{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T21:40:01Z","timestamp":1729978801309,"version":"3.28.0"},"reference-count":92,"publisher":"Association for Computing Machinery (ACM)","issue":"11","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2023,7]]},"abstract":"Elasticity of compute and storage is crucial for analytical cloud database systems. All cloud vendors provide disaggregated object stores, which can be used as storage backend for analytical query engines. Until recently, local storage was unavoidable to process large tables efficiently due to the bandwidth limitations of the network infrastructure in public clouds. However, the gap between remote network and local NVMe bandwidth is closing, making cloud storage more attractive. This paper presents a blueprint for performing efficient analytics directly on cloud object stores. We derive cost- and performance-optimal retrieval configurations for cloud object stores with the first in-depth study of this foundational service in the context of analytical query processing. For achieving high retrieval performance, we presentAnyBlob<\/jats:italic>, a novel download manager for query engines that optimizes throughput while minimizing CPU usage. We discuss the integration of high-performance data retrieval in query engines and demonstrate it by incorporatingAnyBlob<\/jats:italic>in our database systemUmbra.<\/jats:italic>Our experiments show that even without caching,Umbra<\/jats:italic>with integratedAnyBlob<\/jats:italic>achieves similar performance to state-of-the-art cloud data warehouses that cache data on local SSDs while improving resource elasticity.<\/jats:p>","DOI":"10.14778\/3611479.3611486","type":"journal-article","created":{"date-parts":[[2023,8,25]],"date-time":"2023-08-25T02:08:08Z","timestamp":1692929288000},"page":"2769-2782","update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Exploiting Cloud Object Storage for High-Performance Analytics"],"prefix":"10.14778","volume":"16","author":[{"given":"Dominik","family":"Durner","sequence":"first","affiliation":[{"name":"Technische Universit\u00e4t M\u00fcnchen"}]},{"given":"Viktor","family":"Leis","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t M\u00fcnchen"}]},{"given":"Thomas","family":"Neumann","sequence":"additional","affiliation":[{"name":"Technische Universit\u00e4t M\u00fcnchen"}]}],"member":"320","published-online":{"date-parts":[[2023,8,24]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Merv Adrian. 2022. DBMS Market Transformation 2021: The Big Picture. https:\/\/blogs.gartner.com\/merv-adrian\/2022\/04\/16\/dbms-market-transformation-2021-the-big-picture\/. accessed: 2022-09-30. Merv Adrian. 2022. DBMS Market Transformation 2021: The Big Picture. https:\/\/blogs.gartner.com\/merv-adrian\/2022\/04\/16\/dbms-market-transformation-2021-the-big-picture\/. accessed: 2022-09-30."},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415545"},{"key":"e_1_2_1_3_1","unstructured":"Amazon. 2021. What's the maximum transfer speed between Amazon EC2 and Amazon S3? https:\/\/aws.amazon.com\/premiumsupport\/knowledge-center\/s3-maximum-transfer-speed-ec2\/. accessed: 2022-09-15. Amazon. 2021. What's the maximum transfer speed between Amazon EC2 and Amazon S3? https:\/\/aws.amazon.com\/premiumsupport\/knowledge-center\/s3-maximum-transfer-speed-ec2\/. accessed: 2022-09-15."},{"key":"e_1_2_1_4_1","unstructured":"Amazon. 2022. Amazon S3 Storage Classes. https:\/\/aws.amazon.com\/s3\/storage-classes\/. accessed: 2022-10-05. Amazon. 2022. Amazon S3 Storage Classes. https:\/\/aws.amazon.com\/s3\/storage-classes\/. accessed: 2022-10-05."},{"key":"e_1_2_1_5_1","unstructured":"Amazon. 2022. Announcing Amazon EC2 C7gn instances (Preview). https:\/\/aws.amazon.com\/about-aws\/whats-new\/2022\/11\/announcing-amazon-ec2-c7gn-instances-preview\/. accessed: 2023-06-17. Amazon. 