Query cost estimation in DBMS with deep learning
Cost and cardinality estimation is considered the Achilles Heel of modern query optimizers. Poor cardinality estimates lead to bad cost estimates resulting in sub-optimal query execution plans being selected which drops the performance of query optimizers. With the recent rise of ML for DB, the d...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | |
| التنسيق: | Final Year Project |
| اللغة: | English |
| منشور في: |
Nanyang Technological University
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/10356/166095 |
| الوسوم: |
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| الملخص: | Cost and cardinality estimation is considered the Achilles Heel of modern query
optimizers. Poor cardinality estimates lead to bad cost estimates resulting in
sub-optimal query execution plans being selected which drops the performance
of query optimizers. With the recent rise of ML for DB, the database community explored the use of learned methods in cost and cardinality estimation.
However none of the methods till date can achieve prediction speeds required
for modern database systems. In this project we introduce a novel algorithm
(TreeGBM) using Gradient Boosting Trees to solve both cost estimation and
cardinality estimation on numeric JOB workloads based on the IMDB dataset.
We conducted multiple experiments to improve prediction scores and inference
times. Our experiments showed that the TreeGBM was ∼120 times faster than
state-of-the-art learned methods while maintaining good prediction scores. We
stated possible improvements to our method that could help improve prediction
scores and inference times. Future work can add on to the algorithm by using
a new predicate embedding algorithm that does not incur much latency and by
using prefix tries to encode string values. |
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