SkinnerDB

Trummer, Immanuel; Wang, Junxiong; Maram, Deepak; Moseley, S. H.; Jo, Sung-Kee; Antonakakis, Joseph

doi:10.1145/3299869.3300088

Cited by 52 publications

(1 citation statement)

References 32 publications

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“…There has been a host of work on query optimization in DBMS, including access path selection [46] , join optimization [47,48] and recently, machine leaning methods [49][50][51] . These focus on access path cost models for, e.g., main-memory concurrent systems [46] , heuristics for join [47] and group-by [48] re-ordering, learned indices [50,51] or optimizers [49,[52][53][54] . Our algorithms and techniques are complementary to the prior work, to incorporate bounded evaluation into DBMS query optimization.…”

Section: R(|x → Y N |)mentioning

confidence: 99%

Bounded Evaluation: Querying Big Data with Bounded Resources

CaoYang

Fan

Yuan

2020

Int. J. Autom. Comput.

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This work aims to reduce queries on big data to computations on small data, and hence make querying big data possible under bounded resources. A query is boundedly evaluable when posed on any big dataset , there exists a fraction of such that , and the cost of identifying is independent of the size of. It has been shown that with an auxiliary structure known as access schema, many queries in relational algebra (RA) are boundedly evaluable under the set semantics of RA. This paper extends the theory of bounded evaluation to RA aggr , i.e., RA extended with aggregation, under the bag semantics. (1) We extend access schema to bag access schema, to help us identify for RA aggr queries. (2) While it is undecidable to determine whether an RA aggr query is boundedly evaluable under a bag access schema, we identify special cases that are decidable and practical. (3) In addition, we develop an effective syntax for bounded RA aggr queries, i.e., a core subclass of boundedly evaluable RA aggr queries without sacrificing their expressive power. (4) Based on the effective syntax, we provide efficient algorithms to check the bounded evaluability of RA aggr queries and to generate query plans for bounded RA aggr queries. (5) As proof of concept, we extend PostgreSQL to support bounded evaluation. We experimentally verify that the extended system improves performance by orders of magnitude.

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Section: R(|x → Y N |)mentioning

confidence: 99%

Bounded Evaluation: Querying Big Data with Bounded Resources

CaoYang

Fan

Yuan

2020

Int. J. Autom. Comput.

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A Flexi Partner Selection Model for the Emergence of Cooperation in N-person Social Dilemmas

Gu¹,

An²

2023

Lecture Notes in Computer Science

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DB-BERT: making database tuning tools “read” the manual

Trummer

2023

The VLDB Journal

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DB-BERT is a database tuning tool that exploits information gained via natural language analysis of manuals and other relevant text documents. It uses text to identify database system parameters to tune as well as recommended parameter values. DB-BERT applies large, pre-trained language models (specifically, the BERT model) for text analysis. During an initial training phase, it fine-tunes model weights in order to translate natural language hints into recommended settings. At run time, DB-BERT learns to aggregate, adapt, and prioritize hints to achieve optimal performance for a specific database system and benchmark. Both phases are iterative and use reinforcement learning to guide the selection of tuning settings to evaluate (penalizing settings that the database system rejects while rewarding settings that improve performance). In our experiments, we leverage hundreds of text documents about database tuning as input for DB-BERT. We compare DB-BERT against various baselines, considering different benchmarks (TPC-C and TPC-H), metrics (throughput and run time), as well as database systems (PostgreSQL and MySQL). The experiments demonstrate clearly that DB-BERT benefits from combining general information about database tuning, mined from text documents, with scenario-specific insights, gained via trial runs. The full source code of DB-BERT is available online at https://itrummer.github.io/dbbert/.

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SkinnerDB

Cited by 52 publications

References 32 publications

Bounded Evaluation: Querying Big Data with Bounded Resources

Bounded Evaluation: Querying Big Data with Bounded Resources

A Flexi Partner Selection Model for the Emergence of Cooperation in N-person Social Dilemmas

DB-BERT: making database tuning tools “read” the manual

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