Proceedings of the Vldb Endowment最新文献_第9页

Automatic SQL Error Mitigation in Oracle Oracle中的自动SQL错误缓解

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611568

Krishna Kantikiran Pasupuleti, Jiakun Li, Hong Su, Mohamed Ziauddin

{"title":"Automatic SQL Error Mitigation in Oracle","authors":"Krishna Kantikiran Pasupuleti, Jiakun Li, Hong Su, Mohamed Ziauddin","doi":"10.14778/3611540.3611568","DOIUrl":"https://doi.org/10.14778/3611540.3611568","url":null,"abstract":"Despite best coding practices, software bugs are inevitable in a large codebase. In traditional databases, when errors occur during query processing, they disrupt user workflow until workarounds are found and applied. Manual identification of workarounds often relies on a trial-and-error method. The process is not only time-consuming but also requires domain expertise that users are often lacking. In this paper, we propose a framework to automatically mitigate errors that occur during query compilation (including optimization and code generation) without any user intervention. An error is intercepted by the database internally, a workaround is identified for it, and the query is recompiled using the workaround. The entire process remains transparent to the user with the query being executed seamlessly. The proposed technique handles SQL errors during query compilation and provides three types of mitigation strategies - i) quickly failover to one of the readily-available historical plans for the statement ii) apply targeted error-correcting directives (hints) identified from the optimizer context at the time of the error iii) modify the global configuration of the optimizer using hints. This feature has been implemented and will be released in an upcoming version of Oracle Autonomous Database.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135003296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural Language Interfaces for Databases with Deep Learning 深度学习数据库的自然语言接口

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611575

George Katsogiannis-Meimarakis, Mike Xydas, Georgia Koutrika

{"title":"Natural Language Interfaces for Databases with Deep Learning","authors":"George Katsogiannis-Meimarakis, Mike Xydas, Georgia Koutrika","doi":"10.14778/3611540.3611575","DOIUrl":"https://doi.org/10.14778/3611540.3611575","url":null,"abstract":"In the age of the Digital Revolution, almost all human activities, from industrial and business operations to medical and academic research, are reliant on the constant integration and utilisation of ever-increasing volumes of data. However, the explosive volume and complexity of data makes data querying and exploration challenging even for experts, and makes the need to democratise the access to data, even for non-technical users, all the more evident. It is time to lift all technical barriers, by empowering users to access relational databases through conversation. We consider 3 main research areas that a natural language data interface is based on: Text-to-SQL, SQL-to-Text, and Data-to-Text. The purpose of this tutorial is a deep dive into these areas, covering state-of-the-art techniques and models, and explaining how the progress in the deep learning field has led to impressive advancements. We will present benchmarks that sparked research and competition, and discuss open problems and research opportunities with one of the most important challenges being the integration of these 3 research areas into one conversational system.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"91 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135003302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interactive Demonstration of EVA EVA互动演示

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611626

Gaurav Tarlok Kakkar, Aryan Rajoria, Myna Prasanna Kalluraya, Ashmita Raju, Jiashen Cao, Kexin Rong, Joy Arulraj

引用次数: 1

The Story of AWS Glue AWS Glue的故事

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611547

Mohit Saxena, Benjamin Sowell, Daiyan Alamgir, Nitin Bahadur, Bijay Bisht, Santosh Chandrachood, Chitti Keswani, G. Krishnamoorthy, Austin Lee, Bohou Li, Zach Mitchell, Vaibhav Porwal, Maheedhar Reddy Chappidi, Brian Ross, Noritaka Sekiyama, Omer Zaki, Linchi Zhang, Mehul A. Shah

{"title":"The Story of AWS Glue","authors":"Mohit Saxena, Benjamin Sowell, Daiyan Alamgir, Nitin Bahadur, Bijay Bisht, Santosh Chandrachood, Chitti Keswani, G. Krishnamoorthy, Austin Lee, Bohou Li, Zach Mitchell, Vaibhav Porwal, Maheedhar Reddy Chappidi, Brian Ross, Noritaka Sekiyama, Omer Zaki, Linchi Zhang, Mehul A. Shah","doi":"10.14778/3611540.3611547","DOIUrl":"https://doi.org/10.14778/3611540.3611547","url":null,"abstract":"AWS Glue is Amazon's serverless data integration cloud service that makes it simple and cost effective to extract, clean, enrich, load, and organize data. Originally launched in August 2017, AWS Glue began as an extract-transform-load (ETL) service designed to relieve developers and data engineers of the undifferentiated heavy lifting needed to load databases, data warehouses, and build data lakes on Amazon S3. Since then, it has evolved to serve a larger audience including ETL specialists and data scientists, and includes a broader suite of data integration capabilities. Today, hundreds of thousands of customers use AWS Glue every month. In this paper, we describe the use cases and challenges cloud customers face in preparing data for analytics and the tenets we chose to drive Glue's design. We chose early on to focus on ease-of-use, scale, and extensibility. At its core, Glue offers serverless Apache Spark and Python engines backed by a purpose-built resource manager for fast startup and auto-scaling. In Spark, it offers a new data structure --- DynamicFrames --- for manipulating messy schema-free semi-structured data such as event logs, a variety of transformations and tooling to simplify data preparation, and a new shuffle plugin to offload to cloud storage. It also includes a Hivemetastore compatible Data Catalog with Glue crawlers to build and manage metadata, e.g. for data lakes on Amazon S3. Finally, Glue Studio is its visual interface for authoring Spark and Python-based ETL jobs. We describe the innovations that differentiate AWS Glue and drive its popularity and how it has evolved over the years.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134996886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Will LLMs Reshape, Supercharge, or Kill Data Science? (VLDB 2023 Panel) 法学硕士将重塑、强化还是扼杀数据科学?(VLDB 2023面板)

