Auto-Instrumenting Go Applications: A Study of Compile-Time and Runtime Instrumentation Using Opentelemetry
Keywords:
adaptability, instrumentation, observability, performance, reliability, telemetry, tracingAbstract
The study aims to provide a systematic comparison of automatic instrumentation methods for Go applications within a unified OpenTelemetry observability architecture. The research problem is that the Go language, being statically compiled and performance-oriented, lacks built-in mechanisms for dynamic telemetry injection, which makes it difficult to achieve complete observability without modifying the source code. To address this issue, the study applies methods of comparative architectural analysis, engineering modeling, and synthesis of experimental data presented in scientific research. The work compares instrumentation performed at compile time and during program execution. It is shown that the first approach ensures high semantic accuracy and metric predictability, while the second provides flexibility and continuous monitoring without requiring recompilation of the application. The study concludes that creating a hybrid observability model that combines the advantages of both approaches offers an effective balance between data precision and operational reliability. The significance of this research lies in forming conceptual foundations for the development of self-regulating observability systems, which can be applied in the design of telemetry infrastructures, optimization of DevOps processes, and enhancement of the resilience of industrial software systems.
References
[1]. Balis, B., Czerepak, K., Kuzma, A., Meizner, J., & Wronski, L. (2024, August 27). Towards observability of scientific applications (arXiv:2408.15439). arXiv. https://doi.org/10.48550/arXiv.2408.15439
[2]. Hansen, M., & Hasselbring, W. (2024, November 19). Instrumentation of software systems with OpenTelemetry for software visualization (arXiv:2411.12380). arXiv. https://doi.org/10.48550/arXiv.2411.12380
[3]. Jia, J., Qin, R., Craun, M., Lukiyanov, E., Bansal, A., Le, M. V., Franke, H., Jamjoom, H., Xu, T., & Williams, D. (2025, April 28). Safe and usable kernel extensions with Rex (arXiv:2502.18832). arXiv. https://doi.org/10.48550/arXiv.2502.18832
[4]. Nõu, A., Talluri, S., Iosup, A., & Bonetta, D. (2025). Investigating performance overhead of distributed tracing in microservices and serverless systems. In Companion of the 16th ACM/SPEC International Conference on Performance Engineering (pp. 162–166). Association for Computing Machinery. https://doi.org/10.1145/3680256.3721316
[5]. Risdianto, A. C., Usman, M., & Rathore, M. A. (2024). Transforming network management: Intent-based flexible control empowered by efficient flow-centric visibility. Future Internet, 16(7), 223. https://doi.org/10.3390/fi16070223
[6]. Sabbioni, A., Corradi, A., Monti, S., & De Rolt, C. R. (2025). From digital services to sustainable ones: Novel Industry 5.0 environments enhanced by observability. Information, 16(9), 821. https://doi.org/10.3390/info16090821
[7]. Shen, J., Zhang, H., Xiang, Y., Shi, X., Li, X., Shen, Y., Zhang, Z., Wu, Y., Yin, X., Wang, J., Xu, M., Li, Y., Yin, J., Song, J., Li, Z., & Nie, R. (2023). Network-centric distributed tracing with DeepFlow: Troubleshooting your microservices in zero code. In Proceedings of the ACM SIGCOMM 2023 Conference (pp. 420–437). Association for Computing Machinery. https://doi.org/10.1145/3603269.3604823
[8]. Shin, J., Kim, J., & Nam, J. (2025). Aquila: Efficient in-kernel system call telemetry for cloud-native environments. Sensors, 25(21), 6511. https://doi.org/10.3390/s25216511
[9]. Yang, S., Reichelt, D. G., & Hasselbring, W. (2024, November 26). Evaluating the overhead of the performance profiler Cloudprofiler with MooBench (arXiv:2411.17413). arXiv. https://doi.org/10.48550/arXiv.2411.17413
[10]. Yang, W., Chen, P., Liu, K., & Zhang, H. (2023, November 15). Nahida: In-band distributed tracing with eBPF (arXiv:2311.09032). arXiv. https://doi.org/10.48550/arXiv.2311.09032
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