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Learning to Optimize Join Queries With Deep Reinforcement Learning
[article]
2019
arXiv
pre-print
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Our extensive evaluation shows that DQ achieves plans with optimization costs and query execution times competitive with the native query optimizer in each system, but can execute significantly faster ...
We implement three versions of DQ to illustrate the ease of integration into existing DBMSes: (1) A version built on top of Apache Calcite, (2) a version integrated into PostgreSQL, and (3) a version integrated ...
Our empirical results with DQ span across multiple systems, multiple cost models, and workloads. ...
arXiv:1808.03196v2
fatcat:w35hsr6li5g23lbhnw6z3ueibe
BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation
[article]
2021
arXiv
pre-print
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We show empirically that even with an approximate tree structure, BayesCard can achieve comparable or better accuracy than the current SOTA methods. ...
Evaluation metric: We use the Q-error as our evaluation metrics, which is define as follow: Q-error = ( Estimated Cardinality True Cardinality ,
True Cardinality Estimated Cardinality
) This evaluation ...
arXiv:2012.14743v2
fatcat:b2wf3gs5x5antmrces4jatpafq
The Synchronized Filtering Dataflow
2014
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
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Graphics processing units (GPUs) can have hundreds of cores [84] . ...
Meanwhile, the use of GPU for streaming computing was also studied [13, 47, 103] . The use of GPU for pipelined SIMD processing has taken off since then [85] . ...
doi:10.7936/k7cf9n7j
fatcat:pthmuop7inamdohl4cn763p7du