Pub Date : 2023-08-01DOI: 10.14778/3611540.3611569
Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Pritam Damania, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Ajit Mathews, Shen Li
It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industry leaders, resulting in an implicit technical barrier for the wider community to access and leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training. FSDP has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and CUDA memory caching allocator, to provide non-intrusive user experiences and high training efficiency. Additionally, FSDP natively incorporates a range of techniques and settings to optimize resource utilization across a variety of hardware configurations. The experimental results demonstrate that FSDP is capable of achieving comparable performance to Distributed Data Parallel while providing support for significantly larger models with near-linear scalability in terms of TFLOPS.
{"title":"PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel","authors":"Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Pritam Damania, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Ajit Mathews, Shen Li","doi":"10.14778/3611540.3611569","DOIUrl":"https://doi.org/10.14778/3611540.3611569","url":null,"abstract":"It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of machine learning systems research, which has enabled the development and exploration of large models, such abilities remain confined to a small group of advanced users and industry leaders, resulting in an implicit technical barrier for the wider community to access and leverage these technologies. In this paper, we introduce PyTorch Fully Sharded Data Parallel (FSDP) as an industry-grade solution for large model training. FSDP has been closely co-designed with several key PyTorch core components including Tensor implementation, dispatcher system, and CUDA memory caching allocator, to provide non-intrusive user experiences and high training efficiency. Additionally, FSDP natively incorporates a range of techniques and settings to optimize resource utilization across a variety of hardware configurations. The experimental results demonstrate that FSDP is capable of achieving comparable performance to Distributed Data Parallel while providing support for significantly larger models with near-linear scalability in terms of TFLOPS.","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":"135165172","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}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611629
Junxiong Wang, Mitchell Gray, Immanuel Trummer, Ahmet Kara, Dan Olteanu
Performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. It is challenging to identify suitable orders prior to query execution due to the huge search space of possible orders and unreliable execution cost estimates in case of data skew or data correlation. We demonstrate ADOPT, a novel query engine that integrates adaptive query processing with a worst-case optimal join algorithm. ADOPT divides query execution into episodes, during which different attribute orders are invoked. With runtime feedback on performance of different attribute orders, ADOPT rapidly approaches near-optimal orders. Moreover, ADOPT uses a unique data structure which keeps track of the processed input data to prevent redundant work across different episodes. It selects attribute orders to try via reinforcement learning, balancing the need for exploring new orders with the desire to exploit promising orders. In experiments, ADOPT outperforms baselines, including commercial and open-source systems utilizing worst-case optimal join algorithms, particularly for complex queries that are difficult to optimize.
{"title":"Demonstrating ADOPT: Adaptively Optimizing Attribute Orders for Worst-Case Optimal Joins via Reinforcement Learning","authors":"Junxiong Wang, Mitchell Gray, Immanuel Trummer, Ahmet Kara, Dan Olteanu","doi":"10.14778/3611540.3611629","DOIUrl":"https://doi.org/10.14778/3611540.3611629","url":null,"abstract":"Performance of worst-case optimal join algorithms depends on the order in which the join attributes are processed. It is challenging to identify suitable orders prior to query execution due to the huge search space of possible orders and unreliable execution cost estimates in case of data skew or data correlation. We demonstrate ADOPT, a novel query engine that integrates adaptive query processing with a worst-case optimal join algorithm. ADOPT divides query execution into episodes, during which different attribute orders are invoked. With runtime feedback on performance of different attribute orders, ADOPT rapidly approaches near-optimal orders. Moreover, ADOPT uses a unique data structure which keeps track of the processed input data to prevent redundant work across different episodes. It selects attribute orders to try via reinforcement learning, balancing the need for exploring new orders with the desire to exploit promising orders. In experiments, ADOPT outperforms baselines, including commercial and open-source systems utilizing worst-case optimal join algorithms, particularly for complex queries that are difficult to optimize.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"8 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":"134996879","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}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611630
Immanuel Trummer
GPT-DB generates code for SQL processing in general-purpose programming languages such as Python. Generated code can be freely customized using user-provided natural language instructions. This enables users, for instance, to try out specific libraries for SQL processing or to generate non-standard output while processing. GPT-DB is based on OpenAI's GPT model series, neural networks capable of translating natural language instructions into code. By default, GPT-DB exploits the most recently released GPT-4 model whereas visitors may also select prior versions for comparison. GPT-DB automatically generates query-specific prompts, instructing GPT on code generation. These prompts include a description of the target database, as well as logical query plans described as natural language text, and instructions for customization. GPT-DB automatically verifies, and possibly re-generates, code using a reference database system for result comparisons. It enables users to select code samples for training, thereby increasing accuracy for future queries. The proposed demonstration showcases code generation for various queries and with varying instructions for code customization.
