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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 thenon-materialized join result.Although this view update problem is generally ambiguous, we identifyaddition-to-multiplication preserving, the key property of variance semi-ring to supportrmsethe 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 3× (1.1×) faster for random forests (gradient boosting) compared to LightGBM, and over an order of 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).
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Database Gyms
In the past decade, academia and industry have embraced machine learning (ML) for database management system (DBMS) automation. These efforts have focused on designing ML models that predict DBMS behavior to support picking actions (e.g., building indexes) that improve the system's performance. Recent developments in ML have created automated methods for finding good models. Such advances shift the bottleneck from DBMS model design to obtaining the training data necessary for building these models. But generating good training data is challenging and requires encoding subject matter expertise into DBMS instrumentation. Existing methods for training data collection are bespoke to individual DBMS components and do not account for (1) how workload trends affect the system and (2) the subtle interactions between internal system components. Consequently, the models created from this data do not support holistic tuning across subsystems and require frequent retraining to boost their accuracy. This paper presents the architecture of a database gym, an integrated environment that provides a unified API of pluggable components for obtaining high-quality training data. The goal of a database gym is to simplify ML model training and evaluation to accelerate autonomous DBMS research. But unlike gyms in other domains that rely on custom simulators, a database gym uses the DBMS itself to create simulation environments for ML training. Thus, we discuss and prescribe methods for overcoming challenges in DBMS simulation, which include demanding requirements for performance, simulation fidelity, and DBMS-generated hints for guiding training processes.
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- PAR ID:
- 10395623
- Date Published:
- Journal Name:
- Conference on Innovative Data Systems Research
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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