Presto™ is an open source SQL query engine used on a large scale at Uber. Uber has around 20+ Presto clusters comprising over 12,000 hosts. We have about 7,000 weekly users and run about half a million queries per day. Presto has various use cases at Uber like ad hoc interactive analytics, ETL and batch workloads, dashboarding, data quality checks, report generation, experimentation, and data-driven services. Due to the scale of the system, there are various opportunities to make it more efficient. However, these opportunities need intelligence regarding the queries being processed by the system.

Learn how Uber made a breakthrough in tracking design metrics across Figma, Android, and iOS with Design System Observability.

SQL is a vital tool used daily by engineers, operations managers, and data scientists at Uber to access and manipulate terabytes of data. Crafting these queries not only requires a solid understanding of SQL syntax, but also deep knowledge of how our internal data models represent business concepts. QueryGPT aims to bridge this gap, enabling users to generate SQL queries through natural language prompts, thereby significantly enhancing productivity.

QueryGPT uses large language models (LLM), vector databases, and similarity search to generate complex queries from English questions that are provided by the user as input.

This article chronicles our development journey over the past year and where we are today with this vision.

In the competitive world of corporate travel, efficiency and precision are paramount. At Uber, we’ve recognized a specific need within this sector: senior executives and their assistants require a streamlined and reliable booking experience. To address this, we developed the Delegate Booking tool, a significant enhancement to our platform designed to empower executive assistants (EAs) to manage travel with ease and precision.

Learn how Uber is streamlining the Cloud migration of its massive Data Lake by incorporating key Data Mesh principles.

In the dynamic ecosystem of Uber, search functionality serves as the backbone for numerous critical operations, ranging from matching riders to drivers, to geo search functionalities within Uber ride apps, to facilitating seamless exploration of restaurants and dishes in Uber Eats. The reliance on search is paramount, given the diverse and extensive nature of Uber’s service offerings.

The Search Platform team at Uber has built an in-house search engine on top of Apache Lucene, with the primary objective of establishing a unified search infrastructure across all business verticals. Since its inception in 2019, the service has operated on Lucene version 7.5.0, which is two major versions and nearly four years behind the latest iteration.

Learn how Uber uses Apache Pinot for serving over 100 low-latency offline analytics use cases.

In this blog, we share how we reimagined CD at Uber to improve deployment automation and UX of managing microservices, while tackling the peculiar challenges of working with large monorepos.

Learn how we achieved diff-time E2E testing for thousands of microservices at Uber.

Discover how Uber's in-house ETL framework helps standardize modular ETL development, improving developer productivity and ensuring reliable data pipelines.

Learn all about our journey of successfully upgrading our MySQL fleet at Uber from v5.7 to v8.0, enhancing performance and reliability.

Learn about how the Storage team at Uber significantly reduced costs and improved speed for backups of its Petabyte-scale, MyRocks-based distributed databases by devising a Differential Backups…

Uber's data lake is migrating to the cloud! Learn how they're tackling security challenges and scaling the system to handle massive amounts of data while ensuring a seamless transition for users.

What follows is the story of how we started designing for surfaces outside the app, the engineering problems we had to solve along the way, and ultimately how we measurably improved the experience of riders and drivers.

The Odin platform aims to provide a unified operational experience by encompassing all aspects of managing stateful workloads. These aspects include host lifecycle, workload scheduling, cluster management, monitoring, state propagation, operational user interfaces, alerting, auto-scaling, and automation. Uber deploys stateful systems at global, regional, and zonal levels, and Odin is designed to manage these systems consistently and in a technology-agnostic manner. Moreover, Odin supports co-location to increase hardware cost efficiency. All stateful workloads must be fully containerized, a relatively novel and controversial concept when the platform was created.

This blog post is the first of a series on Uber’s stateful platform. The series aims to be accessible and engaging for readers with no prior knowledge of building container platforms and those with extensive expertise. This post provides an overview of Odin’s origins, the fundamental principles, and the challenges encountered early on. The next post will explore how we have safely scaled operational throughput, significantly improving our handling of large-scale, fleet-wide operations and up-leveling runbooks through workflows. Stay tuned for more posts in the series.

Large Language Models (LLMs) have emerged as pivotal instruments in the tech industry, unlocking new avenues for innovation and progress across various sectors. At Uber, the impact of LLMs is…