{"title":"Demonstrating GPT-DB: Generating Query-Specific and Customizable Code for SQL Processing with GPT-4","authors":"Immanuel Trummer","doi":"10.14778/3611540.3611630","DOIUrl":"https://doi.org/10.14778/3611540.3611630","url":null,"abstract":"GPT-DB generates code for SQL processing in general-purpose programming languages such as Python. Generated code can be freely customized using user-provided natural language instructions. This enables users, for instance, to try out specific libraries for SQL processing or to generate non-standard output while processing. GPT-DB is based on OpenAI's GPT model series, neural networks capable of translating natural language instructions into code. By default, GPT-DB exploits the most recently released GPT-4 model whereas visitors may also select prior versions for comparison. GPT-DB automatically generates query-specific prompts, instructing GPT on code generation. These prompts include a description of the target database, as well as logical query plans described as natural language text, and instructions for customization. GPT-DB automatically verifies, and possibly re-generates, code using a reference database system for result comparisons. It enables users to select code samples for training, thereby increasing accuracy for future queries. The proposed demonstration showcases code generation for various queries and with varying instructions for code customization.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"17 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":"134996884","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}
We demonstrate EQUI-VOCAL, a system that synthesizes compositional queries over videos from user feedback. EQUI-VOCAL enables users to query a video database for complex events by providing a few positive and negative examples of what they are looking for and labeling a small number of additional system-selected examples. Using those user inputs, EQUI-VOCAL synthesizes declarative queries that can then retrieve additional instances of the desired events. The demonstration makes two contributions: it introduces EQUI-VOCAL's graphical user interface and enables conference attendees to experiment with EQUI-VOCAL on a variety of queries. Both enable users to gain a better understanding of EQUI-VOCAL's query synthesis approach and to explore the impact of hyperparameters and label noise on system performance.
{"title":"EQUI-VOCAL Demonstration: Synthesizing Video Queries from User Interactions","authors":"Enhao Zhang, Maureen Daum, Dong He, Manasi Ganti, Brandon Haynes, Ranjay Krishna, Magdalena Balazinska","doi":"10.14778/3611540.3611600","DOIUrl":"https://doi.org/10.14778/3611540.3611600","url":null,"abstract":"We demonstrate EQUI-VOCAL, a system that synthesizes compositional queries over videos from user feedback. EQUI-VOCAL enables users to query a video database for complex events by providing a few positive and negative examples of what they are looking for and labeling a small number of additional system-selected examples. Using those user inputs, EQUI-VOCAL synthesizes declarative queries that can then retrieve additional instances of the desired events. The demonstration makes two contributions: it introduces EQUI-VOCAL's graphical user interface and enables conference attendees to experiment with EQUI-VOCAL on a variety of queries. Both enable users to gain a better understanding of EQUI-VOCAL's query synthesis approach and to explore the impact of hyperparameters and label noise on system performance.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"37 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":"134996891","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}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611584
Xiu Tang, Sai Wu, Dongxiang Zhang, Ziyue Wang, Gongsheng Yuan, Gang Chen
Database management systems (DBMSs) are prone to logic bugs that can result in incorrect query results. Current debugging tools are limited to single table queries and struggle with issues like lack of ground-truth results and repetitive query space exploration. In this paper, we demonstrate DLBD, a system that automatically detects logic bugs in databases. DLBD offers holistic logic bug detection by providing automatic schema and query generation and ground-truth query result retrieval. Additionally, DLBD provides minimal test cases and root cause analysis for each bug to aid developers in reproducing and fixing detected bugs. DLBD incorporates heuristics and domain-specific knowledge to efficiently prune the search space and employs query space exploration mechanisms to avoid the repetitive search. Finally, DLBD utilizes a distributed processing framework to test database logic bugs in a scalable and efficient manner. Our system offers developers a reliable and effective way to detect and fix logic bugs in DBMSs.
