{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T04:13:38Z","timestamp":1784261618852,"version":"3.55.0"},"reference-count":38,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Database knob tuning is important to achieve high performance (e.g., high throughput and low latency). However, knob tuning is an NP-hard problem and existing methods have several limitations. First, DBAs cannot tune a lot of database instances on different environments (e.g., different database vendors). Second, traditional machine-learning methods either cannot find good configurations or rely on a lot of high-quality training examples which are rather hard to obtain. Third, they only support coarse-grained tuning (e.g., workload-level tuning) but cannot provide fine-grained tuning (e.g., query-level tuning).<\/jats:p>\n          <jats:p>To address these problems, we propose a query-aware database tuning system QTune with a deep reinforcement learning (DRL) model, which can efficiently and effectively tune the database configurations. QTune first featurizes the SQL queries by considering rich features of the SQL queries. Then QTune feeds the query features into the DRL model to choose suitable configurations. We propose a Double-State Deep Deterministic Policy Gradient (DS-DDPG) model to enable query-aware database configuration tuning, which utilizes the actor-critic networks to tune the database configurations based on both the query vector and database states. QTune provides three database tuning granularities: query-level, workload-level, and cluster-level tuning. We deployed our techniques onto three real database systems, and experimental results show that QTune achieves high performance and outperforms the state-of-the-art tuning methods.<\/jats:p>","DOI":"10.14778\/3352063.3352129","type":"journal-article","created":{"date-parts":[[2019,9,18]],"date-time":"2019-09-18T18:36:11Z","timestamp":1568831771000},"page":"2118-2130","source":"Crossref","is-referenced-by-count":223,"title":["QTune"],"prefix":"10.14778","volume":"12","author":[{"given":"Guoliang","family":"Li","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuanhe","family":"Zhou","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shifu","family":"Li","sequence":"additional","affiliation":[{"name":"Huawei Company"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Gao","sequence":"additional","affiliation":[{"name":"Huawei Company"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,8]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"A. F. Agarap. Deep learning using rectified linear units (relu). CoRR abs\/1803.08375 2018.  A. F. Agarap. Deep learning using rectified linear units (relu). CoRR abs\/1803.08375 2018."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3064029"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2886216"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-53455-7_5"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2009.165"},{"key":"e_1_2_1_6_1","volume-title":"CASCON, page 299","author":"Dikaleh S. G.","year":"2017"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687767"},{"key":"e_1_2_1_8_1","first-page":"226","volume-title":"KDD","author":"Ester M.","year":"1996"},{"key":"e_1_2_1_9_1","doi-asserted-by":"crossref","unstructured":"V. Fran\u00e7ois-Lavet P. Henderson R. Islam M. G. Bellemare and J. Pineau. An introduction to deep reinforcement learning. CoRR abs\/1811.12560 2018.  V. Fran\u00e7ois-Lavet P. Henderson R. Islam M. G. Bellemare and J. Pineau. An introduction to deep reinforcement learning. CoRR abs\/1811.12560 2018.","DOI":"10.1561\/9781680835397"},{"key":"e_1_2_1_10_1","unstructured":"D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR abs\/1412.6980 2014.  D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. CoRR abs\/1412.6980 2014."},{"key":"e_1_2_1_11_1","volume-title":"CIDR","author":"Kraska T.","year":"2019"},{"key":"e_1_2_1_12_1","unstructured":"S. Krishnan. Hierarchical Deep Reinforcement Learning For Robotics and Data Science. PhD thesis University of California Berkeley USA 2018.  S. Krishnan. Hierarchical Deep Reinforcement Learning For Robotics and Data Science . PhD thesis University of California Berkeley USA 2018."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.14778\/3137765.3137833"},{"key":"e_1_2_1_14_1","volume-title":"IEEE Data Bulletin","author":"Li G.","year":"2019"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-018-0074-4"},{"key":"e_1_2_1_16_1","unstructured":"T. P. Lillicrap J. J. Hunt A. Pritzel N. Heess T. Erez Y. Tassa D. Silver and D. Wierstra. Continuous control with deep reinforcement learning. CoRR abs\/1509.02971 2015.  T. P. Lillicrap J. J. Hunt A. Pritzel N. Heess T. Erez Y. Tassa D. Silver and D. Wierstra. Continuous control with deep reinforcement learning. CoRR abs\/1509.02971 2015."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2018.8513203"},{"issue":"4","key":"e_1_2_1_18_1","first-page":"303","article-title":"Binary relevance efficacy for multilabel classification","volume":"1","author":"Luaces O.","year":"2012","journal-title":"Progress in AI"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2829492"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3211954.3211957"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14236"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1017992615625"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2012.6252524"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45856-4_2"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2005.274"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5256-5"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1005686.1005739"},{"key":"e_1_2_1_28_1","unstructured":"J. Sun and G. Li. An end-to-end learning-based cost estimator. CoRR abs\/1906.02560 2019.  J. Sun and G. Li. An end-to-end learning-based cost estimator. CoRR abs\/1906.02560 2019."},{"key":"e_1_2_1_29_1","unstructured":"K. Tzoumas T. Sellis and C. S. Jensen. A reinforcement learning approach for adaptive query processing. 2008.  K. Tzoumas T. Sellis and C. S. Jensen. A reinforcement learning approach for adaptive query processing. 2008."},{"key":"e_1_2_1_30_1","first-page":"2613","volume-title":"NIPS","author":"van Hasselt H.","year":"2010"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41019-017-0043-3"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF00992698"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/FSKD.2014.6980831"},{"key":"e_1_2_1_34_1","first-page":"20","volume-title":"VLDB","author":"Weikum G.","year":"2002"},{"key":"e_1_2_1_35_1","volume-title":"CIDR","author":"Wu E.","year":"2019"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3299869.3300085"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-09333-8_1"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3127479.3128605"}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3352063.3352129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T10:43:54Z","timestamp":1672224234000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3352063.3352129"}},"subtitle":["a query-aware database tuning system with deep reinforcement learning"],"short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":38,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2019,8]]}},"alternative-id":["10.14778\/3352063.3352129"],"URL":"https:\/\/doi.org\/10.14778\/3352063.3352129","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2019,8]]}}}