Proceedings of the 2016 International Conference on Management of Data最新文献_第7页

DBSherlock: A Performance Diagnostic Tool for Transactional Databases DBSherlock:事务性数据库的性能诊断工具

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2915218

Dong Young Yoon, Ning Niu, Barzan Mozafari

{"title":"DBSherlock: A Performance Diagnostic Tool for Transactional Databases","authors":"Dong Young Yoon, Ning Niu, Barzan Mozafari","doi":"10.1145/2882903.2915218","DOIUrl":"https://doi.org/10.1145/2882903.2915218","url":null,"abstract":"Running an online transaction processing (OLTP) system is one of the most daunting tasks required of database administrators (DBAs). As businesses rely on OLTP databases to support their mission-critical and real-time applications, poor database performance directly impacts their revenue and user experience. As a result, DBAs constantly monitor, diagnose, and rectify any performance decays. Unfortunately, the manual process of debugging and diagnosing OLTP performance problems is extremely tedious and non-trivial. Rather than being caused by a single slow query, performance problems in OLTP databases are often due to a large number of concurrent and competing transactions adding up to compounded, non-linear effects that are difficult to isolate. Sudden changes in request volume, transactional patterns, network traffic, or data distribution can cause previously abundant resources to become scarce, and the performance to plummet. This paper presents a practical tool for assisting DBAs in quickly and reliably diagnosing performance problems in an OLTP database. By analyzing hundreds of statistics and configurations collected over the lifetime of the system, our algorithm quickly identifies a small set of potential causes and presents them to the DBA. The root-cause established by the DBA is reincorporated into our algorithm as a new causal model to improve future diagnoses. Our experiments show that this algorithm is substantially more accurate than the state-of-the-art algorithm in finding correct explanations.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"196 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79882225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 65

Top-k Relevant Semantic Place Retrieval on Spatial RDF Data 空间RDF数据Top-k相关语义位置检索

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2882941

Jieming Shi, Dingming Wu, N. Mamoulis

{"title":"Top-k Relevant Semantic Place Retrieval on Spatial RDF Data","authors":"Jieming Shi, Dingming Wu, N. Mamoulis","doi":"10.1145/2882903.2882941","DOIUrl":"https://doi.org/10.1145/2882903.2882941","url":null,"abstract":"RDF data are traditionally accessed using structured query languages, such as SPARQL. However, this requires users to understand the language as well as the RDF schema. Keyword search on RDF data aims at relieving the user from these requirements; the user only inputs a set of keywords and the goal is to find small RDF subgraphs which contain all keywords. At the same time, popular RDF knowledge bases also include spatial semantics, which opens the road to location-based search operations. In this work, we propose and study a novel location-based keyword search query on RDF data. The objective of top-k relevant semantic places (kSP) retrieval is to find RDF subgraphs which contain the query keywords and are rooted at spatial entities close to the query location. The novelty of kSP queries is that they are location-aware and that they do not rely on the use of structured query languages. We design a basic method for the processing of kSP queries. To further accelerate kSP retrieval, two pruning approaches and a data preprocessing technique are proposed. Extensive empirical studies on two real datasets demonstrate the superior and robust performance of our proposals compared to the basic method.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84330807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 27

Adaptive Data Skipping in Main-Memory Systems 主存系统中的自适应数据跳变

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2914836

Wilson Qin, Stratos Idreos

引用次数: 17

Automated Demand-driven Resource Scaling in Relational Database-as-a-Service 关系数据库即服务中自动化需求驱动的资源扩展

