Proceedings of the 17th International Workshop on Data Management on New Hardware最新文献

{"title":"KallaxDB: A Table-less Hash-based Key-Value Store on Storage Hardware with Built-in Transparent Compression","authors":"Xubin Chen, Ning Zheng, Shukun Xu, Yifan Qiao, Yang Liu, Jiangpeng Li, Tong Zhang","doi":"10.1145/3465998.3466004","DOIUrl":"https://doi.org/10.1145/3465998.3466004","url":null,"abstract":"This paper studies the design of a key-value (KV) store that can take full advantage of modern storage hardware with built-in transparent compression capability. Many modern storage appliances/drives implement hardware-based data compression, transparent to OS and applications. Moreover, the growing deployment of hardware-based compression in Cloud infrastructure leads to the imminent arrival of Cloud-based storage hardware with built-in transparent compression. By decoupling the logical storage space utilization efficiency from the true physical storage usage, transparent compression allows data management software to purposely waste logical storage space in return for simpler data structures and algorithms, leading to lower implementation complexity and higher performance. This work proposes a table-less hash-based KV store, where the basic idea is to hash the key space directly onto the logical storage space without using a hash table at all. With a substantially simplified data structure, this approach is subject to significant logical storage space under-utilization, which can be seamlessly mitigated by storage hardware with transparent compression. This paper presents the basic KV store architecture, and develops mathematical formulations to assist its configuration and analysis. We implemented such a KV store KallaxDB and carried out experiments on a commercial SSD with built-in transparent compression. The results show that, while consuming very little memory resource, it compares favorably with the other modern KV stores in terms of throughput, latency, and CPU usage.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124519211","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}

{"title":"Filter Representation in Vectorized Query Execution","authors":"Amadou Latyr Ngom, Prashanth Menon, Matthew Butrovich, Lin Ma, Wan Shen Lim, T. Mowry, Andrew Pavlo","doi":"10.1145/3465998.3466009","DOIUrl":"https://doi.org/10.1145/3465998.3466009","url":null,"abstract":"Advances in memory technology have made it feasible for database management systems (DBMS) to store their working data set in main memory. This trend shifts the bottleneck for query execution from disk accesses to CPU efficiency. One technique to improve CPU efficiency is batch-oriented processing, or vectorization, as it reduces interpretation overhead. For each vector (batch) of tuples, the DBMS must track the set of valid (visible) tuples that survive all previous processing steps. To that end, existing systems employ one of two data structures, or filter representations: selection vectors or bitmaps. In this work, we analyze each approach's strengths and weaknesses and offer recommendations on how to implement vectorized operations. Through a wide range of micro-benchmarks, we determine that the optimal strategy is a function of many factors: the cost of iterating through tuples, the cost of the operation itself, and how amenable it is to SIMD vectorization. Our analysis shows that bitmaps perform better for operations that can be vectorized using SIMD instructions and that selection vectors perform better on all other operations due to cheaper iteration logic.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126212418","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}