2022. Announcing Amazon EC2 C7gn instances (Preview). https:\/\/aws.amazon.com\/about-aws\/whats-new\/2022\/11\/announcing-amazon-ec2-c7gn-instances-preview\/. accessed: 2023-06-17."},{"key":"e_1_2_1_6_1","unstructured":"Amazon. 2022. AQUA (Advanced Query Accelerator) for Amazon Redshift. https:\/\/aws.amazon.com\/redshift\/features\/aqua\/. accessed: 2022-10-12. Amazon. 2022. AQUA (Advanced Query Accelerator) for Amazon Redshift. https:\/\/aws.amazon.com\/redshift\/features\/aqua\/. accessed: 2022-10-12."},{"key":"e_1_2_1_7_1","unstructured":"Amazon. 2022. AWS SDK for C++. https:\/\/github.com\/aws\/aws-sdk-cpp. accessed: 2022-10-05. Amazon. 2022. AWS SDK for C++. https:\/\/github.com\/aws\/aws-sdk-cpp. accessed: 2022-10-05."},{"key":"e_1_2_1_8_1","unstructured":"Amazon. 2022. Encryption in transit. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/data-protection.html#encryption-transit. accessed: 2022-09-30. Amazon. 2022. Encryption in transit. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/data-protection.html#encryption-transit. accessed: 2022-09-30."},{"key":"e_1_2_1_9_1","unstructured":"Amazon. 2022. Network maximum transmission unit (MTU) for your EC2 instance. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/network_mtu.html. accessed: 2022-10-11. Amazon. 2022. Network maximum transmission unit (MTU) for your EC2 instance. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/network_mtu.html. accessed: 2022-10-11."},{"key":"e_1_2_1_10_1","unstructured":"Amazon. 2022. Performance Guidelines for Amazon S3. https:\/\/docs.aws.amazon.com\/AmazonS3\/latest\/userguide\/optimizing-performance-guidelines.html. accessed: 2022-10-11. Amazon. 2022. Performance Guidelines for Amazon S3. https:\/\/docs.aws.amazon.com\/AmazonS3\/latest\/userguide\/optimizing-performance-guidelines.html. accessed: 2022-10-11."},{"key":"e_1_2_1_11_1","unstructured":"Amazon. 2022. Retrieve security credentials from instance metadata. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/iam-roles-for-amazon-ec2.html#instance-metadata-security-credentials. accessed: 2022-10-15. Amazon. 2022. Retrieve security credentials from instance metadata. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/iam-roles-for-amazon-ec2.html#instance-metadata-security-credentials. accessed: 2022-10-15."},{"key":"e_1_2_1_12_1","unstructured":"Amazon. 2022. Spot Instance interruption notices. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/spot-instance-termination-notices.html. accessed: 2022-10-08. Amazon. 2022. Spot Instance interruption notices. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/spot-instance-termination-notices.html. accessed: 2022-10-08."},{"key":"e_1_2_1_13_1","unstructured":"Amazon. 2023. Amazon S3 pricing. https:\/\/aws.amazon.com\/s3\/pricing. accessed: 2023-06-17. Amazon. 2023. Amazon S3 pricing. https:\/\/aws.amazon.com\/s3\/pricing. accessed: 2023-06-17."},{"key":"e_1_2_1_14_1","unstructured":"Amazon. 2023. Compute optimizes instances: Network performance. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/compute-optimized-instances.html. accessed: 2023-05-02. Amazon. 2023. Compute optimizes instances: Network performance. https:\/\/docs.aws.amazon.com\/AWSEC2\/latest\/UserGuide\/compute-optimized-instances.html. accessed: 2023-05-02."},{"key":"e_1_2_1_15_1","unstructured":"Amazon. 2023. Filtering and retrieving data using Amazon S3 Select. https:\/\/docs.aws.amazon.com\/AmazonS3\/latest\/userguide\/selecting-content-from-objects.html. accessed: 2023-05-02. Amazon. 2023. Filtering and retrieving data using Amazon S3 Select. https:\/\/docs.aws.amazon.com\/AmazonS3\/latest\/userguide\/selecting-content-from-objects.html. accessed: 2023-05-02."},{"key":"e_1_2_1_16_1","volume-title":"Socrates: The New SQL Server in the Cloud. In SIGMOD Conference. 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ACM."