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Learning to Optimize Join Queries With Deep Reinforcement Learning [article]

Sanjay Krishnan, Zongheng Yang, Ken Goldberg, Joseph Hellerstein, Ion Stoica
2019 arXiv   pre-print
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]

Ziniu Wu, Amir Shaikhha, Rong Zhu, Kai Zeng, Yuxing Han, Jingren Zhou
2021 arXiv   pre-print
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

Peng Li
2014
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