{"title":"A Demonstration of DLBD: Database Logic Bug Detection System","authors":"Xiu Tang, Sai Wu, Dongxiang Zhang, Ziyue Wang, Gongsheng Yuan, Gang Chen","doi":"10.14778/3611540.3611584","DOIUrl":"https://doi.org/10.14778/3611540.3611584","url":null,"abstract":"Database management systems (DBMSs) are prone to logic bugs that can result in incorrect query results. Current debugging tools are limited to single table queries and struggle with issues like lack of ground-truth results and repetitive query space exploration. In this paper, we demonstrate DLBD, a system that automatically detects logic bugs in databases. DLBD offers holistic logic bug detection by providing automatic schema and query generation and ground-truth query result retrieval. Additionally, DLBD provides minimal test cases and root cause analysis for each bug to aid developers in reproducing and fixing detected bugs. DLBD incorporates heuristics and domain-specific knowledge to efficiently prune the search space and employs query space exploration mechanisms to avoid the repetitive search. Finally, DLBD utilizes a distributed processing framework to test database logic bugs in a scalable and efficient manner. Our system offers developers a reliable and effective way to detect and fix logic bugs in DBMSs.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"72 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":"134997929","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}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611596
Tingyu Wang, Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy
Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? We propose to demonstrate DPXPlain, the first system for explaining group-by aggregate query answers with DP. DPXPlain allows users to compare values of two groups and receive a validity check, and further provides an explanation table with an interactive visualization, containing the approximately 'top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps.
{"title":"Explaining Differentially Private Query Results with DPXPlain","authors":"Tingyu Wang, Yuchao Tao, Amir Gilad, Ashwin Machanavajjhala, Sudeepa Roy","doi":"10.14778/3611540.3611596","DOIUrl":"https://doi.org/10.14778/3611540.3611596","url":null,"abstract":"Employing Differential Privacy (DP), the state-of-the-art privacy standard, to answer aggregate database queries poses new challenges for users to understand the trends and anomalies observed in the query results: Is the unexpected answer due to the data itself, or is it due to the extra noise that must be added to preserve DP? We propose to demonstrate DPXPlain, the first system for explaining group-by aggregate query answers with DP. DPXPlain allows users to compare values of two groups and receive a validity check, and further provides an explanation table with an interactive visualization, containing the approximately 'top-k' explanation predicates along with their relative influences and ranks in the form of confidence intervals, while guaranteeing DP in all steps.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"36 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":"134998133","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}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611628
Ibrahim Abdelaziz, Julian Dolby, Udayan Khurana, Horst Samulowitz, Kavitha Srinivas
Data preparation is a crucial first step to any data analysis problem. This task is largely manual, performed by a person familiar with the data domain. DataRinse is a system designed to extract relevant transforms from large scale static analysis of repositories of code. Our motivation is that in any large enterprise, multiple personas such as data engineers and data scientists work on similar datasets. However, sharing or re-using that code is not obvious and difficult to execute. In this paper, we demonstrate DataRinse to handle data preparation, such that the system recommends code designed to help with the preparation of a column for data analysis more generally. We show that DataRinse does not simply shard expressions observed in code but also uses analysis to group expressions applied to the same field such that related transforms appear coherently to a user. It is a human-in-the-loop system where the users select relevant code snippets produced by DataRinse to apply on their dataset.