{"title":"A Parametric I/O Model for Modern Storage Devices","authors":"Tarikul Islam Papon, Manos Athanassoulis","doi":"10.1145/3465998.3466003","DOIUrl":"https://doi.org/10.1145/3465998.3466003","url":null,"abstract":"Storage devices have evolved to offer increasingly faster read/write access, through flash-based and other solid-state storage technologies. When compared to classical rotating hard disk drives (HDDs), modern solid-state drives (SSDs) have two key differences: (i) the absence of mechanical parts, and (ii) an inherent difference between the process of reading and writing. The former removes a key performance bottleneck, enabling internal device parallelism, whereas the latter manifests as a read/write performance asymmetry. In other words, SSDs can serve multiple concurrent I/Os, and their writes are generally slower than reads; none of which is true for HDDs. Yet, the performance of storage-resident applications is typically modeled by the number of disk accesses performed, inherently assuming symmetric read and write performance and the ability to perform only one I/O at a time, failing to accurately capture the performance of modern storage devices. To address this mismatch, we propose a simple yet expressive storage model, termed Parametric I/O Model (PIO) that captures contemporary devices by parameterizing read/write asymmetry (α) and access concurrency (k). PIO enables device-specific decisions at algorithm design time, rather than as an optimization during deployment and testing, thus ensuring optimal algorithm design by taking into account the properties of each device. We present a benchmarking of several storage devices that shows that α and k vary significantly across devices. Further, we show that using carefully quantified values of α and k for each storage device, we can fully exploit the performance it offers, and we lay the groundwork for asymmetry/concurrency-aware storage-intensive algorithms. We also highlight that the degree of the performance benefit due to concurrent reads or writes depends on the asymmetry of the underlying device. Finally, we summarize our findings as a set of guidelines for designing storage-intensive algorithms and discuss specific examples for better algorithm and system designs as well as runtime tuning.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"313 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123205757","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}

{"title":"Workload-Driven Placement of Column-Store Data Structures on DRAM and NVM","authors":"Robert Lasch, Robert Schulze, T. Legler, K. Sattler","doi":"10.1145/3465998.3466008","DOIUrl":"https://doi.org/10.1145/3465998.3466008","url":null,"abstract":"Non-volatile memory (NVM) offers lower costs per capacity and higher total capacities than DRAM. However, NVM cannot simply be used as a drop-in replacement for DRAM in database management systems due to its different performance characteristics. We thus investigate the placement of column-store data structures in a hybrid hierarchy of DRAM and NVM, with the goal of placing as much data as possible in NVM without compromising performance. After analyzing how different memory access patterns affect query runtimes when columns are placed in NVM, we propose a heuristic that leverages lightweight access counters to suggest which structures should be placed in DRAM and which in NVM. Our evaluation using TPC-H shows that more than 80% of the data touched by queries can be placed in NVM with almost no slowdown, while naively placing all data in NVM would increase runtime by 53%.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114070319","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}

{"title":"Playing Fetch with CAT: Composing Cache Partitioning and Prefetching for Task-based Query Processing","authors":"Qitian Zeng, Kyle C. Hale, Boris Glavic","doi":"10.1145/3465998.3466016","DOIUrl":"https://doi.org/10.1145/3465998.3466016","url":null,"abstract":"Software prefetching and hardware-based cache allocation techniques (CAT) have been successfully applied in main-memory database engines to fetch data into cache before it is needed and to partition a shared last-level cache (LLC) to prevent concurrent tasks from evicting each others' data. We investigate the interaction of these techniques and demonstrate that while a single prefetching strategy is sufficient, the combination of both techniques is only effective if the cache partitioning strategy adapts the partitioning based on the types of tasks currently sharing an LLC. We present a simple, yet effective, scheme that uses prefetching and adapts cache partition allocations dynamically.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"60 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114036897","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}

{"title":"Instant Graph Query Recovery on Persistent Memory","authors":"Alexander Baumstark, Philipp Götze, M. Jibril, K. Sattler","doi":"10.1145/3465998.3466011","DOIUrl":"https://doi.org/10.1145/3465998.3466011","url":null,"abstract":"Persistent memory (PMem) - also known as non-volatile memory (NVM) - offers new opportunities not only for the design of data structures and system architectures but also for failure recovery in databases. However, instant recovery can mean not only to bring the system up as fast as possible but also to continue long-running queries which have been interrupted by a system failure. In this work, we discuss how PMem can be utilized to implement query recovery for analytical graph queries. Furthermore, we investigate the trade-off between the overhead of managing the query state in PMem at query runtime as well as the recovery and restart costs.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114355107","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}

