Real-Time Ride-Hailing Architecture: Executive Summary

Answer-first: Real-time ride-hailing platforms combine HTTP/3 gRPC stream ingestion for driver GPS telemetry, Uber H3 hexagonal spatial indexing in Redis RAM, Apache Kafka/Redpanda event streaming, and DISCO global assignment matching engines to dispatch rides in under 2 seconds. Key Takeaways: Telemetry Scale: Ingest driver GPS coordinates every 4 seconds using Extended Kalman Filters and binary gRPC Protobuf streams over HTTP/3 QUIC. Spatial Pre-filtering: Index driver positions using Uber H3 Resolution 8 cells (~0.74 km²), isolating nearest candidates in <10ms. Global Matching Optimization: DISCO batched matching aggregates ride requests every 2-5 seconds, solving bipartite graph assignment problems for minimal ETA. What You’ll Learn: ...

May 6, 2026 · 9 min · Lê Tuấn Anh

Ride-Hailing GPS Location Ingestion Pipeline in Go

Prerequisite: Before reading this part, review the Executive Summary. GPS Ingestion at Scale: gRPC Streaming, MQTT & Kalman Filter Answer-first: High-throughput location ingestion processes over 1 million GPS updates per second by using binary gRPC streams or MQTT over persistent TCP/QUIC connections. Devices run Kalman filters and dead-reckoning interpolation to clean telemetry noise before publishing updates to Apache Kafka and Redis. Key Takeaways: Protocol Overhead: Replacing HTTP REST with gRPC Protobuf binary framing (vtproto) reduces packet overhead from 800 bytes to 40 bytes per GPS update. Noise Reduction: Kalman filters apply prediction-correction matrix equations directly on handset sensors to eliminate urban canyon GPS reflections. Batching Savings: Aggregating 3-5 telemetry points into single gRPC frames saves up to 67% of mobile radio transmission energy. What You’ll Learn: ...

May 6, 2026 · 10 min · Lê Tuấn Anh

Uber H3 Geospatial Indexing: Redis Driver Discovery

Prerequisite: Familiarity with the concepts introduced in Part 1 — Location Ingestion. Review it first if the terminology in this part is unfamiliar. Answer-first: Uber and Grab find the nearest available driver in under 100ms by dividing the Earth’s surface into hexagonal cells (H3 index at Resolution 8, each ~0.74 km²). Instead of calculating distance to every driver, they look up only the 7 cells nearest to the rider — reducing millions of comparisons to dozens. ...

May 6, 2026 · 12 min · Lê Tuấn Anh