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611634

Alon Halevy, Yejin Choi, Avrilia Floratou, Michael J. Franklin, Natasha Noy, Haixun Wang

引用次数: 1

CDSBen: Benchmarking the Performance of Storage Services in Cloud-Native Database System at ByteDance CDSBen:在ByteDance上对云原生数据库系统中的存储服务性能进行基准测试

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611549

Jiashu Zhang, Wen Jiang, Bo Tang, Haoxiang Ma, Lixun Cao, Zhongbin Jiang, Yuanyuan Nie, Fan Wang, Lei Zhang, Yuming Liang

引用次数: 0

Sniffer: A Novel Model Type Detection System against Machine-Learning-as-a-Service Platforms Sniffer:一种针对机器学习即服务平台的新型模型类型检测系统

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611591

Zhuo Ma, Yilong Yang, Bin Xiao, Yang Liu, Xinjing Liu, Zhuoran Ma, Tong Yang

{"title":"Sniffer: A Novel Model Type Detection System against Machine-Learning-as-a-Service Platforms","authors":"Zhuo Ma, Yilong Yang, Bin Xiao, Yang Liu, Xinjing Liu, Zhuoran Ma, Tong Yang","doi":"10.14778/3611540.3611591","DOIUrl":"https://doi.org/10.14778/3611540.3611591","url":null,"abstract":"Recent works explore several attacks against Machine-Learning-as-a-Service (MLaaS) platforms (e.g., the model stealing attack), allegedly posing potential real-world threats beyond viability in laboratories. However, hampered by model-type-sensitive , most of the attacks can hardly break mainstream real-world MLaaS platforms. That is, many MLaaS attacks are designed against only one certain type of model, such as tree models or neural networks. As the black-box MLaaS interface hides model type info, the attacker cannot choose a proper attack method with confidence, limiting the attack performance. In this paper, we demonstrate a system, named Sniffer, that is capable of making model-type-sensitive attacks \"great again\" in real-world applications. Specifically, Sniffer consists of four components: Generator, Querier, Probe, and Arsenal. The first two components work for preparing attack samples. Probe, as the most characteristic component in Sniffer, implements a series of self-designed algorithms to determine the type of models hidden behind the black-box MLaaS interfaces. With model type info unraveled, an optimum method can be selected from Arsenal (containing multiple attack methods) to accomplish its attack. Our demonstration shows how the audience can interact with Sniffer in a web-based interface against five mainstream MLaaS platforms.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134998300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DHive: Query Execution Performance Analysis via Dataflow in Apache Hive Hive: Apache Hive中基于数据流的查询执行性能分析

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611605

Chaozu Zhang, Qiaomu Shen, Bo Tang

引用次数: 0

Anser: Adaptive Information Sharing Framework of AnalyticDB 答:AnalyticDB自适应信息共享框架

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611553

Liang Lin, Yuhan Li, Bin Wu, Huijun Mai, Renjie Lou, Jian Tan, Feifei Li

{"title":"Anser: Adaptive Information Sharing Framework of AnalyticDB","authors":"Liang Lin, Yuhan Li, Bin Wu, Huijun Mai, Renjie Lou, Jian Tan, Feifei Li","doi":"10.14778/3611540.3611553","DOIUrl":"https://doi.org/10.14778/3611540.3611553","url":null,"abstract":"The surge in data analytics has fostered burgeoning demand for AnalyticDB on Alibaba Cloud, which has well served thousands of customers from various business sectors. The most notable feature is the diversity of the workloads it handles, including batch processing, real-time data analytics, and unstructured data analytics. To improve the overall performance for such diverse workloads, one of the major challenges is to optimize long-running complex queries without sacrificing the processing efficiency of short-running interactive queries. While existing methods attempt to utilize runtime dynamic statistics for adaptive query processing, they often focus on specific scenarios instead of providing a holistic solution. To address this challenge, we propose a new framework called Anser , which enhances the design of traditional distributed data warehouses by embedding a new information sharing mechanism. This allows for the efficient management of the production and consumption of various dynamic information across the system. Building on top of Anser , we introduce a novel scheduling policy that optimizes both data and information exchanges within the physical plan, enabling the acceleration of complex analytical queries without sacrificing the performance of short-running interactive queries. We conduct comprehensive experiments over public and in-house workloads to demonstrate the effectiveness and efficiency of our proposed information sharing framework.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135003293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ScalarDB: Universal Transaction Manager for Polystores 用于polystore的通用事务管理器

3区计算机科学

Proceedings of the Vldb Endowment Pub Date : 2023-08-01 DOI: 10.14778/3611540.3611563

Hiroyuki Yamada, Toshihiro Suzuki, Yuji Ito, Jun Nemoto

引用次数: 1