LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for
  Boosting Query Efficiency
Query rewrite, which aims to generate more efficient queries by altering a
SQL query's structure without changing the query result, has been an important
research problem. In order to maintain equivalence between the rewritten query
and the original one during rewriting, traditional query rewrite methods always
rewrite the queries following certain rewrite rules. However, some problems
still remain. Firstly, existing methods of finding the optimal choice or
sequence of rewrite rules are still limited and the process always costs a lot
of resources. Methods involving discovering new rewrite rules typically require
complicated proofs of structural logic or extensive user interactions.
Secondly, current query rewrite methods usually rely highly on DBMS cost
estimators which are often not accurate. In this paper, we address these
problems by proposing a novel method of query rewrite named LLM-R2, adopting a
large language model (LLM) to propose possible rewrite rules for a database
rewrite system. To further improve the inference ability of LLM in recommending
rewrite rules, we train a contrastive model by curriculum to learn query
representations and select effective query demonstrations for the LLM.
Experimental results have shown that our method can significantly improve the
query execution efficiency and outperform the baseline methods. In addition,
our method enjoys high robustness across different datasets.
6625f216fe61f6233049686b
Zhaodonghui Li
claimed_verified
2024-04-22T11:53:24.832Z
662620578c67281c9eb17f7b
/avatars/0e9c26d2234a9f1898a380f183b1645a.svg
Li Zhaodonghui
user
LZ12DH
6625f216fe61f6233049686c
Haitao Yuan
admin_assigned
2024-04-22T14:05:56.838Z
64a5224a49fe135745c715ad
/avatars/a645e79b0f06eea3f0bf796535fac18d.svg
haitao yuan
user
yht520100
6625f216fe61f6233049686d
Huiming Wang
6625f216fe61f6233049686e
Gao Cong
6625f216fe61f6233049686f
Lidong Bing
claimed_verified
2024-04-22T09:19:13.753Z
6454685a548f22be598414c4
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/eMjMWKJ-AouF7eY1-RzGF.jpeg
Lidong Bing
user
LidongBing
LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for
  Boosting Query Efficiency
Query rewrite, which aims to generate more efficient queries by altering a
SQL query's structure without changing the query result, has been an important
research problem. In order to maintain equivalence between the rewritten query
and the original one during rewriting, traditional query rewrite methods always
rewrite the queries following certain rewrite rules. However, some problems
still remain. Firstly, existing methods of finding the optimal choice or
sequence of rewrite rules are still limited and the process always costs a lot
of resources. Methods involving discovering new rewrite rules typically require
complicated proofs of structural logic or extensive user interactions.
Secondly, current query rewrite methods usually rely highly on DBMS cost
estimators which are often not accurate. In this paper, we address these
problems by proposing a novel method of query rewrite named LLM-R2, adopting a
large language model (LLM) to propose possible rewrite rules for a database
rewrite system. To further improve the inference ability of LLM in recommending
rewrite rules, we train a contrastive model by curriculum to learn query
representations and select effective query demonstrations for the LLM.
Experimental results have shown that our method can significantly improve the
query execution efficiency and outperform the baseline methods. In addition,
our method enjoys high robustness across different datasets.
A new query rewriting method using large language models and contrastive learning improves query execution efficiency and robustness across various datasets.
large language model contrastive model curriculum learning query representations query demonstrations query rewriting query execution efficiency