Zhaoguo Wang, Shuai Mu, Yang Cui, Han Yi, Haibo Chen, Jinyang Li
Multicore in-memory databases often rely on traditional con- currency control schemes such as two-phase-locking (2PL) or optimistic concurrency control (OCC). Unfortunately, when the workload exhibits a non-trivial amount of contention, both 2PL and OCC sacrifice much parallel execution op- portunity. In this paper, we describe a new concurrency control scheme, interleaving constrained concurrency con- trol (IC3), which provides serializability while allowing for parallel execution of certain conflicting transactions. IC3 combines the static analysis of the transaction workload with runtime techniques that track and enforce dependencies among concurrent transactions. The use of static analysis simplifies IC3's runtime design, allowing it to scale to many cores. Evaluations on a 64-core machine using the TPC- C benchmark show that IC3 outperforms traditional con- currency control schemes under contention. It achieves the throughput of 434K transactions/sec on the TPC-C bench- mark configured with only one warehouse. It also scales better than several recent concurrent control schemes that also target contended workloads.
{"title":"Scaling Multicore Databases via Constrained Parallel Execution","authors":"Zhaoguo Wang, Shuai Mu, Yang Cui, Han Yi, Haibo Chen, Jinyang Li","doi":"10.1145/2882903.2882934","DOIUrl":"https://doi.org/10.1145/2882903.2882934","url":null,"abstract":"Multicore in-memory databases often rely on traditional con- currency control schemes such as two-phase-locking (2PL) or optimistic concurrency control (OCC). Unfortunately, when the workload exhibits a non-trivial amount of contention, both 2PL and OCC sacrifice much parallel execution op- portunity. In this paper, we describe a new concurrency control scheme, interleaving constrained concurrency con- trol (IC3), which provides serializability while allowing for parallel execution of certain conflicting transactions. IC3 combines the static analysis of the transaction workload with runtime techniques that track and enforce dependencies among concurrent transactions. The use of static analysis simplifies IC3's runtime design, allowing it to scale to many cores. Evaluations on a 64-core machine using the TPC- C benchmark show that IC3 outperforms traditional con- currency control schemes under contention. It achieves the throughput of 434K transactions/sec on the TPC-C bench- mark configured with only one warehouse. It also scales better than several recent concurrent control schemes that also target contended workloads.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79574668","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}
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.
{"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}
Sudipto Das, Feng Li, Vivek R. Narasayya, A. König
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.
{"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}
A key promise of SQL is that the optimizer will find the most efficient execution plan, regardless of how the query is formulated. In general, query optimizers of modern database systems are able to keep this promise, with the notable exception of nested queries. While several optimization techniques for nested queries have been proposed, their adoption in practice has been limited. In this paper, we argue that the NF2 (non-first normal form) algebra, which was originally designed to process nested tables, is a better approach to nested query optimization as it fulfills two key requirements. First, the NF2 algebra can represent all types of nested queries as well as both existing and novel optimization techniques based on its equivalences. Second, performance benefits can be achieved with little changes to existing transformation-based query optimizers as the NF2 algebra is an extension of the relational algebra.
{"title":"Optimization of Nested Queries using the NF2 Algebra","authors":"Jürgen Hölsch, Michael Grossniklaus, M. Scholl","doi":"10.1145/2882903.2915241","DOIUrl":"https://doi.org/10.1145/2882903.2915241","url":null,"abstract":"A key promise of SQL is that the optimizer will find the most efficient execution plan, regardless of how the query is formulated. In general, query optimizers of modern database systems are able to keep this promise, with the notable exception of nested queries. While several optimization techniques for nested queries have been proposed, their adoption in practice has been limited. In this paper, we argue that the NF2 (non-first normal form) algebra, which was originally designed to process nested tables, is a better approach to nested query optimization as it fulfills two key requirements. First, the NF2 algebra can represent all types of nested queries as well as both existing and novel optimization techniques based on its equivalences. Second, performance benefits can be achieved with little changes to existing transformation-based query optimizers as the NF2 algebra is an extension of the relational algebra.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90971329","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}
Joins are expensive, and online aggregation over joins was proposed to mitigate the cost, which offers a nice and flexible tradeoff between query efficiency and accuracy in a continuous, online fashion. However, the state-of-the-art approach, in both internal and external memory, is based on ripple join, which is still very expensive and may also need very restrictive assumptions (e.g., tuples in a table are stored in random order). We introduce a new approach, wander join, to the online aggregation problem by performing random walks over the underlying join graph. We have also implemented and tested wander join in the latest PostgreSQL.
{"title":"Wander Join: Online Aggregation for Joins","authors":"Feifei Li, Bin Wu, K. Yi, Zhuoyue Zhao","doi":"10.1145/2882903.2899413","DOIUrl":"https://doi.org/10.1145/2882903.2899413","url":null,"abstract":"Joins are expensive, and online aggregation over joins was proposed to mitigate the cost, which offers a nice and flexible tradeoff between query efficiency and accuracy in a continuous, online fashion. However, the state-of-the-art approach, in both internal and external memory, is based on ripple join, which is still very expensive and may also need very restrictive assumptions (e.g., tuples in a table are stored in random order). We introduce a new approach, wander join, to the online aggregation problem by performing random walks over the underlying join graph. We have also implemented and tested wander join in the latest PostgreSQL.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73081134","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}
Due to the "curse of dimensionality" problem, it is very expensive to process the nearest neighbor (NN) query in high-dimensional spaces; and hence, approximate approaches, such as Locality-Sensitive Hashing (LSH), are widely used for their theoretical guarantees and empirical performance. Current LSH-based approaches target at the L1 and L2 spaces, while as shown in previous work, the fractional distance metrics (Lp metrics with 0 < p < 1) can provide more insightful results than the usual L1 and L2 metrics for data mining and multimedia applications. However, none of the existing work can support multiple fractional distance metrics using one index. In this paper, we propose LazyLSH that answers approximate nearest neighbor queries for multiple Lp metrics with theoretical guarantees. Different from previous LSH approaches which need to build one dedicated index for every query space, LazyLSH uses a single base index to support the computations in multiple Lp spaces, significantly reducing the maintenance overhead. Extensive experiments show that LazyLSH provides more accurate results for approximate kNN search under fractional distance metrics.
