Software at Scale

Aravind was a Staff Software Engineer at Uber, and currently works at OpenAI.

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Edited Transcript

Can you tell us about the scale of data Uber was dealing with when you joined in 2018, and how it evolved?

When I joined Uber in mid-2018, we were handling a few petabytes of data. The company was going through a significant scaling journey, both in terms of launching in new cities and the corresponding increase in data volume. By the time I left, our data had grown to over an exabyte. To put it in perspective, the amount of data grew by a factor of about 20 in just a three to four-year period.

Currently, Uber ingests roughly a petabyte of data daily. This includes some replication, but it's still an enormous amount. About 60-70% of this is raw data, coming directly from online systems or message buses. The rest is derived data sets and model data sets built on top of the raw data.

That's an incredible amount of data. What kinds of insights and decisions does this enable for Uber?

This scale of data enables a wide range of complex analytics and data-driven decisions. For instance, we can analyze how many concurrent trips we're handling throughout the year globally. This is crucial for determining how many workers and CPUs we need running at any given time to serve trips worldwide.

We can also identify trends like the fastest growing cities or seasonal patterns in traffic. The vast amount of historical data allows us to make more accurate predictions and spot long-term trends that might not be visible in shorter time frames.

Another key use is identifying anomalous user patterns. For example, we can detect potentially fraudulent activities like a single user account logging in from multiple locations across the globe. We can also analyze user behavior patterns, such as which cities have higher rates of trip cancellations compared to completed trips.

These insights don't just inform day-to-day operations; they can lead to key product decisions. For instance, by plotting heat maps of trip coordinates over a year, we could see overlapping patterns that eventually led to the concept of Uber Pool.

How does Uber manage real-time versus batch data processing, and what are the trade-offs?

We use both offline (batch) and online (real-time) data processing systems, each optimized for different use cases. For real-time analytics, we use tools like Apache Pinot. These systems are optimized for low latency and quick response times, which is crucial for certain applications.

For example, our restaurant manager system uses Pinot to provide near-real-time insights. Data flows from the serving stack to Kafka, then to Pinot, where it can be queried quickly. This allows for rapid decision-making based on very recent data.

On the other hand, our offline flow uses the Hadoop stack for batch processing. This is where we store and process the bulk of our historical data. It's optimized for throughput – processing large amounts of data over time.

The trade-off is that real-time systems are generally 10 to 100 times more expensive than batch systems. They require careful tuning of indexes and partitioning to work efficiently. However, they enable us to answer queries in milliseconds or seconds, whereas batch jobs might take minutes or hours.

The choice between batch and real-time depends on the specific use case. We always ask ourselves: Does this really need to be real-time, or can it be done in batch? The answer to this question goes a long way in deciding which approach to use and in building maintainable systems.

What challenges come with maintaining such large-scale data systems, especially as they mature?

As data systems mature, we face a range of challenges beyond just handling the growing volume of data. One major challenge is the need for additional tools and systems to manage the complexity.

For instance, we needed to build tools for data discovery. When you have thousands of tables and hundreds of users, you need a way for people to find the right data for their needs. We built a tool called Data Book at Uber to solve this problem.

Governance and compliance are also huge challenges. When you're dealing with sensitive customer data, you need robust systems to enforce data retention policies and handle data deletion requests. This is particularly challenging in a distributed system where data might be replicated across multiple tables and derived data sets.

We built an in-house lineage system to track which workloads derive from what data. This is crucial for tasks like deleting specific data across the entire system. It's not just about deleting from one table – you need to track down and update all derived data sets as well.

Data deletion itself is a complex process. Because most files in the batch world are kept immutable for efficiency, deleting data often means rewriting entire files. We have to batch these operations and perform them carefully to maintain system performance.

Cost optimization is an ongoing challenge. We're constantly looking for ways to make our systems more efficient, whether that's by optimizing our storage formats, improving our query performance, or finding better ways to manage our compute resources.

How do you see the future of data infrastructure evolving, especially with recent AI advancements?

The rise of AI and particularly generative AI is opening up new dimensions in data infrastructure. One area we're seeing a lot of activity in is vector databases and semantic search capabilities. Traditional keyword-based search is being supplemented or replaced by embedding-based semantic search, which requires new types of databases and indexing strategies.

We're also seeing increased demand for real-time processing. As AI models become more integrated into production systems, there's a need to handle more GPUs in the serving flow, which presents its own set of challenges.

Another interesting trend is the convergence of traditional data analytics with AI workloads. We're starting to see use cases where people want to perform complex queries that involve both structured data analytics and AI model inference.

Overall, I think we're moving towards more integrated, real-time, and AI-aware data infrastructure. The challenge will be balancing the need for advanced capabilities with concerns around cost, efficiency, and maintainability.