Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics最新文献

Cost-based Memory Partitioning and Management in Memcached Memcached中基于成本的内存分区和管理

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 2015-08-31 DOI: 10.1145/2803140.2803146

D. Carra, P. Michiardi

引用次数: 2

Gaussian Mixture Models Use-Case: In-Memory Analysis with Myria 高斯混合模型用例:内存分析与Myria

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 2015-08-31 DOI: 10.1145/2803140.2803143

R. Maas, Jeremy Hyrkas, O. Telford, M. Balazinska, A. Connolly, Bill Howe

引用次数: 9

Query Optimization Time: The New Bottleneck in Real-time Analytics 查询优化时间:实时分析的新瓶颈

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 2015-08-31 DOI: 10.1145/2803140.2803148

Rajkumar Sen, Jack Chen, Nika Jimsheleishvilli

{"title":"Query Optimization Time: The New Bottleneck in Real-time Analytics","authors":"Rajkumar Sen, Jack Chen, Nika Jimsheleishvilli","doi":"10.1145/2803140.2803148","DOIUrl":"https://doi.org/10.1145/2803140.2803148","url":null,"abstract":"In the recent past, in-memory distributed database management systems have become increasingly popular to manage and query huge amounts of data. For an in-memory distributed database like MemSQL, it is imperative that the analytical queries run fast. A huge proportion of MemSQL's customer workloads have ad-hoc analytical queries that need to finish execution within a second or a few seconds. This leaves us with very little time to perform query optimization for complex queries involving several joins, aggregations, sub-queries etc. Even for queries that are not ad-hoc, a change in data statistics can trigger query re-optimization. Query Optimization, if not done intelligently, could very well be the bottleneck for such complex analytical queries that require real-time response. In this paper, we outline some of the early steps that we have taken to reduce the query optimization time without sacrificing plan quality. We optimized the Enumerator (the optimizer component that determines operator order), which takes up bulk of the optimization time. Generating bushy plans inside the Enumerator can be a bottleneck and so we used heuristics to generate bushy plans via query rewrite. We also implemented new distribution aware greedy heuristics to generate a good starting candidate plan that significantly prunes out states during search space analysis inside the Enumerator. We demonstrate the effectiveness of these techniques over several queries in TPC-H and TPC-DS benchmarks.","PeriodicalId":175654,"journal":{"name":"Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131379802","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: 6

NVC-Hashmap: A Persistent and Concurrent Hashmap For Non-Volatile Memories NVC-Hashmap:用于非易失性内存的持久和并发Hashmap

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 2015-08-31 DOI: 10.1145/2803140.2803144

David Schwalb, Markus Dreseler, M. Uflacker, H. Plattner

引用次数: 51

Write Amplification: An Analysis of In-Memory Database Durability Techniques 写放大:内存数据库持久性技术的分析

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 2015-08-31 DOI: 10.1145/2803140.2803141

Jaemyung Kim, K. Salem, Khuzaima S. Daudjee

引用次数: 4

Partitioned Bit-Packed Vectors for In-Memory-Column-Stores 用于内存列存储的分区位打包向量

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 2015-08-31 DOI: 10.1145/2803140.2803142

Martin Faust, Pedro Flemming, David Schwalb, H. Plattner

{"title":"Partitioned Bit-Packed Vectors for In-Memory-Column-Stores","authors":"Martin Faust, Pedro Flemming, David Schwalb, H. Plattner","doi":"10.1145/2803140.2803142","DOIUrl":"https://doi.org/10.1145/2803140.2803142","url":null,"abstract":"In recent database development, in-memory databases have grown more and more in popularity. The hardware development of the past years has made it possible to keep even larger data sets entirely in main memory of one or a few machines. However, most applications on in-memory databases are memory-latency-bound rather than compute-bound. Combining strong compression techniques and efficient data structures is essential to fully utilize the hardware capabilities. A common data structure for efficient storing is the bit-packed vector. The bit-packed vector uses a fixed encoding length, which cannot be changed after initialization. Therefore it requires full re-initialization, when the encoding-length changes. In this paper we propose a new data structure, the partitioned bit-packed vector. Therein the encoding length of the stored elements may increase dynamically, while still providing comparable single-value access performance. This paper outlines the access to this data structure and evaluates its performance characteristics. The results suggest that the partitioned bitvector has the capabilities to improve the performance of existing in-memory column-stores for typical enterprise workloads.","PeriodicalId":175654,"journal":{"name":"Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics","volume":"98 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114094396","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: 0

Hyrise-R: Scale-out and Hot-Standby through Lazy Master Replication for Enterprise Applications Hyrise-R:通过企业应用程序的延迟主复制进行横向扩展和热备

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 2015-08-31 DOI: 10.1145/2803140.2803147

David Schwalb, Jan Kossmann, Martin Faust, Stefan Klauck, M. Uflacker, H. Plattner

{"title":"Hyrise-R: Scale-out and Hot-Standby through Lazy Master Replication for Enterprise Applications","authors":"David Schwalb, Jan Kossmann, Martin Faust, Stefan Klauck, M. Uflacker, H. Plattner","doi":"10.1145/2803140.2803147","DOIUrl":"https://doi.org/10.1145/2803140.2803147","url":null,"abstract":"In-memory database systems are well-suited for enterprise workloads, consisting of transactional and analytical queries. A growing number of users and an increasing demand for enterprise applications can saturate or even overload single-node database systems at peak times. Better performance can be achieved by improving a single machine's hardware but it is often cheaper and more practicable to follow a scale-out approach and replicate data by using additional machines. In this paper we present Hyrise-R, a lazy master replication system for the in-memory database Hyrise. By setting up a snapshot-based Hyrise cluster, we increase both performance by distributing queries over multiple instances and availability by utilizing the redundancy of the cluster structure. This paper describes the architecture of Hyrise-R and details of the implemented replication mechanisms. We set up Hyrise-R on instances of Amazon's Elastic Compute Cloud and present a detailed performance evaluation of our system, including a linear query throughput increase for enterprise workloads.","PeriodicalId":175654,"journal":{"name":"Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121084635","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: 8

Selection on Modern CPUs 现代cpu的选择

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 2015-08-31 DOI: 10.1145/2803140.2803145

Steffen Zeuch, J. Freytag

引用次数: 10

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics 第三届VLDB内存数据管理与分析研讨会论文集

Proceedings of the 3rd VLDB Workshop on In-Memory Data Mangement and Analytics Pub Date : 1900-01-01 DOI: 10.1145/2803140

引用次数: 0