{"title":"DataRinse: Semantic Transforms for Data Preparation Based on Code Mining","authors":"Ibrahim Abdelaziz, Julian Dolby, Udayan Khurana, Horst Samulowitz, Kavitha Srinivas","doi":"10.14778/3611540.3611628","DOIUrl":"https://doi.org/10.14778/3611540.3611628","url":null,"abstract":"Data preparation is a crucial first step to any data analysis problem. This task is largely manual, performed by a person familiar with the data domain. DataRinse is a system designed to extract relevant transforms from large scale static analysis of repositories of code. Our motivation is that in any large enterprise, multiple personas such as data engineers and data scientists work on similar datasets. However, sharing or re-using that code is not obvious and difficult to execute. In this paper, we demonstrate DataRinse to handle data preparation, such that the system recommends code designed to help with the preparation of a column for data analysis more generally. We show that DataRinse does not simply shard expressions observed in code but also uses analysis to group expressions applied to the same field such that related transforms appear coherently to a user. It is a human-in-the-loop system where the users select relevant code snippets produced by DataRinse to apply on their dataset.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"42 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":"134998289","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}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611599
Hussein Abdallah, Waleed Afandi, Essam Mansour
This demo paper presents KGNet, a graph machine learning-enabled RDF engine. KGNet integrates graph machine learning (GML) models with existing RDF engines as query operators to support node classification and link prediction tasks. For easy integration, KGNet extends the SPARQL language with user-defined predicates to support the GML operators. We refer to this extension as SPARQL ML query. Our SPARQL ML query optimizer is in charge of optimizing the selection of the near-optimal GML models. The development of KGNet poses research opportunities in various areas spanning KG management. In the paper, we demonstrate the ease of integration between the RDF engines and GML models through the SPARQL ML inference query language. We present several real use cases of different GML tasks on real KGs. Using KGNet, users do not need to learn a new scripting language or have a deep understanding of GML methods. The audience will experience KGNet with different KGs and GML models, as shown in our demo video and Colab notebook.
{"title":"Demonstration of SPARQL <sup> <i>ML</i> </sup> : An Interfacing Language for Supporting Graph Machine Learning for RDF Graphs","authors":"Hussein Abdallah, Waleed Afandi, Essam Mansour","doi":"10.14778/3611540.3611599","DOIUrl":"https://doi.org/10.14778/3611540.3611599","url":null,"abstract":"This demo paper presents KGNet, a graph machine learning-enabled RDF engine. KGNet integrates graph machine learning (GML) models with existing RDF engines as query operators to support node classification and link prediction tasks. For easy integration, KGNet extends the SPARQL language with user-defined predicates to support the GML operators. We refer to this extension as SPARQL ML query. Our SPARQL ML query optimizer is in charge of optimizing the selection of the near-optimal GML models. The development of KGNet poses research opportunities in various areas spanning KG management. In the paper, we demonstrate the ease of integration between the RDF engines and GML models through the SPARQL ML inference query language. We present several real use cases of different GML tasks on real KGs. Using KGNet, users do not need to learn a new scripting language or have a deep understanding of GML methods. The audience will experience KGNet with different KGs and GML models, as shown in our demo video and Colab notebook.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"1 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":"134998294","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}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611590
Ramon Antonio Rodriges Zalipynis
Array DBMSs operate on N -d arrays. During the Data Ingestion phase, the widely used mosaic operator ingests a massive collection of overlapping arrays into a single large array, called mosaic. The operator can utilize sophisticated statistical and machine learning techniques, e.g. Canonical Correlation Analysis (CCA), to produce a high quality seamless mosaic where the contrasts between the values of cells taken from input overlapping arrays are minimized. However, the performance bottleneck becomes a major challenge when applying such advanced techniques over increasingly growing array volumes. We introduce a new, scalable way to perform CCA that is orders of magnitude faster than the popular Python's scikit-learn library for the purpose of array mosaicking. Furthermore, we developed a hybrid web-desktop application to showcase our novel FastMosaic operator, based on this new CCA. A rich GUI enables users to comprehensively investigate in/out arrays, interactively guides through an end-to-end mosaic construction on real-world geospatial arrays using FastMosaic, facilitating a convenient exploration of the FastMosaic pipeline and its internals.
