Apache Druid: Effortlessly Shipping Updates While Keeping Metrics in Check

Are you managing a massive e-commerce store with millions of transactions happening every day? You want to track real-time sales, monitor product trends, and even identify suspicious activities—all on the fly. One second, a product goes viral on social media; the next, you need to replenish stocks or risk losing sales. Now, querying such a huge dataset with traditional systems feels like watching paint dry. That’s where Apache Druid steps in and makes a difference.

Apache Druid is an open-source, high-performance analytics database designed to handle large-scale event-driven data. Think of it as the perfect solution for fast, interactive queries across huge datasets—whether it’s user clicks, financial transactions, or IoT sensor data. It’s a go-to choice for companies that need real-time dashboards, like e-commerce platforms, fintech firms, or streaming services, to react to insights in the moment.

We were keen to explore the efficiency of their software delivery pipeline so using Middleware OSS we jumped into their behind-the-scenes work. Let’s see what we found.

Also read: Middleware: Open-Source DORA Metrics for a Smoother Engineering Flow

Driving Efficiency: Apache Druid's Commitment to Continuous Improvement

Apache Druid’s engineering pipeline metrics paint a picture of a team that knows how to keep things moving. With all their performance markers comfortably within the 2023 State of DevOps benchmarks, Druid’s CI/CD process reflects both discipline and a deep commitment to continuous improvement. But even the most efficient pipeline can hit occasional bumps, and there are subtle lessons to take from their metrics.

Merging PRs Like a Boss

September was a blockbuster month, with 154 merged PRs, far surpassing the average from previous months (77 in July and 72 in August).

This translates to the fact that the Druid team isn’t just making changes—they’re shipping them fast. This kind of frequent deployment frequency reflects a robust and agile CI/CD pipeline, allowing rapid delivery of features, bug fixes, and documentation with minimal disruption.

Notable examples like PR #16868 and #16865,highlights the team’s efficiency, with timely approvals and almost immediate merges. This pace shows not only technical prowess but also an alignment between developers and reviewers, ensuring that code moves smoothly from completion to deployment.

⏱ Cycle Time: A Mixed Bag

While deployment is frequent, cycle time offers an interesting narrative. Druid managed to reduce the average cycle time from 66 hours in July to 50.1 hours in September. Although these numbers are respectable, there’s still room to push for greater speed. Faster code reviews could further tighten the pipeline and reduce bottlenecks, making it easier for contributors to maintain their momentum.

The high September PR count might have contributed to slight delays, but delivering quality code takes time—and judging by the balance of features, fixes, documentation updates, and tests, the team’s priorities seem well-aligned.

⚡ First Response Time: Staying on Top of Feedback

If you’ve ever waited days for someone to review your PR, you’ll appreciate Druid’s impressive response times. The average first response landed at 31.87 hours in July and steadily improved to 26.6 hours by September. This kind of quick turnaround isn’t just about keeping the process moving—it also shows the team’s respect for contributors, encouraging collaboration and community involvement. With over 50 contributors active in recent months, keeping response times this low is no small feat.

🔄 Rework Time: Low and Steady Wins the Race

One area where Druid truly shines is rework time—the amount of time spent correcting or refining submissions after review. With rework times averaging between 1.04 and 2.43 hours, the team has clearly fostered solid coding practices. This suggests that contributors submit higher-quality code from the outset, reducing the need for extensive rewrites and revisions.

Also read: SymPy Dora Metrics: Strong Rework and Merge Times, Thriving Community

🛠 What Are They Building?

The diverse nature of their work is worth applauding. Recent contributions reflect a balanced focus, with 30% of PRs adding new features, 25% squashing bugs, 15% enhancing documentation, and 20% focused on tests and refactoring.

Notable additions, like custom DimensionSchema support by gargvishesh and HTTP client timeout configurations by a2l007 , indicate that Druid isn’t just maintaining—they’re actively improving functionality and performance.

🏆 DORA Score: 8/10 – Almost There

Druid’s strong metrics—particularly deployment frequency and merge efficiency—place it among the top-tier repositories. With a Dora score of 8 out of 10, they’re well on their way to excellence. Tightening up cycle times through review process improvements could nudge them toward that elusive 10/10.

Also read: The 2024 Dora Report Summary by Middleware

Recommendations to Keep Up Apache Druid’s Momentum

1. Leverage the Power of 618-Member Community: With over 50 contributors actively engaging each month, consider organizing community-driven code sprints or mentorship programs to maintain enthusiasm and onboard new contributors seamlessly. Recognizing individual efforts can further enhance engagement and sustain momentum.

2. Optimize the CI/CD Pipeline for Faster Cycle Time
   While deployment frequency is impressive, further refining review and testing processes could reduce cycle time from its current average (50.1 hours in September). Automation tools and early-stage code checks can minimize delays and keep your pipeline running smoothly.

3. Monitor and Analyze Rework Trends
   Keep rework time low by identifying patterns in repetitive corrections. Regular retrospective meetings can help address issues early, allowing contributors to submit high-quality PRs from the start, reducing time spent on rework and ensuring smoother merges.

Also read: Ant Design Dora Metrics: Speedy, Sleek, and Almost There—But Can They Crack the Cycle Time Code?

Apache Druid: Effortlessly Shipping Updates While Keeping Metrics in Check

Apache Druid’s engineering pipeline reflects a blend of discipline, collaboration, and smart processes. Their ability to maintain frequent deployments, engage a diverse community, and keep rework times low sets them apart. With a bit more focus on tightening cycle times, they’re poised to reach even greater efficiency. It’s clear that this team isn’t just chasing numbers—they’re building a sustainable ecosystem where every contribution moves them closer to long-term success. If they stay on this path, Druid will continue to raise the bar for open-source excellence.

If you are also facing such engineering dilemmas, then write to us at productivity@middlewarehq.com and we would be happy to help you provide actionable insights into your workflow or you can also try tracking your Dora metrics yourself using Middleware Open Source that too for free!

Did you know?

Apache Druid was originally developed by Metamarkets, a real-time analytics company, in 2012. It became an Apache Incubator project in 2018 and was promoted to a top-level project in 2020.

Further Resources

Apache Druid Documentation

Middleware and Dora Metrics

Continuous Integration Guides

Dora Metrics Methodology