Tag: VLDB

Four papers from the Wu Lab were presented at the 49th International Conference on Very Large Data Bases (VLDB 2023). VLDB features research talks, tutorials, demonstrations, and workshops on issues in data management, database, and information systems research.

JoinBoost: Grow Trees Over Normalized Data Using Only SQL
Zezhou Huang, Rathijit Sen, Jiaxiang Liu, Eugene Wu

Although dominant for tabular data, ML libraries that train tree models over normalized databases (e.g., LightGBM, XGBoost) require the data to be denormalized as a single table, materialized, and exported. This process is not scalable, slow, and poses security risks. In-DB ML aims to train models within DBMSes to avoid data movement and provide data governance. Rather than modify a DBMS to support In-DB ML, is it possible to offer competitive tree training performance to specialized ML libraries…with only SQL?

We present JoinBoost, a Python library that rewrites tree training algorithms over normalized databases into pure SQL. It is portable to any DBMS, offers performance competitive with specialized ML libraries, and scales with the underlying DBMS capabilities. JoinBoost extends prior work from both algorithmic and systems perspectives. Algorithmically, we support factorized gradient boosting, by updating theYvariable to the residual in the non-materialized join result. Although this view update problem is generally ambiguous, we identify addition-to-multiplication preserving, the key property of variance semi-ring to support rmse, the most widely used criterion. System-wise, we identify residual updates as a performance bottleneck. Such overhead can be natively minimized on columnar DBMSes by creating a new column of residual values and adding it as a projection. We validate this with two implementations on DuckDB, with no or minimal modifications to its internals for portability. Our experiment shows that JoinBoost is 3x (1.1x) faster for random forests (gradient boosting) compared to LightGBM, and over an order magnitude faster than state-of-the-art In-DB ML systems. Further, JoinBoost scales well beyond LightGBM in terms of the # features, DB size (TPC-DS SF=1000), and join graph complexity (galaxy schemas).

Saibot: A Differentially Private Data Search Platform
Zezhou Huang, Jiaxiang Liu, Daniel Alabi, Raul Castro Fernandez, Eugene Wu

Recent data search platforms use ML task-based utility measures rather than metadata-based keywords, to search large dataset corpora. Requesters submit a training dataset and these platforms search for augmentations (join or union compatible datasets) that, when used to augment the requester’s dataset, most improve model (e.g., linear regression) performance. Although effective, providers that manage personally identifiable data demand differential privacy (DP) guarantees before granting these platforms data access. Unfortunately, making data search differentially private is nontrivial, as a single search can involve training and evaluating datasets hundreds or thousands of times, quickly depleting privacy budgets.

We present Saibot, a differentially private data search platform that employs Factorized Privacy Mechanism (FPM), a novel DP mechanism, to calculate sufficient semi-ring statistics for ML over different combinations of datasets. These statistics are privatized once, and can be freely reused for the search. This allows Saibot to scale to arbitrary numbers of datasets and requests, while minimizing the amount that DP noise affects search results. We optimize the sensitivity of FPM for common augmentation operations, and analyze its properties with respect to linear regression. Specifically, we develop an unbiased estimator for many-to-many joins, prove its bounds, and develop an optimization to redistribute DP noise to minimize the impact on the model. Our evaluation on a real-world dataset corpus of 329 datasets demonstrates that Saibot can return augmentations that achieve model accuracy within 50 to 90% of non-private search, while the leading alternative DP mechanisms (TPM, APM, shuffling) are several orders of magnitude worse.

Pollock: A Data Loading Benchmark
Gerardo Vitagliano, Mazhar Hameed, Lan Jiang, Lucas Reisener, Eugene Wu, Felix Naumann

Any system at play in a data-driven project has a fundamental requirement: the ability to load data. The de-facto standard format to distribute and consume raw data is csv. Yet, the plain text and flexible nature of this format make such files often difficult to parse and correctly load their content, requiring cumbersome data preparation steps.

We propose a benchmark to assess the robustness of systems in loading data from non-standard csv formats and with structural inconsistencies. First, we formalize a model to describe the issues that affect real-world files and use it to derive a systematic “pollution” process to generate dialects for any given grammar. Our benchmark leverages the pollution framework for the csv format. To guide pollution, we have surveyed thousands of real-world, publicly available csv files, recording the problems we encountered. We demonstrate the applicability of our benchmark by testing and scoring 16 different systems: popular csv parsing frameworks, relational database tools, spreadsheet systems, and a data visualization tool.

ConnectorX: Accelerating Data Loading From Databases to Dataframes
Xiaoying Wang, Weiyuan Wu, Jinze Wu, Yizhou Chen, Nick Zrymiak, Changbo Qu, Lampros Flokas, George Chow, Jiannan Wang, Tianzheng Wang, Eugene Wu, Qingqing Zhou

Data is often stored in a database management system (DBMS) but dataframe libraries are widely used among data scientists. An important but challenging problem is how to bridge the gap between databases and dataframes. To solve this problem, we present ConnectorX, a client library that enables fast and memory-efficient data loading from various databases to different dataframes.

We first investigate why the loading process is slow and consumes large memory. We surprisingly find that the main overhead comes from the client-side rather than query execution or data transfer. We integrate several existing and new techniques to reduce the overhead and carefully design the system architecture and interface to make ConnectorX easy to extend to various databases and dataframes. Moreover, we propose server-side result partitioning that can be adopted by DBMSs in order to better support exporting data to data science tools. We conduct extensive experiments to evaluate ConnectorX and compare it with popular libraries. The results show that ConnectorX significantly outperforms existing solutions. ConnectorX is open sourced at: https://github.com/sfu-db/connector-x.