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System Design Lab›System Design Questions›Design Food Delivery (DoorDash)

Design Food Delivery (DoorDash)

MediumE-commercegeolocationmatchingreal-timepayments⬛ Uber

Question Overview

Design a three-sided marketplace connecting customers, restaurants, and couriers for on-demand food delivery. Key challenges include geo search for restaurants, a reliable order and payment flow, ingesting a constant stream of courier GPS updates, matching orders to couriers, and live tracking with ETAs.…

Requirements

Browse and search restaurants that deliver to the customer's address, with live menus and hours
Place an order and pay exactly once, with the order moving through a clear status state machine
Dispatch each order to one available courier near the restaurant, timed to food preparation
Live courier location and ETA on the customer's map, updated every few seconds
Restaurant and courier apps to accept, prepare, pick up, and complete orders
Browse under 200 ms at p99 and 99.95% availability through lunch and dinner peaks

Back-of-the-envelope numbers

Orders: 10M/day ÷ 86,400 s ≈ 116/s average, ~350/s at 3× meal peak
Browse: 20 requests per order × 10M = 200M/day ÷ 86,400 s ≈ 2.3K QPS average, ~7K at peak, mostly cacheable
Courier GPS: 500K couriers ÷ 4 s = 125K location writes/s at peak, which belong in an in-memory geo index, not the order database
Location history: 250K average couriers ÷ 4 s ≈ 62.5K/s × 86,400 s ≈ 5.4B points/day × 100 B ≈ 540 GB/day, ~16 TB for 30 days
In-flight deliveries: 350 orders/s × 1,800 s (30 minutes) ≈ 630K at peak, served by 500K couriers with some batched orders
Live tracking: up to 630K watched orders ÷ 4 s update interval ≈ 160K pushes/s to customers at peak
Menus: 500K restaurants × 100 items × ~1 KB ≈ 50 GB, small enough to cache fully and index for search

Key components

Restaurant and menu service with a geo index (geohash or H3 cells, or Elasticsearch geo queries) filtered by delivery zone and hours, heavily cached
Order service: an order state machine in a relational database with idempotency keys; payment authorized at checkout and captured on completion
Courier location service that ingests GPS pings, keeps latest positions in an in-memory geo index such as Redis GEO, and streams history to Kafka
Dispatch service that batches pending orders per zone every few seconds and solves an assignment using distance, prep time, and batching
Offer protocol with timeouts: couriers get time-limited offers, and assignment is a conditional write so each order has exactly one courier
ETA service combining predicted prep time, routing-engine travel time, and historical data, recomputed as the courier moves
Tracking gateway pushing courier position and status to the customer's app over WebSocket or SSE, plus push notifications for key transitions

Common mistakes

Finding nearby couriers with latitude and longitude range scans on the relational database instead of an in-memory geospatial index
Writing every GPS ping to the transactional order database, adding 125K writes/s of short-lived data to the critical store
Assigning the closest courier the instant an order is placed, ignoring prep time, so couriers wait idle at restaurants
Assigning without an atomic compare-and-set, letting one courier receive conflicting orders or two couriers accept the same order
Charging the card on order placement without an idempotency key, so retries on flaky mobile networks double-charge
Treating menus as static, so customers order sold-out items or order from restaurants that are already overloaded

Likely follow-ups

How would you batch two orders from nearby restaurants onto one courier?
What happens if the assigned courier goes offline mid-delivery?
How would you handle a surge where orders far exceed available couriers in one area?
How would you estimate food preparation time for each restaurant?
How would you partition dispatch by geography, and handle orders near a region boundary?
How would you keep ETAs accurate as traffic and restaurant delays change?

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