From a Monolithic Big Data System to a Microservices Event-Driven Architecture
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Context: Data-intensive systems, a.k.a. big data systems (BDS), are software systems that handle a large volume of data in the presence of performance quality attributes, such as scalability and availability. Before the advent of big data management systems (e.g. Cassandra) and frameworks (e.g. Spark), organizations had to cope with large data volumes with custom-tailored solutions. In particular, a decade ago, Tecgraf/PUC-Rio developed a system to monitor truck fleet in real-time and proactively detect events from the positioning data received. Over the years, the system evolved into a complex and large obsolescent code base involving a costly maintenance process. Goal: We report our experience on replacing a legacy BDS with a microservice-based event-driven system. Method: We applied action research, investigating the reasons that motivate the adoption of a microservice-based event-driven architecture, intervening to define the new architecture, and documenting the challenges and lessons learned. Results: We perceived that the resulting architecture enabled easier maintenance and faultisolation. However, the myriad of technologies and the complex data flow were perceived as drawbacks. Based on the challenges faced, we highlight opportunities to improve the design of big data reactive systems. Conclusions: We believe that our experience provides helpful takeaways for practitioners modernizing systems with data-intensive requirements.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it