine learning (ML) models is much more challenging. Successful migrations ensure that the new service works as well or better than its legacy version. But the complexity of ML models makes regressions more likely to happen and harder ...

Ying Chi 14 Minute Read