How Doordash Rebuilt Delivery Logistics for Peak Demand

Episode notes

This episode of The CTO Podcast dives into how Doordash rebuilt its delivery logistics platform to handle peak demand during the COVID-19 pandemic and beyond. Lucas and Luna explore the specific architecture decisions behind Doordash's real-time dispatching system, which processes millions of orders per day with sub-second latency. They discuss the shift from a monolithic Rails backend to a microservices-based system using Kotlin and gRPC, the use of geospatial indexing with PostGIS for proximity search, and the implementation of a custom time-series database for demand forecasting. The conversation also covers how Doordash handles 'dark store' inventory synchronization and the trade-offs between centralized and decentralized routing algorithms. Listeners will learn one concrete insight: how Doordash uses a technique called 'batch optimization with rolling horizons' to balance driver utilization and customer wait times. Perfect for CTOs, engineering leaders, and anyone interested in the operational challenges of real-time logistics at scale.

• How Airbnb Rebuilt Search for 8 Million Listings62 / 100
• How GitLab Built a Single Codebase for One Million CI Pipelines65 / 100
• How Slack Rebuilt Its Search Index for 10 Million Daily Queries57 / 100
• How Notion Rebuilt Its Sync Engine for Offline-First
• How Notion Rebuilt Its Block Engine for Hybrid Local-Sync