Uber architecture overview in Microservices - Scalability & System Analysis

Scalability Analysis - Uber architecture overview

Growth Table: Uber Architecture Overview

Scale	Users	Requests per Second	Data Volume	Key Changes
Small	100	~50-100	Few GBs	Monolithic or few microservices, single DB instance, simple load balancer
Medium	10,000	~5,000	TBs	Multiple microservices, DB read replicas, caching layers, API gateway
Large	1,000,000	~500,000	Petabytes	Service partitioning by region, sharded databases, distributed caches, message queues
Very Large	100,000,000	~50,000,000	Exabytes	Global multi-region deployment, advanced sharding, CDN for static content, autoscaling, event-driven architecture

First Bottleneck

At small to medium scale, the database is the first bottleneck. Uber's system needs to handle many writes and reads for rides, locations, and user data. A single database instance can only handle so many queries per second (around 5,000-10,000 QPS). As user count grows, the DB becomes slow and unresponsive, causing delays in matching riders and drivers.

Scaling Solutions

• Database scaling: Use read replicas to spread read load, and shard data by geography or user ID to distribute writes.
• Microservices: Break the system into smaller services (e.g., ride matching, payments, notifications) to scale independently.
• Caching: Use Redis or Memcached to cache frequent queries like driver locations and surge pricing.
• Message queues: Use Kafka or RabbitMQ for asynchronous processing (e.g., trip events, notifications) to smooth spikes.
• Load balancing: Distribute incoming requests across multiple app servers to avoid CPU/memory bottlenecks.
• CDN: For static content like app assets and map tiles, use CDN to reduce latency and bandwidth load.
• Autoscaling: Automatically add or remove servers based on traffic to optimize cost and performance.

Back-of-Envelope Cost Analysis

Assuming 1 million active users generating 500,000 requests per second:

• Database: Needs sharding and replicas to handle 500K QPS (each DB node ~10K QPS -> ~50 nodes minimum)
• Storage: Trip data and logs can reach petabytes annually; use distributed storage with tiering
• Bandwidth: 500K requests/sec x 1 KB/request ≈ 500 MB/s (~4 Gbps network capacity needed)
• Cache: Redis clusters handling hundreds of thousands ops/sec to reduce DB load
• Servers: Hundreds to thousands of app servers behind load balancers for concurrency

Interview Tip

When discussing Uber's architecture scalability, start by outlining the main components (users, drivers, ride matching, payments). Then identify the bottleneck (usually database). Next, explain how microservices and data partitioning help scale. Mention caching and asynchronous processing to handle load spikes. Finally, discuss global deployment and autoscaling for very large scale. Keep your explanation clear and structured.

Self Check

Question: Your database handles 1000 QPS. Traffic grows 10x. What do you do first?

Answer: Add read replicas to distribute read queries and reduce load on the primary database. Then consider sharding data to scale writes. Also, introduce caching to reduce database hits.

Key Result

Uber's architecture first hits database bottlenecks as users grow; scaling requires microservices, sharded databases, caching, and distributed processing to handle millions of concurrent requests efficiently.