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A Comparative Study of Consistent Snapshot Algorithms for Main-memory database systems

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第2期33卷 316-330页

作者： Li, Liang Wang, Guoren Wu, Gang Yuan, Ye Chen, Lei Lian, Xiang Northeastern Univ China Dept Comp Sci Shenyang 100819 Peoples R China Beging Inst Technol Dept Comp Sci Beijing 100081 Peoples R China Nanjing Univ Dept State Key Lab Novel Software Technol Sheng 210008 Jiangsu Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Hong Kong Peoples R China Kent State Univ Dept Comp Sci Kent OH 44240 USA

In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Specifically, it is necessary for IMDBs to have efficient snapshot performance to support certain special applications (e.g., consistent checkpoint, HTAP). Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in academia to trade off throughput and latency, but industrial IMDBs such as Redis adhere to the simple fork algorithm. To understand this phenomenon, we conduct comprehensive performance evaluations on mainstream snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from academia. Finally, we have opensourced the implementation of all the above snapshot algorithms so that practitioners are able to benchmark the performance of each algorithm and select proper methods for different application scenarios.

关键词： in-memory database systems snapshot algorithms checkpoints HTAP

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Work-Energy Profiles: General Approach and In-memory database Application 1

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8th TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC)

作者： Ungethuem, Annett Kissinger, Thomas Habich, Dirk Lehner, Wolfgang Tech Univ Dresden Database Syst Grp D-01062 Dresden Germany

ISBN: (数字)9783319543345

ISBN: (纸本)9783319543345;9783319543338

Recent energy-related hardware developments trend towards offering more and more configuration opportunities for the software to control its own energy consumption. Existing research so far mainly focused on finding the most energy-efficient hardware configuration for specific operators or entire queries in the database domain. However, the configuration opportunities influence the energy consumption as well as the processing performance. Thus, treating energy efficiency and performance as independent optimization goals offers a lot of drawbacks. To overcome these drawbacks, we introduce a model based approach in this paper which enables us to select a hardware configuration offering the best energy efficiency for a requested performance. Our model is a work-energy-profile being a set of useful work done during a fixed time span and the required energy for this work for all possible hardware configurations. The models are determined using a well-defined benchmark concept. Moreover, we apply our approach on in-memory databases and present the work-energy profiles for a heterogeneous multiprocessor.

关键词： Energy efficiency in-memory database systems Benchmarking Profiles

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Simulation of In-memory database Workload: Markov Chains versus Relative Invocation Frequency and Equal Probability - A Trade-off between Accuracy and Time 21

Simulation of In-Memory Database Workload: Markov Chains ver...

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ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： Barnert, Maximilian Krcmar, Helmut Tech Univ Munich Garching Germany

ISBN: (纸本)9781450381949

In the last years, performance modeling approaches have been proposed to tackle new concepts for modern in-memory database systems (IMDB). While these approaches model specific performance-relevant aspects, workload representation during performance modeling is considered only marginally. Furthermore, the manual integration of workload into modeling approaches comes along with high effort and requires deep domain-specific knowledge. This paper presents our experience in representing workload within performance models for IMDB. In particular, we use a Markov chain-based approach to extract and reflect probabilistic user behavior during performance modeling. An automatic model generation process is integrated to simplify and reduce the effort for transferring workload characteristics from traces to performance models. In an experimental series running analytical and transactional workloads on an IMDB, we compare this approach with two other methods which rely on less granular data to reflect database workload within performance models, namely reproducing the relative invocation frequency of queries and using the same query execution probability. The results reveal a trade-off between accuracy and speed when simulating database workload. Markov chains are the most accurate independent from workload characteristics, but the relative invocation frequency approach is appropriate for scenarios where simulation speed is important.