},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2610507"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.14778\/3461535.3461549"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/3448016.3457540"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352141"},{"key":"e_1_2_1_57_1","doi-asserted-by":"crossref","unstructured":"Feilong Liu Lingyan Yin and Spyros Blanas. 2017. Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems. In EuroSys. ACM 48--63. Feilong Liu Lingyan Yin and Spyros Blanas. 2017. Design and Evaluation of an RDMA-aware Data Shuffling Operator for Parallel Database Systems. In EuroSys. 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Cloud Storage Pricing. https:\/\/www.oracle.com\/cloud\/storage\/pricing\/. accessed: 2023-06-17."},{"key":"e_1_2_1_65_1","unstructured":"Oracle. 2023. Software Development Kits. https:\/\/docs.oracle.com\/en-us\/iaas\/Content\/API\/Concepts\/sdks.htm. accessed: 2023-06-17. Oracle. 2023. Software Development Kits. https:\/\/docs.oracle.com\/en-us\/iaas\/Content\/API\/Concepts\/sdks.htm. accessed: 2023-06-17."},{"volume-title":"Making Sense of Performance in Data Analytics Frameworks","author":"Ousterhout Kay","key":"e_1_2_1_66_1","unstructured":"Kay Ousterhout , Ryan Rasti , Sylvia Ratnasamy , Scott Shenker , and Byung-Gon Chun . 2015. Making Sense of Performance in Data Analytics Frameworks . In NSDI. USENIX Association , 293--307. Kay Ousterhout, Ryan Rasti, Sylvia Ratnasamy, Scott Shenker, and Byung-Gon Chun. 2015. Making Sense of Performance in Data Analytics Frameworks. In NSDI. USENIX Association, 293--307."},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476311.3476391"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.14778\/3461535.3461538"},{"key":"e_1_2_1_69_1","volume-title":"Starling: A Scalable Query Engine on Cloud Functions. In SIGMOD Conference. ACM, 131--141","author":"Perron Matthew","year":"2020","unstructured":"Matthew Perron , Raul Castro Fernandez , David J. DeWitt , and Samuel Madden . 2020 . Starling: A Scalable Query Engine on Cloud Functions. In SIGMOD Conference. ACM, 131--141 . Matthew Perron, Raul Castro Fernandez, David J. DeWitt, and Samuel Madden. 2020. Starling: A Scalable Query Engine on Cloud Functions. In SIGMOD Conference. ACM, 131--141."},{"key":"e_1_2_1_70_1","unstructured":"Simeon Pilgrim. 2019. What are the specifications of a Snowflake server? https:\/\/stackoverflow.com\/questions\/58973007\/what-are-the-specifications-of-a-snowflake-server\/58982398. accessed: 2023-05-02. Simeon Pilgrim. 2019. What are the specifications of a Snowflake server? https:\/\/stackoverflow.com\/questions\/58973007\/what-are-the-specifications-of-a-snowflake-server\/58982398. accessed: 2023-05-02."},{"key":"e_1_2_1_71_1","volume-title":"Thomas Fenech, David Carrera, Jos\u00e9 A. Blakeley, Umar Farooq Minhas, and Nikola Vujic.","author":"Poggi Nicol\u00e1s","year":"2016","unstructured":"Nicol\u00e1s Poggi , Josep Lluis Berral , Thomas Fenech, David Carrera, Jos\u00e9 A. Blakeley, Umar Farooq Minhas, and Nikola Vujic. 2016 . The state of SQL-on-Hadoop in the cloud. In IEEE BigData. IEEE Computer Society , 1432--1443. Nicol\u00e1s Poggi, Josep Lluis Berral, Thomas Fenech, David Carrera, Jos\u00e9 A. Blakeley, Umar Farooq Minhas, and Nikola Vujic. 2016. The state of SQL-on-Hadoop in the cloud. In IEEE BigData. IEEE Computer Society, 1432--1443."},{"key":"e_1_2_1_72_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920902"},{"key":"e_1_2_1_73_1","volume-title":"Presto: SQL on Everything","author":"Sethi Raghav","year":"2019","unstructured":"Raghav Sethi , Martin Traverso , Dain Sundstrom , David Phillips , Wenlei Xie , Yutian Sun , Nezih Yegitbasi , Haozhun Jin , Eric Hwang , Nileema Shingte , and Christopher Berner . 2019 . Presto: SQL on Everything . In ICDE. IEEE , 1802--1813. Raghav Sethi, Martin Traverso, Dain Sundstrom, David Phillips, Wenlei Xie, Yutian Sun, Nezih Yegitbasi, Haozhun Jin, Eric Hwang, Nileema Shingte, and Christopher Berner. 2019. Presto: SQL on Everything. In ICDE. IEEE, 1802--1813."