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Billy Odell Tucker-Robinson Founder & Host — Banking With Billy Network • Intelligence Network • Data Science • AI Research • World News
Published: 2026-10-01T10:46:38.524Z • Permanent link

Uber's massive monorepos handling over 65,000 monthly changes is a logistical nightmare, but the company has found a solution in SubmitQueue, a system that maintains green mainlines across its infrastructure. Dhruva Juloori, Uber's director of software engineering, has shed light on the inner workings of SubmitQueue, a proprietary tool that uses binary speculation trees, conflict analysis, and machine learning models to predict build success. Juloori has revealed how SubmitQueue has become the backbone of Uber's engineering efforts, enabling the company to maintain a high degree of reliability and efficiency in its software development pipeline.

SubmitQueue's origins date back to 2018, when Uber's engineering team began experimenting with different approaches to managing its complex software infrastructure. The company's engineers recognized the need for a more robust and efficient system, one that could handle the sheer volume of changes being made to its codebase. After months of trial and error, the team developed SubmitQueue, a system that would go on to revolutionize the way Uber approached software development. Today, SubmitQueue is an integral part of Uber's engineering process, with thousands of developers relying on the system to manage their work.

Juloori has revealed that SubmitQueue's success is due in part to its ability to predict build success with a high degree of accuracy. By analyzing data from previous builds and incorporating machine learning models, SubmitQueue is able to identify potential issues before they become major problems. This enables Uber's engineers to take proactive steps to mitigate risks, resulting in faster and more reliable software deployments. Juloori has also highlighted the importance of conflict analysis in SubmitQueue, a process that involves identifying and resolving conflicts between different parts of the codebase. By resolving these conflicts early, Uber's engineers can avoid costly and time-consuming rewrites down the line.

The success of SubmitQueue has far-reaching implications for the Data Sources domain, with companies like Uber and others taking notice of the system's capabilities. Research communities and policymakers are also taking a closer look at SubmitQueue, recognizing the potential benefits of its approach to software development. For example, a recent study published by the National Institute of Standards and Technology (NIST) highlighted the importance of using machine learning models to predict software reliability, a key aspect of SubmitQueue's design. As companies like Uber continue to innovate and push the boundaries of what is possible, it's likely that we'll see more and more adoption of systems like SubmitQueue.

The impact of SubmitQueue can also be seen in the broader market, where companies are increasingly recognizing the value of investing in their software infrastructure. According to a recent report by Gartner, companies that prioritize software development are more likely to experience significant returns on investment, with some even seeing a return of 10 times their initial investment. As the demand for software continues to grow, it's likely that we'll see more and more companies investing in systems like SubmitQueue, driving innovation and growth in the Data Sources domain.

The success of SubmitQueue is not an isolated event, but rather part of a larger trend in the software development community. In recent years, there has been a growing recognition of the importance of investing in software infrastructure, with companies like Netflix and Google taking a proactive approach to managing their codebases. This trend is likely to continue, with more and more companies recognizing the value of investing in systems like SubmitQueue.