Automated Database Indexing using Model-free Reinforcement Learning
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2020
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| _version_ | 1866916200098824192 |
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| author | Licks, Gabriel Paludo Meneguzzi, Felipe |
| author_facet | Licks, Gabriel Paludo Meneguzzi, Felipe |
| contents | Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. We develop an architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. In our experimental evaluation, our architecture shows superior performance compared to related work on reinforcement learning and genetic algorithms, maintaining near-optimal index configurations and efficiently scaling to large databases. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2007_14244 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | Automated Database Indexing using Model-free Reinforcement Learning Licks, Gabriel Paludo Meneguzzi, Felipe Databases Artificial Intelligence I.2.6; H.2.4 Configuring databases for efficient querying is a complex task, often carried out by a database administrator. Solving the problem of building indexes that truly optimize database access requires a substantial amount of database and domain knowledge, the lack of which often results in wasted space and memory for irrelevant indexes, possibly jeopardizing database performance for querying and certainly degrading performance for updating. We develop an architecture to solve the problem of automatically indexing a database by using reinforcement learning to optimize queries by indexing data throughout the lifetime of a database. In our experimental evaluation, our architecture shows superior performance compared to related work on reinforcement learning and genetic algorithms, maintaining near-optimal index configurations and efficiently scaling to large databases. |
| title | Automated Database Indexing using Model-free Reinforcement Learning |
| topic | Databases Artificial Intelligence I.2.6; H.2.4 |
| url | https://arxiv.org/abs/2007.14244 |