{"title":"Resource-Efficient Database Query Processing on FPGAs","authors":"Mehdi Moghaddamfar, Christian Färber, Wolfgang Lehner, Norman May, Akash Kumar","doi":"10.1145/3465998.3466006","DOIUrl":"https://doi.org/10.1145/3465998.3466006","url":null,"abstract":"FPGA technology has introduced new ways to accelerate database query processing, that often result in higher performance and energy efficiency. This is thanks to the unique architecture of FPGAs using reconfigurable resources to behave like an application-specific integrated circuit upon programming. The limited amount of these resources restricts the number and type of modules that an FPGA can simultaneously support. In this paper, we propose \"morphing sort-merge\": a set of run-time configurable FPGA modules that achieves resource efficiency by reusing the FPGA's resources to support different pipeline-breaking database operators, namely sort, aggregation, and equi-join. The proposed modules use dynamic optimization mechanisms that adapt the implementation to the distribution of data at run-time, thus resulting in higher performance. Our benchmarks show that morphing sort-merge reaches an average speedup of 5x compared to MonetDB.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"202 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134440433","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}

{"title":"Drop It In Like It's Hot: An Analysis of Persistent Memory as a Drop-in Replacement for NVMe SSDs","authors":"Maximilian Böther, Otto Kißig, Lawrence Benson, T. Rabl, H. Plattner","doi":"10.1145/3465998.3466010","DOIUrl":"https://doi.org/10.1145/3465998.3466010","url":null,"abstract":"Solid-state drives (SSDs) have improved database system performance significantly due to the higher bandwidth that they provide over traditional hard disk drives. Persistent memory (PMem) is a new storage technology that offers DRAM-like speed at SSD-like capacity. Due to its byte-addressability, research has mainly treated PMem as a replacement of, or an addition to DRAM, e.g., by proposing highly-optimized, DRAM-PMem-hybrid data structures and system designs. However, PMem can also be used via a regular file system interface and standard Linux I/O operations. In this paper, we analyze PMem as a drop-in replacement for Non-Volatile Memory Express (NVMe) SSDs and evaluate possible performance gains while requiring no or only minor changes to existing applications. This drop-in approach speeds-up database systems like Postgres, without requiring any code changes. We systematically evaluate PMem and NVMe SSDs in three database microbenchmarks and the widely used TPC-H benchmark on Postgres. Our experiments show that PMem outperforms a RAID of four NVMe SSDs in read-intensive OLAP workloads by up to 4x without any modifications while achieving similar performance in write-intensive workloads. Finally, we give four practical insights to aid decision-making on when to use PMem as an SSD drop-in replacement and how to optimize for it.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116592966","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}

{"title":"Proceedings of the 17th International Workshop on Data Management on New Hardware","authors":"","doi":"10.1145/3465998","DOIUrl":"https://doi.org/10.1145/3465998","url":null,"abstract":"","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122672438","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}

{"title":"An Energy-Efficient Stream Join for the Internet of Things","authors":"Adrian Michalke, P. M. Grulich, Clemens Lutz, Steffen Zeuch, V. Markl","doi":"10.1145/3465998.3466005","DOIUrl":"https://doi.org/10.1145/3465998.3466005","url":null,"abstract":"The Internet of Things (IoT) combines large data centers with (mobile, networked) edge devices that are constrained both in compute power and energy budget. Modern edge devices contribute to query processing by leveraging accelerated processing units with multicore CPUs or GPUs. Therefore, data processing in the IoT presents the challenges of 1) minimizing the energy consumed while sustaining a given query throughput, and 2) processing increasingly complex queries within a given energy budget. In this paper, we investigate how modern edge devices can reduce the energy requirements of stream joins as a common data processing operation. We explore three dimensions to save energy: workload characteristics, computational efficiency, and heterogeneous hardware. Based on our findings, we propose the ecoJoin that 1) reduces energy consumption by 81% at a given join throughput, and 2) enables scaling the throughput by two orders-of-magnitude within a given energy budget.","PeriodicalId":183683,"journal":{"name":"Proceedings of the 17th International Workshop on Data Management on New Hardware","volume":"568 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123375209","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}