Uniform data access platform for SQL and NoSQL database systems

2017 ◽  
Vol 69 ◽  
pp. 93-105 ◽  
Author(s):  
Ágnes Vathy-Fogarassy ◽  
Tamás Hugyák
Keyword(s):  
Data Access ◽  
Database Systems ◽  
Nosql Database

scholarly journals Performance Benchmarking of Key-Value Store NoSQL Databases

2018 ◽  
Vol 8 (6) ◽  
pp. 5333
Author(s):  
Omoruyi Osemwegie ◽  
Kennedy Okokpujie ◽  
Nsikan Nkordeh ◽  
Charles Ndujiuba ◽  
Samuel John ◽  
...  
Keyword(s):  
Data Storage ◽  
Web Applications ◽  
Query Language ◽  
Research Work ◽  
Database Systems ◽  
Nosql Databases ◽  
Performance Benchmarking ◽  
Nosql Database ◽  
Structured Query Language ◽  
Web Developers

<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>

Fragment Re-Allocation Strategy Based on Hypergraph for NoSQL Database Systems

2016 ◽  
Vol 8 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Zhikun Chen ◽  
Shuqiang Yang ◽  
Yunfei Shang ◽  
Yong Liu ◽  
Feng Wang ◽  
...  
Keyword(s):  
Database Systems ◽  
Communication Cost ◽  
Allocation Strategy ◽  
Hypergraph Partitioning ◽  
Computing Model ◽  
Operation Pattern ◽  
Nosql Database ◽  
High Scalability ◽  
Weighted Hypergraph ◽  
Allocation Strategies

NoSQL database is famed for the characteristics of high scalability, high availability, and high fault-tolerance. It is used to manage data for a lot of applications. The computing model has been transferred to “computing close to data”. Therefore, the location of fragment directly affects system's performance. Every site's load dynamical changes because of the increasing data and the ever-changing operation pattern. So system has to re-allocate fragment to improve system's performance. The general fragment re-allocation strategies of NoSQL database scatter the related fragments as possible to improve the operations' parallel degree. But those fragments may interact with each other in some application's operations. So the high parallel degree of operation may increase system's communication cost such as data are transferred by network. In this paper, the authors propose a fragment re-allocation strategy based on hypergraph. This strategy uses a weighted hypergraph to represent the fragments' access pattern of operations. A hypergraph partitioning algorithm is used to cluster fragments in the strategy. This strategy can improve system's performance according to reducing the communication cost while guaranteeing the parallel degree of operations. Experimental results confirm that the strategy will effectively contribute in solving fragment re-allocation problem in specific application environment of NoSQL database system, and it can improve system's performance.

Probabilistic analysis of transaction blocking under arbitrary data access distribution in database systems

1994 ◽  
Vol 78 (3-4) ◽  
pp. 161-187 ◽  
Author(s):  
Mukesh Singhal ◽  
Yelena Yesha ◽  
Ming T. (Mike) Liu
Keyword(s):  
Probabilistic Analysis ◽  
Data Access ◽  
Database Systems

scholarly journals Application-Oriented Data Migration to Accelerate In-Memory Database on Hybrid Memory

Micromachines ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 52
Author(s):  
Wenze Zhao ◽  
Yajuan Du ◽  
Mingzhe Zhang ◽  
Mingyang Liu ◽  
Kailun Jin ◽  
...  
Keyword(s):  
Random Access ◽  
Data Access ◽  
Database Systems ◽  
Data Migration ◽  
Memory Architecture ◽  
Hybrid Memory ◽  
High Bandwidth ◽  
Data Objects ◽  
Stacked Memory

With the advantage of faster data access than traditional disks, in-memory database systems, such as Redis and Memcached, have been widely applied in data centers and embedded systems. The performance of in-memory database greatly depends on the access speed of memory. With the requirement of high bandwidth and low energy, die-stacked memory (e.g., High Bandwidth Memory (HBM)) has been developed to extend the channel number and width. However, the capacity of die-stacked memory is limited due to the interposer challenge. Thus, hybrid memory system with traditional Dynamic Random Access Memory (DRAM) and die-stacked memory emerges. Existing works have proposed to place and manage data on hybrid memory architecture in the view of hardware. This paper considers to manage in-memory database data in hybrid memory in the view of application. We first perform a preliminary study on the hotness distribution of client requests on Redis. From the results, we observe that most requests happen on a small portion of data objects in in-memory database. Then, we propose the Application-oriented Data Migration called ADM to accelerate in-memory database on hybrid memory. We design a hotness management method and two migration policies to migrate data into or out of HBM. We take Redis under comprehensive benchmarks as a case study for the proposed method. Through the experimental results, it is verified that our proposed method can effectively gain performance improvement and reduce energy consumption compared with existing Redis database.

SWIFT

2009 ◽  
pp. 737-743 ◽  
Author(s):  
Udai Shanker ◽  
Manoj Misra ◽  
Anil K. Sarje
Keyword(s):  
Response Times ◽  
Data Access ◽  
Database Systems ◽  
Data Consistency ◽  
Timing Constraints ◽  
Medical Monitoring ◽  
Cpu Scheduling ◽  
Storage And Retrieval ◽  
Database Technology ◽  
Remote Sites

Many applications such as military tracking, medical monitoring, stock arbitrage system, network management, aircraft control, factory automation, and so forth that depend heavily on database technology for the proper storage and retrieval of data located at different remote sites have certain timing constraints associated with them. Such applications introduce the need for distributed real-time database systems (DRTDBS) [Ramamritham, 1993]. The implementation of DRTDBS is difficult due to the conflicting requirements of maintaining data consistency and meeting distributed transaction’s deadlines. The difficulty comes from the unpredictability of the transactions’ response times [Huang, 1991]. Due to the distributed nature of the transactions and in presence of other sources of unpredictability such as data access conflicts, uneven distribution of transactions over the sites, variable local CPU scheduling time, communication delay, failure of coordinator and cohort’s sites, and so forth, it is not easy to meet the deadline of all transactions in DRTDBS [Kao & Garcia – Monila, 1995]. The unpredictability in the commitment phase makes it more serious because the blocking time of the waiting cohorts due to execute-commit conflict may become longer. Hence, due to unique characteristics of the committing transactions and unpredictability in the commitment process, design of an efficient commit protocol is an important issue that affects the performance of DRTDBS [Shanker, Misra & Sarje, 2006d].

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