| _version_ | 1866901248493486080 |
|---|---|
| author | Tejendra Patel |
| author_facet | Tejendra Patel |
| contents | <p>For production Java microservices, there is an observability gap in tracking infrastructure cost down to the method level of a source code change. Existing Java profilers either have low overhead sampling with short retention for unqueryable flame graph formats or rich events with high overhead that must be manually enabled during production. Neither approach supports automated regression detection workflows that fast-moving engineering teams need. A runtime instrumentation architecture supports (1) configuration-driven bytecode instrumentation, (2) adaptive per-method sampling that calibrates how much telemetry is collected from each application method based on how many times that method is called per second, (3) fail-open streaming telemetry that separates telemetry transport from application execution, and (4) SQL-driven automated regression detection over a columnar data warehouse with extended retention. It provides cost and latency telemetry at the method call level with low overhead, a structured queryable system, and long-term storage. It can automatically detect performance regressions, attribute costs per method, and provide systematic performance governance, without requiring source-level modification or manual profiling of the code under assessment.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19069322 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Method-Level Performance Instrumentation for Production Java Microservices Tejendra Patel <p>For production Java microservices, there is an observability gap in tracking infrastructure cost down to the method level of a source code change. Existing Java profilers either have low overhead sampling with short retention for unqueryable flame graph formats or rich events with high overhead that must be manually enabled during production. Neither approach supports automated regression detection workflows that fast-moving engineering teams need. A runtime instrumentation architecture supports (1) configuration-driven bytecode instrumentation, (2) adaptive per-method sampling that calibrates how much telemetry is collected from each application method based on how many times that method is called per second, (3) fail-open streaming telemetry that separates telemetry transport from application execution, and (4) SQL-driven automated regression detection over a columnar data warehouse with extended retention. It provides cost and latency telemetry at the method call level with low overhead, a structured queryable system, and long-term storage. It can automatically detect performance regressions, attribute costs per method, and provide systematic performance governance, without requiring source-level modification or manual profiling of the code under assessment.</p> |
| title | Method-Level Performance Instrumentation for Production Java Microservices |
| url | https://doi.org/10.5281/zenodo.19069322 |