{"id":28272,"date":"2026-06-09T22:16:32","date_gmt":"2026-06-09T13:16:32","guid":{"rendered":"https:\/\/aireviewirush.com\/?p=28272"},"modified":"2026-06-09T22:16:32","modified_gmt":"2026-06-09T13:16:32","slug":"datadog-delivers-tens-of-millions-of-in-depth-efficiency-insights-with-profilingmanager","status":"publish","type":"post","link":"https:\/\/aireviewirush.com\/?p=28272","title":{"rendered":"Datadog delivers tens of millions of in-depth efficiency insights with ProfilingManager"},"content":{"rendered":"


\n<\/p>\n

\n
\n\"\"<\/p>\n

Posted by Alice Yuan, Developer Relations Engineer at Google, Arti Arutiunov, Product Supervisor at Datadog and Nikita Ogorodnikov, Employees Software program Engineer at Datadog<\/p>\n

\"\"<\/a><\/div>\n

\n Efficiency regressions are notoriously onerous to breed, making regressions a large bottleneck for cell builders. Though indicators like ANR charges point out what points happen in manufacturing, pinpointing the precise line of code that resulted within the efficiency problem has traditionally necessitated exhaustive handbook copy or speculative trial-and-error experimentation.\n<\/p>\n

Datadog collaborated with Google to mitigate this frustration by integrating the ProfilingManager API (out there on Android 15+ units) into its Actual Consumer Monitoring (RUM) and Steady Profiling platforms. This integration transforms the debugging workflow, permitting builders to maneuver past surface-level signs to having the ability to detect the why<\/em> behind a efficiency bottleneck.\n<\/p>\n

By leveraging this system-level API, Datadog now processes tens of millions of manufacturing profiles weekly throughout the globe in line with Datadog inside knowledge of June 2026. It gives engineering groups with a brand new stage of visibility into real-world efficiency, all whereas sustaining a low runtime overhead for production-scale efficiency monitoring.<\/p>\n

\n
\n

Table of Contents<\/p>\n