{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T01:06:16Z","timestamp":1773277576303,"version":"3.50.1"},"reference-count":53,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>\n            Modern cloud databases adopt a\n            <jats:italic>storage-disaggregation<\/jats:italic>\n            architecture that separates the management of computation and storage. A major bottleneck in such an architecture is the network connecting the computation and storage layers. Two solutions have been explored to mitigate the bottleneck:\n            <jats:italic>caching<\/jats:italic>\n            and\n            <jats:italic>computation pushdown.<\/jats:italic>\n            While both techniques can significantly reduce network traffic, existing DBMSs consider them as orthogonal techniques and support only one or the other, leaving potential performance benefits unexploited.\n          <\/jats:p>\n          <jats:p>\n            In this paper we present\n            <jats:italic>FlexPushdownDB (FPDB)<\/jats:italic>\n            , an OLAP cloud DBMS prototype that supports fine-grained hybrid query execution to combine the benefits of caching and computation pushdown in a storage-disaggregation architecture. We build a hybrid query executor based on a new concept called\n            <jats:italic>separable operators<\/jats:italic>\n            to combine the data from the cache and results from the pushdown processing. We also propose a novel\n            <jats:italic>Weighted-LFU<\/jats:italic>\n            cache replacement policy that takes into account the cost of pushdown computation. Our experimental evaluation on the Star Schema Benchmark shows that the hybrid execution outperforms both the conventional\n            <jats:italic>caching-only<\/jats:italic>\n            architecture and\n            <jats:italic>pushdown-only<\/jats:italic>\n            architecture by 2.2X. In the hybrid architecture, our experiments show that Weighted-LFU can outperform the baseline LFU by 37%.\n          <\/jats:p>","DOI":"10.14778\/3476249.3476265","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T16:46:23Z","timestamp":1635353183000},"page":"2101-2113","source":"Crossref","is-referenced-by-count":41,"title":["FlexPushdownDB"],"prefix":"10.14778","volume":"14","author":[{"given":"Yifei","family":"Yang","sequence":"first","affiliation":[{"name":"University of Wisconsin-Madison"}]},{"given":"Matt","family":"Youill","sequence":"additional","affiliation":[{"name":"Burnian"}]},{"given":"Matthew","family":"Woicik","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}]},{"given":"Yizhou","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}]},{"given":"Xiangyao","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Wisconsin-Madison"}]},{"given":"Marco","family":"Serafini","sequence":"additional","affiliation":[{"name":"University of Massachusetts-Amherst"}]},{"given":"Ashraf","family":"Aboulnaga","sequence":"additional","affiliation":[{"name":"Qatar Computing Research Institute"}]},{"given":"Michael","family":"Stonebraker","sequence":"additional","affiliation":[{"name":"Massachusetts Institute of Technology"}]}],"member":"320","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"2012. Akka. https:\/\/akka.io\/.  2012. Akka. https:\/\/akka.io\/."},{"key":"e_1_2_1_2_1","unstructured":"2012. Ceph. https:\/\/ceph.io\/.  2012. Ceph. https:\/\/ceph.io\/."},{"key":"e_1_2_1_3_1","unstructured":"2016. Apache Arrow. https:\/\/arrow.apache.org\/.  2016. Apache Arrow. https:\/\/arrow.apache.org\/."},{"key":"e_1_2_1_4_1","unstructured":"2016. Apache Parquet. https:\/\/parquet.apache.org\/.  2016. Apache Parquet. https:\/\/parquet.apache.org\/."},{"key":"e_1_2_1_5_1","unstructured":"2016. MinIO. https:\/\/min.io\/.  2016. MinIO. https:\/\/min.io\/."},{"key":"e_1_2_1_6_1","unstructured":"2017. AWS Nitro System. https:\/\/aws.amazon.com\/ec2\/nitro\/.  2017. AWS Nitro System. https:\/\/aws.amazon.com\/ec2\/nitro\/."},{"key":"e_1_2_1_7_1","unstructured":"2018. Amazon Athena --- Serverless Interactive Query Service. https:\/\/aws.amazon.com\/athena\/.  2018. Amazon Athena --- Serverless Interactive Query Service. https:\/\/aws.amazon.com\/athena\/."},{"key":"e_1_2_1_8_1","unstructured":"2018. Amazon Redshift. https:\/\/aws.amazon.com\/redshift\/.  2018. Amazon Redshift. https:\/\/aws.amazon.com\/redshift\/."},{"key":"e_1_2_1_9_1","unstructured":"2018. Amazon S3. https:\/\/aws.amazon.com\/s3\/.  2018. Amazon S3. https:\/\/aws.amazon.com\/s3\/."