},{"key":"e_1_2_1_74_1","volume-title":"Irwin","author":"Shastri Supreeth","year":"2017","unstructured":"Supreeth Shastri and David E . Irwin . 2017 . HotSpot: automated server hopping in cloud spot markets. In SoCC. ACM , 493--505. Supreeth Shastri and David E. Irwin. 2017. HotSpot: automated server hopping in cloud spot markets. In SoCC. ACM, 493--505."},{"key":"e_1_2_1_75_1","unstructured":"Matt Sidley and Sally Guo. 2021. Deep dive on Amazon S3. AWS re:Invent https:\/\/www.slideshare.net\/AmazonWebServices\/stg301deep-dive-on-amazon-s3-and-glacier-architecture https:\/\/www.youtube.com\/watch?v=9_vScxbIQLY. accessed: 2022-09-10. Matt Sidley and Sally Guo. 2021. Deep dive on Amazon S3. AWS re:Invent https:\/\/www.slideshare.net\/AmazonWebServices\/stg301deep-dive-on-amazon-s3-and-glacier-architecture https:\/\/www.youtube.com\/watch?v=9_vScxbIQLY. accessed: 2022-09-10."},{"key":"e_1_2_1_76_1","volume-title":"Shenoy","author":"Subramanya Supreeth","year":"2015","unstructured":"Supreeth Subramanya , Tian Guo , Prateek Sharma , David E. Irwin , and Prashant J . Shenoy . 2015 . SpotOn: a batch computing service for the spot market. In SoCC. ACM , 329--341. Supreeth Subramanya, Tian Guo, Prateek Sharma, David E. Irwin, and Prashant J. Shenoy. 2015. SpotOn: a batch computing service for the spot market. In SoCC. ACM, 329--341."},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352133"},{"key":"e_1_2_1_78_1","volume-title":"Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Anthony, 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 Anthony, Hao Liu, and Raghotham Murthy. 2010 . Hive - a petabyte scale data warehouse using Hadoop. In ICDE. IEEE Computer Society , 996--1005. Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Anthony, Hao Liu, and Raghotham Murthy. 2010. Hive - a petabyte scale data warehouse using Hadoop. In ICDE. IEEE Computer Society, 996--1005."},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3196938"},{"key":"e_1_2_1_80_1","unstructured":"Daniel Vassallo. 2023. Measure Amazon S3's performance from any location. https:\/\/github.com\/dvassallo\/s3-benchmark. accessed: 2023-05-02. Daniel Vassallo. 2023. Measure Amazon S3's performance from any location. https:\/\/github.com\/dvassallo\/s3-benchmark. accessed: 2023-05-02."},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1145\/3035918.3056101"},{"volume-title":"Building An Elastic Query Engine on Disaggregated Storage","author":"Vuppalapati Midhul","key":"e_1_2_1_82_1","unstructured":"Midhul Vuppalapati , Justin Miron , Rachit Agarwal , Dan Truong , Ashish Motivala , and Thierry Cruanes . 2020. Building An Elastic Query Engine on Disaggregated Storage . In NSDI. USENIX Association , 449--462. Midhul Vuppalapati, Justin Miron, Rachit Agarwal, Dan Truong, Ashish Motivala, and Thierry Cruanes. 2020. Building An Elastic Query Engine on Disaggregated Storage. In NSDI. 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Irene Zhang, Amanda Raybuck, Pratyush Patel, Kirk Olynyk, Jacob Nelson, Omar S. Navarro Leija, Ashlie Martinez, Jing Liu, Anna Kornfeld Simpson, Sujay Jayakar, Pedro Henrique Penna, Max Demoulin, Piali Choudhury, and Anirudh Badam. 2021. The Demikernel Datapath OS Architecture for Microsecond-scale Datacenter Systems. In SOSP. ACM, 195--211."},{"key":"e_1_2_1_89_1","unstructured":"Qizhen Zhang Philip A. Bernstein Daniel S. Berger Badrish Chandramouli Vincent Liu and Boon Thau Loo. 2022. CompuCache: Remote Computable Caching using Spot VMs. In CIDR. www.cidrdb.org. Qizhen Zhang Philip A. Bernstein Daniel S. Berger Badrish Chandramouli Vincent Liu and Boon Thau Loo. 2022. CompuCache: Remote Computable Caching using Spot VMs. In CIDR. www.cidrdb.org."},{"key":"e_1_2_1_90_1","unstructured":"Qizhen Zhang Yifan Cai Sebastian Angel Vincent Liu Ang Chen and Boon Thau Loo. 2020. Rethinking Data Management Systems for Disaggregated Data Centers. In CIDR. www.cidrdb.org. 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