A Brief Overview of Uber's Development

How did Uber manage to scale to millions of users? Here are some key lessons drawn from their journey.

Initially established in 2009 by contractors from Mexico City, Uber's first iteration utilized the LAMP stack, with code written in Spanish. Founders Travis and Garrett aimed to address the challenge of securing a taxi in San Francisco. They purchased a vehicle, rented a garage, and hired a driver, envisioning a simple "button" that would allow them to summon the driver. Although this concept may seem trivial, it gained traction as their friends expressed interest in a similar service.

Fast forward three years, and Curtis Chambers recounts the early struggles. With a team of fewer than ten people managing three mobile applications, a blog, a website, and a dispatch engine, the complexities became overwhelming. They discovered that the LAMP stack, while effective for static websites, fell short when it came to managing a real-time ridesharing system. Their single PHP server was overloaded, handling the website, ride dispatching, fare calculations, and promotional features, leading to concurrency issues with the MySQL database—sometimes dispatching multiple cars to a single rider or assigning a single driver to multiple cars.

By 2010, a significant rewrite began, culminating in January 2011. Uber became a pioneer in adopting Node.js, thanks to its asynchronous request handling. They connected a Node.js server to a MongoDB database for their dispatch service, which later transitioned to Redis in February 2012. Business logic related to ride fares, authentication, and promotions was migrated to a Python-based API service. Despite facing growing pains and heightened expectations for reliability, the team persevered through challenges.

In May 2012, they recognized that if the "Dispatch Server" could remain operational, the platform could withstand loads and failures affecting the "API" service/database. They introduced a middle layer known as the Object Node Fallback to temporarily store live transactions or location data, even during database overloads.

By 2013, Uber began breaking down their monolithic structure into several Python services, opting to utilize PostgreSQL for its geospatial features. However, they ultimately reverted to MySQL in March 2014 due to the limitations of their Object-Relational Mapping (ORM) tool.

As they approached the limits of their single database, they developed a sharding layer named Schemaless, deploying it by the end of 2014. As engineers grappled with cumulative system failures, A/B testing and chaos engineering became increasingly vital.

Uber transitioned from DynamoDB to a customized Docstore built on MySQL.

Lessons Learned from Uber's Journey

1. Start Simple to Address Immediate Issues: One of the most surprising revelations was that Uber initially did not aim for scalability. The original goal was simply to solve a minor inconvenience. While LAMP is a dependable choice, it may seem outdated given the vast array of modern frameworks and cloud services. Focus your efforts on essential features rather than over-engineering.
2. Be Open to Change: As user demands evolve, a rewrite may become necessary. In 2010, Uber's team recognized that their existing solution hindered growth. When an initial concept proves effective, leverage those insights to refine or completely overhaul the system.
3. Adapt to User Expectations: As your software gains traction, user expectations will naturally rise. What was once considered impressive may soon become a requirement. While early development often involves experimentation, later stages prioritize reliability and user accessibility.
4. Embrace Better Solutions: The optimal solution can shift over time. It's crucial to recognize that decisions made with the available resources and knowledge may not always align with future needs. Be prepared for changes that may incur additional costs, ensuring that the ROI justifies the transition.
5. Anticipate Database Challenges: While databases might not present immediate issues, they can become critical as an application scales. With rising traffic, data management often becomes the primary challenge.
6. Conduct Real-World Testing: As software complexity increases, testing under real or semi-real conditions becomes essential. A/B testing, popularized by companies like Netflix, can provide valuable insights into system performance.