},{"key":"e_1_2_1_10_1","unstructured":"2018. Gandiva: an LLVM-based Arrow expression compiler. https:\/\/arrow.apache.org\/blog\/2018\/12\/05\/gandiva-donation\/.  2018. Gandiva: an LLVM-based Arrow expression compiler. https:\/\/arrow.apache.org\/blog\/2018\/12\/05\/gandiva-donation\/."},{"key":"e_1_2_1_11_1","unstructured":"2018. Presto. https:\/\/prestodb.io\/.  2018. Presto. https:\/\/prestodb.io\/."},{"key":"e_1_2_1_12_1","unstructured":"2020. AQUA (Advanced Query Accelerator) for Amazon Redshift. https:\/\/pages.awscloud.com\/AQUA_Preview.html\/.  2020. AQUA (Advanced Query Accelerator) for Amazon Redshift. https:\/\/pages.awscloud.com\/AQUA_Preview.html\/."},{"key":"e_1_2_1_13_1","unstructured":"2020. Azure Data Lake Storage query acceleration. https:\/\/docs.microsoft.com\/en-us\/azure\/storage\/blobs\/data-lake-storage-query-acceleration\/.  2020. Azure Data Lake Storage query acceleration. https:\/\/docs.microsoft.com\/en-us\/azure\/storage\/blobs\/data-lake-storage-query-acceleration\/."},{"key":"e_1_2_1_14_1","unstructured":"2020. Presto documentation Alluxio Cache Service. https:\/\/prestodb.io\/docs\/current\/cache\/alluxio.html\/.  2020. Presto documentation Alluxio Cache Service. https:\/\/prestodb.io\/docs\/current\/cache\/alluxio.html\/."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487923.3487929"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742797"},{"key":"e_1_2_1_17_1","volume-title":"Proc. INAP","volume":"96","author":"Armstrong Joe","year":"1996","unstructured":"Joe Armstrong . 1996 . Erlang---a Survey of the Language and its Industrial Applications . In Proc. INAP , Vol. 96 . Joe Armstrong. 1996. Erlang---a Survey of the Language and its Industrial Applications. In Proc. INAP, Vol. 96."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1147\/sj.52.0078"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cl.2016.01.002"},{"key":"e_1_2_1_20_1","unstructured":"Hybrid Memory Cube Consortium. 2014. HMCSpecification2.1.  Hybrid Memory Cube Consortium. 2014. HMCSpecification2.1."},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2903741"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2463676.2465295"},{"key":"e_1_2_1_23_1","unstructured":"Phil Francisco. 2011. The Netezza Data Appliance Architecture.  Phil Francisco. 2011. The Netezza Data Appliance Architecture."},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/235968.233328"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/645913.671448"},{"key":"e_1_2_1_26_1","volume-title":"HRL: Efficient and Flexible Reconfigurable Logic for Near-Data Processing. In HPCA. 126--137.","author":"Gao Mingyu","year":"2016","unstructured":"Mingyu Gao and Christos Kozyrakis . 2016 . HRL: Efficient and Flexible Reconfigurable Logic for Near-Data Processing. In HPCA. 126--137. Mingyu Gao and Christos Kozyrakis. 2016. HRL: Efficient and Flexible Reconfigurable Logic for Near-Data Processing. In HPCA. 126--137."},{"key":"e_1_2_1_27_1","volume-title":"Mechanisms, Future Research Directions. arXiv preprint arXiv:1802.00320","author":"Ghose Saugata","year":"2018","unstructured":"Saugata Ghose , Kevin Hsieh , Amirali Boroumand , Rachata Ausavarungnirun , and Onur Mutlu . 2018. Enabling the Adoption of Processing-in-Memory: Challenges , Mechanisms, Future Research Directions. arXiv preprint arXiv:1802.00320 ( 2018 ). Saugata Ghose, Kevin Hsieh, Amirali Boroumand, Rachata Ausavarungnirun, and Onur Mutlu. 2018. Enabling the Adoption of Processing-in-Memory: Challenges, Mechanisms, Future Research Directions. arXiv preprint arXiv:1802.00320 (2018)."},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/191843.191886"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3007787.3001154"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2723372.2742795"},{"key":"e_1_2_1_31_1","unstructured":"Randall Hunt. 2018. S3 Select and Glacier Select - Retrieving Subsets of Objects. https:\/\/aws.amazon.com\/blogs\/aws\/s3-glacier-select\/.  Randall Hunt. 2018. S3 Select and Glacier Select - Retrieving Subsets of Objects. https:\/\/aws.amazon.com\/blogs\/aws\/s3-glacier-select\/."},{"key":"e_1_2_1_32_1","volume-title":"Terabyte Sort on FPGA-accelerated Flash Storage. In IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM). 17--24","author":"Jun Sang-Woo","year":"2017","unstructured":"Sang-Woo Jun , Shuotao Xu , and Arvind. 2017 . Terabyte Sort on FPGA-accelerated Flash Storage. In IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM). 