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2903733

Sudipto Das, Feng Li, Vivek R. Narasayya, A. König

{"title":"Automated Demand-driven Resource Scaling in Relational Database-as-a-Service","authors":"Sudipto Das, Feng Li, Vivek R. Narasayya, A. König","doi":"10.1145/2882903.2903733","DOIUrl":"https://doi.org/10.1145/2882903.2903733","url":null,"abstract":"Relational Database-as-a-Service (DaaS) platforms today support the abstraction of a resource container that guarantees a fixed amount of resources. Tenants are responsible for selecting a container size suitable for their workloads, which they can change to leverage the cloud's elasticity. However, automating this task is daunting for most tenants since estimating resource demands for arbitrary SQL workloads in an RDBMS is complex and challenging. In addition, workloads and resource requirements can vary significantly within minutes to hours, and container sizes vary by orders of magnitude both in the amount of resources as well as monetary cost. We present a solution to enable a DaaS to auto-scale container sizes on behalf of its tenants. Approaches to auto-scale stateless services, such as web servers, that rely on historical resource utilization as the primary signal, often perform poorly for stateful database servers which are significantly more complex. Our solution derives a set of robust signals from database engine telemetry and combines them to significantly improve accuracy of demand estimation for database workloads resulting in more accurate scaling decisions. Our solution raises the abstraction by allowing tenants to reason about monetary budget and query latency rather than resources. We prototyped our approach in Microsoft Azure SQL Database and ran extensive experiments using workloads with realistic time-varying resource demand patterns obtained from production traces. Compared to an approach that uses only resource utilization to estimate demand, our approach results in 1.5x to 3x lower monetary costs while achieving comparable query latencies.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85170142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 54

Robust Query Processing in Co-Processor-accelerated Databases 协处理器加速数据库中的鲁棒查询处理

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2882936

S. Breß, Henning Funke, J. Teubner

{"title":"Robust Query Processing in Co-Processor-accelerated Databases","authors":"S. Breß, Henning Funke, J. Teubner","doi":"10.1145/2882903.2882936","DOIUrl":"https://doi.org/10.1145/2882903.2882936","url":null,"abstract":"Technology limitations are making the use of heterogeneous computing devices much more than an academic curiosity. In fact, the use of such devices is widely acknowledged to be the only promising way to achieve application-speedups that users urgently need and expect. However, building a robust and efficient query engine for heterogeneous co-processor environments is still a significant challenge. In this paper, we identify two effects that limit performance in case co-processor resources become scarce. Cache thrashing occurs when the working set of queries does not fit into the co-processor's data cache, resulting in performance degradations up to a factor of 24. Heap contention occurs when multiple operators run in parallel on a co-processor and when their accumulated memory footprint exceeds the main memory capacity of the co-processor, slowing down query execution by up to a factor of six. We propose solutions for both effects. Data-driven operator placement avoids data movements when they might be harmful; query chopping limits co-processor memory usage and thus avoids contention. The combined approach-data-driven query chopping-achieves robust and scalable performance on co-processors. We validate our proposal with our open-source GPU-accelerated database engine CoGaDB and the popular star schema and TPC-H benchmarks.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81810565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 58

High-Performance Geospatial Analytics in HyPerSpace 超空间中的高性能地理空间分析

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2899412

Varun Pandey, Andreas Kipf, Dimitri Vorona, Tobias Mühlbauer, Thomas Neumann, A. Kemper

{"title":"High-Performance Geospatial Analytics in HyPerSpace","authors":"Varun Pandey, Andreas Kipf, Dimitri Vorona, Tobias Mühlbauer, Thomas Neumann, A. Kemper","doi":"10.1145/2882903.2899412","DOIUrl":"https://doi.org/10.1145/2882903.2899412","url":null,"abstract":"In the past few years, massive amounts of location-based data has been captured. Numerous datasets containing user location information are readily available to the public. Analyzing such datasets can lead to fascinating insights into the mobility patterns and behaviors of users. Moreover, in recent times a number of geospatial data-driven companies like Uber, Lyft, and Foursquare have emerged. Real-time analysis of geospatial data is essential and enables an emerging class of applications. Database support for geospatial operations is turning into a necessity instead of a distinct feature provided by only a few databases. Even though a lot of database systems provide geospatial support nowadays, queries often do not consider the most current database state. Geospatial queries are inherently slow given the fact that some of these queries require a couple of geometric computations. Disk-based database systems that do support geospatial datatypes and queries, provide rich features and functions, but they fall behind when performance is considered: specifically if real-time analysis of the latest transactional state is a requirement. In this demonstration, we present HyPerSpace, an extension to the high-performance main-memory database system HyPer developed at the Technical University of Munich, capable of processing geospatial queries with sub-second latencies.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88662807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Optimization of Nested Queries using the NF2 Algebra 使用NF2代数优化嵌套查询