关键词： Performance Modeling Workload Representation Markov Chains database Workload in-memory database systems SAP HANA

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A perspective on applications of in-memory analytics in supply chain management

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DECISION SUPPORT systems 2015年 76卷 45-52页

作者： Hahn, G. J. Packowski, J. Univ Mannheim CAMELOT Management Consultants Endowed Assistant Sch Business Mannheim Germany CAMELOT Management Consultants AG Mannheim Germany

Big data, advanced analytics, and in-memory database technology are on the agenda of top management since they are seen as key enablers for enhanced business decision-making. In this paper, we provide a comprehensive perspective on applications of in-memory analytics in the field of supply chain management (SCM) that use the aforementioned concepts. Our contribution is threefold: First, we develop a top-down framework to position in-memory analytics applications against extant IT systems in SCM. Second, we conduct a bottom-up categorization of 41 in-memory analytics applications in SCM to provide supporting empirical evidence of the efficacy of the framework. Third, by contrasting top-down and bottom-up perspectives we derive implications for research and industrial practice. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Business analytics in-memory database systems Supply chain management Advanced planning Business intelligence

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Moses: Heap Partitioning for Semantic Data Tiering 2

Moses: Heap Partitioning for Semantic Data Tiering

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2nd Workshop on Disruptive memory systems (DIMES)

作者： Eberhardt, Felix Grapentin, Andreas Koehler, Sven Grzelka, Felix Hoenig, Timo Polze, Andreas IBM Res & Dev Boblingen Germany Hasso Plattner Inst Potsdam Germany Univ Potsdam Hasso Plattner Inst Potsdam Germany Ruhr Univ Bochum Bochum Germany

ISBN: (纸本)9798400713033

The adoption of emerging memory technologies has long been impeded by the lack of interfaces communicating memory placement semantics not only between applications, system software, and diagnostics tools but also between multiple tenants in a system. With the support of NUMA and memory tiering in Linux, applications can explicitly place pages based on domain knowledge. Yet, the internal reasoning behind placing a specific allocation into a specific memory resource remain largely hidden from the outside world. At the application level, different allocations exhibit different longevity, which is very prominent in the domain of in-memory database systems (IMDBS), where short-lived temporary allocations required by database operators are distinct from long-lived table data. Consequently, access patterns or non-functional requirements on memory resources like bandwidth and latency vary considerably. To allow for more fine-grained control over the allocation behavior of an application, we propose to partition the heap into memory regions with associated semantics, that are exposed in a hierarchical file system. Our prototype Moses enables domain experts (i.e., programmers) to express and refine memory placement requirements. We demonstrate how this can be applied to the in-memory database system Hyrise. Using Moses, we can achieve semantic data tiering, transparent to extrinsic operating system tools. We subsequently reduce the resident set size of the table data of the TPC-H benchmark by nearly 65 % at a moderate execution time overhead of less than 5 %.

关键词： memory Management memory Tiering in-memory database systems

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Improving Performances of an Embedded Relational database Management System with a Hybrid CPU/GPU Processing Engine 1

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5th International Conference on Data Management Technologies and Applications (DATA)

作者： Cremer, Samuel Bagein, Michel Mahmoudi, Said Manneback, Pierre Haute Ecole Hainaut Comp Engn Dept Ave Maistriau 8A B-7000 Mons Belgium Univ Mons Comp Sci Dept Rue Houdain 9 B-7000 Mons Belgium

ISBN: (数字)9783319629117

ISBN: (纸本)9783319629117;9783319629100

End-user systems are increasingly impacted by the exponential growth of data volumes and their processing. Moreover, post-processing operations, essentially dedicated to ergonomic features, require more and more resources. Improving overall performances of embedded relational database management systems (RDBMS) can contribute to deliver better responsiveness of end-user systems while increasing the energy efficiency. In this paper, it is proposed to upgrade SQLite, the most-spreaded embedded RDBMS, with a hybrid CPU/GPU processing engine combined with appropriate data management. With the proposed solution, named CuDB, massively parallel processing is combined with strategic data placement, closer to computing units. Experimental results revealed, in all cases, better performances and power efficiency compared to SQLite with an in-memory database.

关键词： in-memory database systems Embedded databases Relational database management systems GPU

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High-Throughput BitPacking Compression 22

High-Throughput BitPacking Compression

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45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA) / 22nd Euromicro Conference on Digital System Design (DSD)

作者： Lisa, Nusrat Jahan Nguyen, Tuan D. A. Habich, Dirk Kumar, Akash Lehner, Wolfgang Tech Univ Dresden Database Syst Grp Dresden Germany Tech Univ Dresden Proc Design Grp Dresden Germany

ISBN: (纸本)9781728128610

To efficiently support analytical applications from a data management perspective, in-memory column store database systems are state-of-the art. In this kind of database system, lossless lightweight integer compression schemes are crucial to keep the memory storage as low as possible and to speedup query processing. In this specific compression domain, BitPacking is one of the most frequently applied compression scheme. However, (de)compression should not come with any additional cost during run time, but should be provided transparently without compromising the overall system performance. To achieve that, we focus on acceleration of BitPacking using Field Programmable Gate Arrays (FPGAs). Therefore, we outline several FPGA designs for BitPacking in this paper. As we are going to show in our evaluation, our specific designs provide the BitPacking compression scheme with high-throughput.

