Data Management in Non-Volatile Memory

Viglas, Stratis D.

doi:10.1145/2723372.2731082

Cited by 22 publications

(7 citation statements)

References 40 publications

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“…The current trends indicate that these techniques are significantly changing the underlying environment of traditional data management and analysis systems, including highperformance processors and hardware accelerators, NVM, RDMA-capable (remote direct memory access) networks. Significantly, the ongoing underlying environments, marked by heterogeneous multi-core architecture and hybrid storage hierarchy, make the already complicated software design space become more sophisticated [1][2][3][4].…”

Section: 2 the Trend Of Hardwarementioning

confidence: 99%

The New Hardware Development Trend and the Challenges in Data Management and Analysis

Pan

Zhang

et al. 2018

Data Sci. Eng.

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Hardware techniques and environments underwent significant transformations in the field of information technology, represented by high-performance processors and hardware accelerators characterized by abundant heterogeneous parallelism, nonvolatile memory with hybrid storage hierarchies, and RDMA-enabled high-speed network. Recent hardware trends in these areas deeply affect data management and analysis applications. In this paper, we first introduce the development trend of the new hardware in computation, storage, and network dimensions. Then, the related research techniques which affect the upper data management system design are reviewed. Finally, challenges and opportunities are addressed for the key technologies of data management and analysis in new hardware environments.

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Section: 2 the Trend Of Hardwarementioning

confidence: 99%

The New Hardware Development Trend and the Challenges in Data Management and Analysis

Pan

Zhang

et al. 2018

Data Sci. Eng.

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“…All the reads during the join phase tend to be sequential, while the writes in the partitioning phase are random. We note that these adapted cost functions still do not account for the byte-addressability of NVM [77].…”

Section: Query Optimizermentioning

confidence: 99%

“…This tutorial differs from previous presentations on NVM [77] because we go beyond storage management and talk about the entire internal DBMS stack. We will couch all of the above topics in the context of the Peloton [4,63] DBMS.…”

Section: Introductionmentioning

confidence: 97%

How to Build a Non-Volatile Memory Database Management System

Arulraj

Pavlo

2017

Proceedings of the 2017 ACM International Conference on Management of Data

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The difference in the performance characteristics of volatile (DRAM) and non-volatile storage devices (HDD/SSDs) influences the design of database management systems (DBMSs). The key assumption has always been that the latter is much slower than the former. This affects all aspects of a DBMS's runtime architecture. But the arrival of new non-volatile memory (NVM) storage that is almost as fast as DRAM with fine-grained read/writes invalidates these previous design choices. In this tutorial, we provide an outline on how to build a new DBMS given the changes to hardware landscape due to NVM. We survey recent developments in this area, and discuss the lessons learned from prior research on designing NVM database systems. We highlight a set of open research problems, and present ideas for solving some of them. 1 NVM is also referred to as storage-class memory or persistent memory.

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“…Data Blocks are self-contained containers that store one or more attribute chunks in a byte-addressable compressed format. The goal of Data Blocks is to conserve memory tuple count sma offset 0 dict offset 0 data offset 0 compression 0 string offset 0 sma offset 1 [8,33]. In HyPer, Data Blocks are used as a compressed in-memory storage format for cold data and for persistence.…”

Section: Data Blocksmentioning

confidence: 99%

Data Blocks

Lang

Mühlbauer

Funke³

et al. 2016

Proceedings of the 2016 International Conference on Management of Data

104

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This work aims at reducing the main-memory footprint in high performance hybrid OLTP & OLAP databases, while retaining high query performance and transactional throughput. For this purpose, an innovative compressed columnar storage format for cold data, called Data Block s is introduced. Data Blocks further incorporate a new lightweight index structure called Positional SMA that narrows scan ranges within Data Blocks even if the entire block cannot be ruled out. To achieve highest OLTP performance, the compression schemes of Data Blocks are very lightweight , such that OLTP transactions can still quickly access individual tuples. This sets our storage scheme apart from those used in specialized analytical databases where data must usually be bit-unpacked. Up to now, high-performance analytical systems use either vectorized query execution or "just-in-time" (JIT) query compilation. The fine-grained adaptivity of Data Blocks necessitates the integration of the best features of each approach by an interpreted vectorized scan subsystem feeding into JIT-compiled query pipelines. Experimental evaluation of HyPer, our full-fledged hybrid OLTP & OLAP database system, shows that Data Blocks accelerate performance on a variety of query workloads while retaining high transaction throughput.

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Data Management in Non-Volatile Memory

Cited by 22 publications

References 40 publications

The New Hardware Development Trend and the Challenges in Data Management and Analysis

The New Hardware Development Trend and the Challenges in Data Management and Analysis

How to Build a Non-Volatile Memory Database Management System

Data Blocks

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