由于“维数诅咒”问题，在高维空间中处理最近邻(NN)查询是非常昂贵的;因此，近似方法，如位置敏感哈希(LSH)，因其理论保证和经验性能而被广泛使用。当前基于lsh的方法针对L1和L2空间，而正如之前的工作所示，对于数据挖掘和多媒体应用，分数距离度量(0 < p < 1的Lp度量)可以提供比通常的L1和L2度量更有洞察力的结果。然而，现有的工作都不能支持使用一个索引的多个分数距离度量。在本文中，我们提出了一个具有理论保证的回答多个Lp指标的近似最近邻查询的LazyLSH。与以前需要为每个查询空间构建一个专用索引的LSH方法不同，LazyLSH使用单个基本索引来支持多个Lp空间中的计算，从而大大降低了维护开销。大量的实验表明，在分数距离度量下，LazyLSH提供了更准确的近似kNN搜索结果。
{"title":"LazyLSH: Approximate Nearest Neighbor Search for Multiple Distance Functions with a Single Index","authors":"Yuxin Zheng, Qi Guo, A. Tung, Sai Wu","doi":"10.1145/2882903.2882930","DOIUrl":"https://doi.org/10.1145/2882903.2882930","url":null,"abstract":"Due to the \"curse of dimensionality\" problem, it is very expensive to process the nearest neighbor (NN) query in high-dimensional spaces; and hence, approximate approaches, such as Locality-Sensitive Hashing (LSH), are widely used for their theoretical guarantees and empirical performance. Current LSH-based approaches target at the L1 and L2 spaces, while as shown in previous work, the fractional distance metrics (Lp metrics with 0 < p < 1) can provide more insightful results than the usual L1 and L2 metrics for data mining and multimedia applications. However, none of the existing work can support multiple fractional distance metrics using one index. In this paper, we propose LazyLSH that answers approximate nearest neighbor queries for multiple Lp metrics with theoretical guarantees. Different from previous LSH approaches which need to build one dedicated index for every query space, LazyLSH uses a single base index to support the computations in multiple Lp spaces, significantly reducing the maintenance overhead. Extensive experiments show that LazyLSH provides more accurate results for approximate kNN search under fractional distance metrics.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"60 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90201548","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}
We propose the 3c-index that measures the influence degree of researchers by evaluating the links they establish between communities. We evaluate its performance against well known metrics. The results show 3c-index outperforms them in most cases and can be employed as a complementary metric to assess researchers' productivity.
{"title":"Research Contribution as a Measure of Influence","authors":"Lais M. A. Rocha, Mirella M. Moro","doi":"10.1145/2882903.2914834","DOIUrl":"https://doi.org/10.1145/2882903.2914834","url":null,"abstract":"We propose the 3c-index that measures the influence degree of researchers by evaluating the links they establish between communities. We evaluate its performance against well known metrics. The results show 3c-index outperforms them in most cases and can be employed as a complementary metric to assess researchers' productivity.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90325975","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}
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
In this demo, we present FERARI, a prototype that enables real-time Complex Event Processing (CEP) for large volume event data streams over distributed topologies. Our prototype constitutes, to our knowledge, the first complete, multi-cloud based end-to-end CEP solution incorporating: a) a user-friendly, web-based query authoring tool, (b) a powerful CEP engine implemented on top of a streaming cloud platform, (c) a CEP optimizer that chooses the best query execution plan with respect to low latency and/or reduced inter-cloud communication burden, and (d) a query analytics dashboard encompassing graph and map visualization tools to provide a holistic picture with respect to the detected complex events to final stakeholders. As a proof-of-concept, we apply FERARI to enable mobile fraud detection over real, properly anonymized, telecommunication data from T-Hrvatski Telekom network in Croatia.
{"title":"FERARI: A Prototype for Complex Event Processing over Streaming Multi-cloud Platforms","authors":"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","doi":"10.1145/2882903.2899395","DOIUrl":"https://doi.org/10.1145/2882903.2899395","url":null,"abstract":"In this demo, we present FERARI, a prototype that enables real-time Complex Event Processing (CEP) for large volume event data streams over distributed topologies. Our prototype constitutes, to our knowledge, the first complete, multi-cloud based end-to-end CEP solution incorporating: a) a user-friendly, web-based query authoring tool, (b) a powerful CEP engine implemented on top of a streaming cloud platform, (c) a CEP optimizer that chooses the best query execution plan with respect to low latency and/or reduced inter-cloud communication burden, and (d) a query analytics dashboard encompassing graph and map visualization tools to provide a holistic picture with respect to the detected complex events to final stakeholders. As a proof-of-concept, we apply FERARI to enable mobile fraud detection over real, properly anonymized, telecommunication data from T-Hrvatski Telekom network in Croatia.","PeriodicalId":20483,"journal":{"name":"Proceedings of the 2016 International Conference on Management of Data","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2016-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74663280","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}
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.
{"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}
S. Meraji, Berni Schiefer, Lan Pham, Lee Chu, Peter Kokosielis, Adam J. Storm, Wayne Young, Chang Ge, Geoffrey Ng, Kajan Kanagaratnam
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.
{"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}
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