Telemetry of Legacy Web Applications: An Industrial Case Study
Bibliographic record
Abstract
Berger-Levrault, like many companies, has legacy web applications that still bring great values, and cannot be easily replaced. To maintain these applications, it needs data about user navigation, backend actions and client-server data exchange. Berger-Levrault has relied on a traditional logging approach that partially collects these data, requires modifying the application code and heavily impacts its performance. To address the limitations of this logging approach, we propose to replace it by a modern software telemetry approach. Existing telemetry approaches do not meet our needs, they should be extended based on our objectives, technological constraints and industrial regulations. In this paper, we report our experience in instrumenting real, large-scale, industrial legacy web applications based on a telemetry approach. Our goal is to automatically instrument legacy web applications to collect data fulfilling our industrial needs. We extend the automatic instrumentation capabilities of OpenTelemetry agents to instrument our applications without modifying their code. We define a telemetry architecture to integrate telemetry components with legacy web applications. Also, we empirically evaluate the performance overhead produced by our agents. The results show that there is no significant overhead when using OpenTelemetry agents. However, this overhead is sensitive to the size of data being serialized when instrumenting client-server data exchange. Moreover, we discuss lessons learned about the technical challenges we faced during the industrialization of our approach. Also, we empirically evaluate the performance overhead produced by our agents. The results show that there is no significant overhead when using Open Telemetry agents. However, this overhead is sensitive to the size of data being serialized when instrumenting client-server data exchange. Moreover, we discuss lessons learned about some technical challenges we faced during the industrialization of our telemetry approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".