@inproceedings{10.1145/3662165.3662764,
author = {Raman, Aneesh and Huynh, Andy and Lu, Jinqi and Athanassoulis, Manos},
title = {Benchmarking Learned and LSM Indexes for Data Sortedness},
year = {2024},
isbn = {9798400706691},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3662165.3662764},
doi = {10.1145/3662165.3662764},
abstract = {Database systems use indexes on frequently accessed attributes to accelerate query and transaction processing. This requires paying the cost of maintaining and updating those indexes, which can be thought of as the process of adding structure (e.g., sort) to an otherwise unstructured data collection. The indexing cost is redundant when data arrives pre-sorted, even if only up to some degree. While recent work has studied how classical indexes like B+-trees cannot fully exploit the near-sortedness during ingestion, there is a lack of this exploration on other index designs like read-optimized learned indexes or write-optimized LSM-trees.In this paper, we bridge this gap by conducting the first-ever study on the behavior of learned indexes and LSM-trees when varying the data sortedness in an ingestion workload. Specifically, we build on prior work on benchmarking data sortedness on B+-trees and we expand the scope to benchmark: (i) ALEX and LIPP, which are updatable learned index designs; and (ii) the LSM-tree engine offered by RocksDB. We present our evaluation framework and detail key insights on the performance of the index designs when varying data sortedness. Our observations indicate that learned indexes exhibit unpredictable performance when ingesting differently sorted data, while LSM-trees can benefit from sortedness-aware optimizations. We highlight the potential headroom for improvement and lay the groundwork for further research in this domain.},
booktitle = {Proceedings of the Tenth International Workshop on Testing Database Systems},
pages = {16–22},
numpages = {7},
keywords = {Data Sortedness, Indexing, LSM-Trees, Learned-Indexes},
location = {Santiago, AA, Chile},
series = {DBTest '24}
}


@inproceedings {254280,
author = {Konstantinos Kanellis and Ramnatthan Alagappan and Shivaram Venkataraman},
title = {Too Many Knobs to Tune? Towards Faster Database Tuning by Pre-selecting Important Knobs},
booktitle = {12th USENIX Workshop on Hot Topics in Storage and File Systems (HotStorage 20)},
year = {2020},
url = {https://www.usenix.org/conference/hotstorage20/presentation/kanellis},
publisher = {USENIX Association},
month = jul
}

@article{sobri2022study,
  title={A study of database connection pool in microservice architecture},
  author={Sobri, Nur Ayuni Nor and Abas, Mohamad Aqib Haqmi and Yassin, Ihsan Mohd and Ali, Megat Syahirul Amin Megat and Tahir, Nooritawati Md and Zabidi, Azlee and Rizman, Zairi Ismael},
  journal={JOIV: International Journal on Informatics Visualization},
  volume={6},
  number={2-2},
  pages={566--571},
  year={2022}
}

@INPROCEEDINGS{926306,
  author={Sanders, G.L. and Seungkyoon Shin},
  booktitle={Proceedings of the 34th Annual Hawaii International Conference on System Sciences}, 
  title={Denormalization effects on performance of RDBMS}, 
  year={2001},
  volume={},
  number={},
  pages={9 pp.-},
  keywords={Relational databases;Information retrieval;Data warehouses;Transaction databases;Guidelines;System performance;Spatial databases;Data models;Algebra;Stability},
  doi={10.1109/HICSS.2001.926306}}

@inproceedings{martins2021comparing,
  title={Comparing oracle and postgresql, performance and optimization},
  author={Martins, Pedro and Tom{\'e}, Paulo and Wanzeller, Cristina and S{\'a}, Filipe and Abbasi, Maryam},
  booktitle={Trends and Applications in Information Systems and Technologies: Volume 2 9},
  pages={481--490},
  year={2021},
  organization={Springer}
}

@article{mbaiossoumdatabase,
  title={Database Tuning from Relational Database to Big Data},
  author={Mbaiossoum, Bery Leouro and Bellatreche, Ladjel and Batouma, Narkoy and Daouda, Ahmat Mahamat},
  journal={Int. J. Eng. Trends Technol},
  volume={71},
  number={11},
  pages={90--99},
  year={2023}
}

@article{10.1145/1024694.1024720,
author = {Shasha, Dennis and Bonnet, Philippe },
title = {Database tuning principles, experiments, and troubleshooting techniques},
year = {2004},
issue_date = {June 2004},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {33},
number = {2},
issn = {0163-5808},
url = {https://doi.org/10.1145/1024694.1024720},
doi = {10.1145/1024694.1024720},
abstract = {As our reliance on computers and computerized data has increased, we have come to expect more from our computers. We no longer expect our computers to act as large expensive calculators that merely spit out bills and paychecks. We now, additionally, expect our systems to rapidly access and interactively present us with large volumes of accurate data. In fact, our expectations have changed so much, in the past decade, that we no longer focus on what our systems are but rather on what they do. We no longer refer to our systems as computer systems but rather information systems. With these new expectations have come new responsibilities for the information systems professional. We can no longer concern ourselves merely with keeping our systems up and running. We now need to concern ourselves with subjective concepts such as response time and throughput. With current expectations what they are, performance tuning has become vitally important.},
journal = {SIGMOD Rec.},
month = jun,
pages = {115–116},
numpages = {2}
}

@article{shumilovoptimization,
  title={Optimization of queries to MySQL and PostgreSQL Databases for High-Load Applications},
  author={Shumilov, Mykhailo}
}

@article{wang2021udo,
  title={UDO: universal database optimization using reinforcement learning},
  author={Wang, Junxiong and Trummer, Immanuel and Basu, Debabrota},
  journal={arXiv preprint arXiv:2104.01744},
  year={2021}
}

@article{ramu2023optimizing,
  title={Optimizing Database Performance: Strategies for Efficient Query Execution and Resource Utilization},
  author={Ramu, Vivek Basavegowda},
  journal={International Journal of Computer Trends and Technology},
  volume={71},
  number={7},
  pages={15--21},
  year={2023}
}

@inproceedings{van2017automatic,
  title={Automatic database management system tuning through large-scale machine learning},
  author={Van Aken, Dana and Pavlo, Andrew and Gordon, Geoffrey J and Zhang, Bohan},
  booktitle={Proceedings of the 2017 ACM international conference on management of data},
  pages={1009--1024},
  year={2017}
}

@article{zhang2018demonstration,
  title={A demonstration of the ottertune automatic database management system tuning service},
  author={Zhang, Bohan and Van Aken, Dana and Wang, Justin and Dai, Tao and Jiang, Shuli and Lao, Jacky and Sheng, Siyuan and Pavlo, Andrew and Gordon, Geoffrey J},
  journal={Proceedings of the VLDB Endowment},
  volume={11},
  number={12},
  pages={1910--1913},
  year={2018},
  publisher={VLDB Endowment}
}