FunDa: scalable serverless data analytics and in situ query processing
Bibliographic record
Abstract
The pay-what-you-use model of serverless Cloud computing (or serverless, for short) offers significant benefits to the users. This computing paradigm is ideal for short running ephemeral tasks, however, it is not suitable for stateful long running tasks, such as complex data analytics and query processing. We propose F u n Da, an on-premises serverless data analytics framework, which extends our previously proposed system for unified data analytics and in situ SQL query processing called DaskDB. Unlike existing serverless solutions, which struggle with stateful and long running data analytics tasks, F u n Da overcomes their limitations. Our ongoing research focuses on developing a robust architecture for F u n Da, enabling true serverless in on-premises environments, while being able to operate on a public Cloud, such as AWS Cloud. We have evaluated our system on several benchmarks with different scale factors. Our experimental results in both on-premises and AWS Cloud settings demonstrate F u n Da’s ability to support automatic scaling, low-latency execution of data analytics workloads, and more flexibility to serverless users.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".