DaskDB: Scalable Data Science with Unified Data Analytics and In Situ Query Processing
Bibliographic record
Abstract
Due to the rapidly rising data volume, there is a need to analyze this data efficiently and produce results quickly. However, data scientists today need to use different systems, since presently relational databases are primarily used for SQL querying and data science frameworks for complex data analysis. This may incur significant movement of data across multiple systems, which is expensive. Furthermore, with relational databases, the data must be completely loaded into the database before performing any analysis. We believe that data scientists would prefer to use a single system to perform both data analysis tasks and SQL querying, without requiring data movement between different systems. Ideally, this system would offer adequate performance, scalability, built-in data analysis functionalities, and usability. We present DaskDB, a scalable data science system with support for unified data analytics and in situ SQL query processing on heterogeneous data sources. DaskDB supports invoking Python APIs as User-Defined Functions (UDF). So, it can be easily integrated with most existing Python data science applications. Moreover, we introduce a distributed index join algorithm and a novel distributed learned index to improve join performance. Our experimental evaluation involve the TPC-H benchmark and a custom UDF benchmark, which we developed, for data analytics. And, we demonstrate that DaskDB significantly outperforms PySpark and Hive/Hivemall.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".