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2915241

Jürgen Hölsch, Michael Grossniklaus, M. Scholl

引用次数: 9

FERARI: A Prototype for Complex Event Processing over Streaming Multi-cloud Platforms FERARI:流多云平台上复杂事件处理的原型

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2899395

Ioannis Flouris, Vasiliki Manikaki, Nikos Giatrakos, Antonios Deligiannakis, M. Garofalakis, M. Mock, Sebastian Bothe, Inna Skarbovsky, Fabiana Fournier, Marko Stajcer, Tomislav Krizan, Jonathan Yom-Tov, Taji Curin

引用次数: 23

Constance: An Intelligent Data Lake System 康斯坦斯:智能数据湖系统

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2899389

Rihan Hai, Sandra Geisler, C. Quix

{"title":"Constance: An Intelligent Data Lake System","authors":"Rihan Hai, Sandra Geisler, C. Quix","doi":"10.1145/2882903.2899389","DOIUrl":"https://doi.org/10.1145/2882903.2899389","url":null,"abstract":"As the challenge of our time, Big Data still has many research hassles, especially the variety of data. The high diversity of data sources often results in information silos, a collection of non-integrated data management systems with heterogeneous schemas, query languages, and APIs. Data Lake systems have been proposed as a solution to this problem, by providing a schema-less repository for raw data with a common access interface. However, just dumping all data into a data lake without any metadata management, would only lead to a 'data swamp'. To avoid this, we propose Constance, a Data Lake system with sophisticated metadata management over raw data extracted from heterogeneous data sources. Constance discovers, extracts, and summarizes the structural metadata from the data sources, and annotates data and metadata with semantic information to avoid ambiguities. With embedded query rewriting engines supporting structured data and semi-structured data, Constance provides users a unified interface for query processing and data exploration. During the demo, we will walk through each functional component of Constance. Constance will be applied to two real-life use cases in order to show attendees the importance and usefulness of our generic and extensible data lake system.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79424998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 199

Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration 使用图形处理单元实现具有BLU加速的DB2快速查询处理的混合设计:技术演示

Proceedings of the 2016 International Conference on Management of Data Pub Date : 2016-06-26 DOI: 10.1145/2882903.2903735

S. Meraji, Berni Schiefer, Lan Pham, Lee Chu, Peter Kokosielis, Adam J. Storm, Wayne Young, Chang Ge, Geoffrey Ng, Kajan Kanagaratnam

{"title":"Towards a Hybrid Design for Fast Query Processing in DB2 with BLU Acceleration Using Graphical Processing Units: A Technology Demonstration","authors":"S. Meraji, Berni Schiefer, Lan Pham, Lee Chu, Peter Kokosielis, Adam J. Storm, Wayne Young, Chang Ge, Geoffrey Ng, Kajan Kanagaratnam","doi":"10.1145/2882903.2903735","DOIUrl":"https://doi.org/10.1145/2882903.2903735","url":null,"abstract":"In this paper, we show how we use Nvidia GPUs and host CPU cores for faster query processing in a DB2 database using BLU Acceleration (DB2's column store technology). Moreover, we show the benefits and problems of using hardware accelerators (more specifically GPUs) in a real commercial Relational Database Management System(RDBMS).We investigate the effect of off-loading specific database operations to a GPU, and show how doing so results in a significant performance improvement. We then demonstrate that for some queries, using just CPU to perform the entire operation is more beneficial. While we use some of Nvidia's fast kernels for operations like sort, we have also developed our own high performance kernels for operations such as group by and aggregation. Finally, we show how we use a dynamic design that can make use of optimizer metadata to intelligently choose a GPU kernel to run. For the first time in the literature, we use benchmarks representative of customer environments to gauge the performance of our prototype, the results of which show that we can get a speed increase upwards of 2x, using a realistic set of queries.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90517921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9