Source code of Feature Encoding and Model Training for cardinality and cost estimation based on PostgreSQL Execution Plans. The tree-structured model can generate representations for sub-plans which could be used by other query processing tasks like Join Reordering, Materialized View and even Index Selection.
export PYTHONPATH=code/
python -m unittest code.test.test_feature_extraction.TestFeatureExtraction
python -m unittest code.test.test_feature_encoding.TestFeatureEncoding
python -m unittest code.test.test_training.TestTrainingFor Nemerical workload: https://github.com/andreaskipf/learnedcardinalities
For Complete JOB: https://pan.baidu.com/s/14ZN1DqRcTOJJqsi8203suw password: tt2s
If you use the code in your work, please cite our paper.
@article{DBLP:journals/pvldb/SunL19,
author = {Ji Sun and
Guoliang Li},
title = {An End-to-End Learning-based Cost Estimator},
journal = {{PVLDB}},
volume = {13},
number = {3},
pages = {307--319},
year = {2019},
url = {http://www.vldb.org/pvldb/vol13/p307-sun.pdf},
doi = {10.14778/3368289.3368296},
timestamp = {Wed, 04 Dec 2019 19:13:52 +0100},
biburl = {https://dblp.org/rec/bib/journals/pvldb/SunL19},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
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