Introducing Bitrise Build Cache Insights: See What Your Cache Is Really Doing!
We are excited to introduce Bitrise Build Cache Insights—a powerful new feature designed to provide a deep, data-driven understanding of how you utilize the Bitrise Build Cache. Whether you use Gradle or Bazel, Build Cache Insights offers key metrics that can help you refine your development pipeline and accelerate build times. This feature equips engineering teams with the tools needed to evaluate cache efficiency and make strategic adjustments to maximize productivity. Let’s explore the core metrics available in this initial release and understand their practical significance.
Key Metrics
1. Build Cache Invocation Count
The Build Cache Invocation Count is a fundamental metric that helps you understand how frequently the Bitrise Build Cache is used in your builds. Whether you’re using Gradle or Bazel, this count shows how well caching has been integrated into your build process. A high invocation count indicates strong cache adoption, while a low count may suggest missed opportunities to leverage caching for build efficiency. This insight helps identify areas where improvements could lead to faster builds.
2. Data Transfers to/from the Cache (p50 & p90)
The metrics for uploads and downloads to and from the build cache, measured at the p50 (median) and p90 percentiles, reveal the data dynamics for each command. Understanding these data patterns is essential for identifying inefficiencies and optimizing cache usage. High data transfer volumes can point to excessive uploads or downloads, which might unnecessarily slow down your builds. Additionally, if uploads are significantly higher than downloads, it might indicate inefficient caching—such as data being repeatedly generated and stored but rarely reused. Addressing these issues can lead to significant improvements in overall build performance.
3. Cache Hit Rate per Command (p50 & p10)
The Cache Hit Rate per Command metric evaluates how often the cache is successfully used to avoid redundant computations. The p50 (median) and p10 values provide insights into variability across different builds. The p10 value is particularly important because it helps identify the worst-performing scenarios, allowing teams to focus on improving the lower end of cache performance. By addressing these less efficient cases, overall build reliability and speed can be significantly enhanced. A high cache hit rate means that previous computations are being effectively reused, leading to faster builds. On the other hand, a low hit rate suggests suboptimal cache configuration or incorrectly defined cache keys, providing a clear target for optimization. By systematically improving cache hit rates, teams can significantly reduce build times and resource consumption.
Additional Features of Build Cache Insights
Alerts
Build Cache Insights also includes an Alerts feature that allows you to set thresholds for key metrics. If a metric goes above or below the specified threshold, you can receive an alert via email or Slack. This ensures that you are always aware of important changes in cache performance and can respond proactively to potential issues.
Dashboards
With the Dashboards feature, you can add any chart from Build Cache Insights to personalized dashboards. This gives you the flexibility to create custom views that highlight the metrics most relevant to you and your team, helping you keep track of what matters most at a glance.
Get Started Today
This first release of Build Cache Insights is just the beginning of our journey to provide full visibility and better tools for optimizing your build processes. We are committed to expanding the range of metrics and improving Build Cache Insights to ensure you have everything you need to fine-tune your build strategies. If you’re ready to boost your caching performance and streamline your workflows, check out Build Cache Insights on Bitrise today!