17--24 . Sang-Woo Jun, Shuotao Xu, and Arvind. 2017. Terabyte Sort on FPGA-accelerated Flash Storage. In IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM). 17--24."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/290593.290602"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/3368289.3368298"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3123939.3124553"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.14778\/2367502.2367518"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920886"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-10424-4_17"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/2.928624"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/342009.335413"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.5555\/645923.670992"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352133"},{"key":"e_1_2_1_43_1","volume-title":"Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu, and Raghotham Murthy.","author":"Thusoo Ashish","year":"2010","unstructured":"Ashish Thusoo , Joydeep Sen Sarma , Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu, and Raghotham Murthy. 2010 . Hive --- A Petabyte Scale Data Warehouse Using Hadoop . In ICDE. 996--1005. Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu, and Raghotham Murthy. 2010. Hive --- A Petabyte Scale Data Warehouse Using Hadoop. In ICDE. 996--1005."},{"key":"e_1_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Michael Ubell. 1985. The Intelligent Database Machine (IDM). In Query processing in database systems. 237--247.  Michael Ubell. 1985. The Intelligent Database Machine (IDM). In Query processing in database systems. 237--247.","DOI":"10.1007\/978-3-642-82375-6_14"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196938"},{"key":"e_1_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3056101"},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196937"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.5555\/3388242.3388275"},{"key":"e_1_2_1_49_1","unstructured":"Ronald Weiss. 2012. A Technical Overview of the Oracle Exadata Database Machine and Exadata Storage Server. Oracle White Paper. (2012).  Ronald Weiss. 2012. A Technical Overview of the Oracle Exadata Database Machine and Exadata Storage Server. Oracle White Paper. (2012)."},{"key":"e_1_2_1_50_1","volume-title":"Determining the Optimal Amount of Computation Pushdown for a Cloud Database to Minimize Runtime. MIT Master Thesis","author":"Woicik Matthew","year":"2021","unstructured":"Matthew Woicik . 2021. Determining the Optimal Amount of Computation Pushdown for a Cloud Database to Minimize Runtime. MIT Master Thesis ( 2021 ). Matthew Woicik. 2021. Determining the Optimal Amount of Computation Pushdown for a Cloud Database to Minimize Runtime. MIT Master Thesis (2021)."},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732967.2732972"},{"key":"e_1_2_1_52_1","volume-title":"AQUOMAN: An Analytic-Query Offloading Machine. In MICRO. 386--399.","author":"Xu Shuotao","year":"2020","unstructured":"Shuotao Xu , Thomas Bourgeat , Tianhao Huang , Hojun Kim , Sungjin Lee , and Arvind Arvind . 2020 . AQUOMAN: An Analytic-Query Offloading Machine. In MICRO. 386--399. Shuotao Xu, Thomas Bourgeat, Tianhao Huang, Hojun Kim, Sungjin Lee, and Arvind Arvind. 2020. AQUOMAN: An Analytic-Query Offloading Machine. In MICRO. 386--399."},{"key":"e_1_2_1_53_1","unstructured":"Xiangyao Yu Matt Youill Matthew Woicik Abdurrahman Ghanem Marco Serafini Ashraf Aboulnaga and Michael Stonebraker. 2020. PushdownDB: Accelerating a DBMS using S3 Computation. In ICDE. 1802--1805.  Xiangyao Yu Matt Youill Matthew Woicik Abdurrahman Ghanem Marco Serafini Ashraf Aboulnaga and Michael Stonebraker. 2020. PushdownDB: Accelerating a DBMS using S3 Computation. In ICDE. 1802--1805."}],"container-title":["Proceedings of the VLDB Endowment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.14778\/3476249.3476265","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T09:58:47Z","timestamp":1672221527000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.14778\/3476249.3476265"}},"subtitle":["hybrid pushdown and caching in a cloud DBMS"],"short-title":[],"issued":{"date-parts":[[2021,7]]},"references-count":53,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["10.14778\/3476249.3476265"],"URL":"https:\/\/doi.org\/10.14778\/3476249.3476265","relation":{},"ISSN":["2150-8097"],"issn-type":[{"value":"2150-8097","type":"print"}],"subject":[],"published":{"date-parts":[[2021,7]]}}}