The document discusses various technologies used by ride-sharing services like Uber, Lyft, and Cabify for efficient spatial indexing and dispatch optimization. It highlights the importance of GPS and a distributed system using hashing for managing workloads and improving estimated time of arrival (ETA) calculations. Additionally, it briefly mentions tools like Terraform, Docker, and Mesos used in their infrastructure.

1. UBER, LYFT, CABIFY

2. - S2 Library (Spatial Indexing)
- Efficient query operations for
finding nearby objects,
measuring distances,
computing centroids, etc.
- Kafka
- DISCO (Dispatch Optimization)
- Distribute the work in different
servers using a hashing system
- GPS is essential for the system to
work

3. - Break the World Map into pieces
- Divide Workloads into Services.
- Easier to calculate ETA.
- Match Supply and Demand in the area.

5. - Know what servers handles each
location requests, and how to reshard
when necessary using hashing.
- Demand Service ask Supply Service by
Location ID (s2)
- Supply Service find the cab.
- Request is placed to cab by oder.
- After a time-out try another cab.
- If it a match notify the user.

6. - Terraform
- Docker
- Mesos
- Regions
- Kafka - Hadoop

7. - Calculate ETA (pickup, destiny)
- Calculate different options to find the best.

13. - Tech Dummies
- Success in Tech
- Quora Thread
- Pratik Jain
- Uber Engineering Blog