Bibliographic record
Abstract
In our more and more connected world where people are used to managing their lives via digital services, it has become mandatory for a successful company to build applications that can scale with the popularity of the company's services. Scalability is not the only requirement but similarly important is that modern applications are highly available and fast because users are not willing to wait in our ever faster moving world. Due to this, we have seen a shift from the classic monolith towards micro service architectures which promise to be more easily scalable. The emergence of serverless functions further strengthened this trend more recently. By implementing a micro service architecture, application developers are all of a sudden exposed to the realm of distributed applications with its seemingly limitless scalability but also its pitfalls nobody tells you about upfront. So instead of solving business domain problems, developers find themselves fighting with race conditions, distributed failures, inconsistencies and in general a drastically increased complexity. In order to solve some of these problems, people introduce endless retries, timeouts, sagas and distributed transactions. These band aids can quickly result in a not so scalable system that is brittle and hard to maintain. The underlying problem is that developers are responsible for ensuring reliable communication and consistent state changes. Having a system that takes care of these aspects could drastically reduce the complexity of developing scalable distributed applications. By inverting the traditional control-flow from application-to-database to database-to-application, we can put the database in charge of ensuring reliable communication and consistent state changes and, thus, freeing the developer to think about it. In this keynote, I want to explore the idea of putting the database in charge of driving the application logic using the example of Stateful Functions, a library built on top of Apache Flink that follows this idea. I will explain how Stateful Functions achieves scalability and consistency but also what its limitations are. Based on these results, I would like to sketch the requirements for a runtime that can truly realise the full potential of Stateful Functions and discuss with you ideas how it could be implemented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".