{"title":"FastMosaic in Action: A New Mosaic Operator for Array DBMSs","authors":"Ramon Antonio Rodriges Zalipynis","doi":"10.14778/3611540.3611590","DOIUrl":"https://doi.org/10.14778/3611540.3611590","url":null,"abstract":"Array DBMSs operate on N -d arrays. During the Data Ingestion phase, the widely used mosaic operator ingests a massive collection of overlapping arrays into a single large array, called mosaic. The operator can utilize sophisticated statistical and machine learning techniques, e.g. Canonical Correlation Analysis (CCA), to produce a high quality seamless mosaic where the contrasts between the values of cells taken from input overlapping arrays are minimized. However, the performance bottleneck becomes a major challenge when applying such advanced techniques over increasingly growing array volumes. We introduce a new, scalable way to perform CCA that is orders of magnitude faster than the popular Python's scikit-learn library for the purpose of array mosaicking. Furthermore, we developed a hybrid web-desktop application to showcase our novel FastMosaic operator, based on this new CCA. A rich GUI enables users to comprehensively investigate in/out arrays, interactively guides through an end-to-end mosaic construction on real-world geospatial arrays using FastMosaic, facilitating a convenient exploration of the FastMosaic pipeline and its internals.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"36 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":"134998295","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}
Pub Date : 2023-08-01DOI: 10.14778/3611540.3611557
Jiaqi Wang, Tianyi Li, Anni Wang, Xiaoze Liu, Lu Chen, Jie Chen, Jianye Liu, Junyang Wu, Feifei Li, Yunjun Gao
Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing workload pattern discovery systems are not suitable for large-scale cloud databases which are commonly employed by the industry. This is because the workload patterns of large-scale cloud databases are generally far more complicated than those of ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a real-time system for discovering workload patterns in complicated large-scale workloads. AW M encodes and discovers the SQL query patterns logged from user requests and optimizes the querying processing based on the discovered patterns. First, Data Collection & Preprocessing Module collects streaming query logs and encodes them into high-dimensional feature embeddings with rich semantic contexts and execution features. Next, Online Workload Mining Module separates encoded query by business groups and discovers the workload patterns for each group. Meanwhile, Offline Training Module collects labels and trains the classification model using the labels. Finally, Pattern-based Optimizing Module optimizes query processing in cloud databases by exploiting discovered patterns. Extensive experimental results on one synthetic dataset and two real-life datasets (extracted from Alibaba Cloud databases) show that AW M enhances the accuracy of pattern discovery by 66% and reduce the latency of online inference by 22%, compared with the state-of-the-arts.
{"title":"Real-Time Workload Pattern Analysis for Large-Scale Cloud Databases","authors":"Jiaqi Wang, Tianyi Li, Anni Wang, Xiaoze Liu, Lu Chen, Jie Chen, Jianye Liu, Junyang Wu, Feifei Li, Yunjun Gao","doi":"10.14778/3611540.3611557","DOIUrl":"https://doi.org/10.14778/3611540.3611557","url":null,"abstract":"Hosting database services on cloud systems has become a common practice. This has led to the increasing volume of database workloads, which provides the opportunity for pattern analysis. Discovering workload patterns from a business logic perspective is conducive to better understanding the trends and characteristics of the database system. However, existing workload pattern discovery systems are not suitable for large-scale cloud databases which are commonly employed by the industry. This is because the workload patterns of large-scale cloud databases are generally far more complicated than those of ordinary databases. In this paper, we propose Alibaba Workload Miner (AWM), a real-time system for discovering workload patterns in complicated large-scale workloads. AW M encodes and discovers the SQL query patterns logged from user requests and optimizes the querying processing based on the discovered patterns. First, Data Collection & Preprocessing Module collects streaming query logs and encodes them into high-dimensional feature embeddings with rich semantic contexts and execution features. Next, Online Workload Mining Module separates encoded query by business groups and discovers the workload patterns for each group. Meanwhile, Offline Training Module collects labels and trains the classification model using the labels. Finally, Pattern-based Optimizing Module optimizes query processing in cloud databases by exploiting discovered patterns. Extensive experimental results on one synthetic dataset and two real-life datasets (extracted from Alibaba Cloud databases) show that AW M enhances the accuracy of pattern discovery by 66% and reduce the latency of online inference by 22%, compared with the state-of-the-arts.","PeriodicalId":54220,"journal":{"name":"Proceedings of the Vldb Endowment","volume":"35 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":"135002983","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}
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