关键词： in-memory database systems lightweight compression BitPacking hybrid hardware systems FPGA

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Accelerating Hybrid Transactional/Analytical Processing Using Consistent Dual-Snapshot 24th

Accelerating Hybrid Transactional/Analytical Processing Usin...

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24th International Conference on database systems for Advanced Applications (DASFAA)

作者： Li, Liang Wu, Gang Wang, Guoren Yuan, Ye Northeastern Univ Comp Sci & Engn Shenyang Liaoning Peoples R China Beijing Inst Technol Comp Sci & Technol Beijing Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9783030185763;9783030185756

To efficiently deal with OLTP and OLAP workload simultaneously, the conventional method is to deploy two separate processing systems, i.e., the transaction-friendly designed OLTP system and the analytic-friendly optimized OLAP system. To maintain the freshness of the data, extra ETL tools are required to propagate data from the OLTP to the OLAP system. However, low-speed ETL processing is the bottleneck in those business decision support systems. As a result, there has been tremendous interest in developing hybrid transactional/analytical processing (HTAP) systems. This paper proposes a wait-free HTAP (WHTAP) architecture, that can perform both OLTP and OLAP requests efficiently in a wait-free form. We develop and evaluate a prototype WHTAP system. Our experiments show that the system can obtain a similar OLTP performance as the TicToc system and a four to six times acceleration in analytical processing at the same time.

关键词： HTAP in-memory database systems Transaction concurrency control

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Boosting an Embedded Relational database Management System with Graphics Processing Units 5

Boosting an Embedded Relational Database Management System w...

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5th International Conference on Data Management Technologies and Applications (DATA)

作者： Cremer, Samuel Bagein, Michel Mahmoudi, Said Manneback, Pierre Univ Mons Dept Comp Sci Rue Houdain 9 B-7000 Mons Belgium

ISBN: (纸本)9789897581939

Concurrently, with the rise of Big Data systems, relational database management systems (RDBMS) are still widely exploited in servers, client devices, and even embedded inside end-user applications. In this paper, it is suggest to improve the performance of SQLite, the most deployed embedded RDBMS. The proposed solution, named CuDB, is an "In-memory" database System (IMDB) which attempts to exploit specificities of modern CPU / GPU architectures. In this study massively parallel processing was combined with strategic data placement, closer to computing units. According to content and selectivity of queries, the measurements reveal an acceleration range between 5 to 120 times

关键词： in-memory database systems Embedded databases Relational database Management systems GPU

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A perspective on applications of in-memory and associative approaches supporting cultural big data analytics

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INTERNATIONAL JOURNAL OF COMPUTATIONAL SCIENCE AND ENGINEERING 2018年第3期16卷 219-233页

作者： Chianese, Angelo Piccialli, Francesco Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy

Business intelligence, advanced analytics, big data, in-memory database and associative technologies are actually the key enablers for enhanced business decision making. In this paper, we provide a perspective on applications of in-memory approaches supporting analytics in the field of the cultural heritage applied to information resources including structured and unstructured contents, geo-spatial and social network data, multimedia (MM), multiple domain vocabularies, classifiers and ontologies. The proposed approach has been implemented in an information system exploiting associative in-memory technologies in a cloud context, as well as integrating semantic technologies for merging and analysing information coming from heterogeneous sources. We analyse and describe the application of this system to trace a behavioural and interest profile of users and visitors for cultural events (exhibitions, museums, etc.) and territorial (tourist areas and routes including cultural resources, historical down-town, and archaeological sites). The results of ongoing experimentation encourage a business intelligence approach which is suitable for supporting cultural heritage asset crowdsourcing, promotion, publication, management and usage.

关键词： in-memory database systems big data social analytics business intelligence cultural heritage internet of things IoT

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