High-Performance Design of HBase with RDMA over InfiniBand

Huang, Jian; Ouyang, Xiangyong; Jose, Jithin; Wasi-ur-Rahman, Md.; Wang, Hao; Luo, Miao; Subramoni, Hari; Murthy, Chet; Panda, Dhabaleswar K.

doi:10.1109/ipdps.2012.74

Cited by 62 publications

(17 citation statements)

References 17 publications

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“…We focus on performance measurements of Cassandra and HBase, as both systems deliver better performance for write-intense workloads than other database systems, and are therefore most suitable for APM applications. Publication SB1 SB2 SB3 EB1 EB2 Awasthi et al [1] x Cooper et al [4] x x x Dory et al [8] x Huang et al [10] x x Klems et al [11] x x Konstantinou et al [12] x x x x Rabl et al [14] x x Rahman et al [15] x x Table 1: Classification of scalability and elasticity benchmarking approaches.…”

Section: Selected Experimentsmentioning

confidence: 99%

Benchmarking scalability and elasticity of distributed database systems

2014

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Distributed database system performance benchmarks are an important source of information for decision makers who must select the right technology for their data management problems. Since important decisions rely on trustworthy experimental data, it is necessary to reproduce experiments and verify the results. We reproduce performance and scalability benchmarking experiments of HBase and Cassandra that have been conducted by previous research and compare the results. The scope of our reproduced experiments is extended with a performance evaluation of Cassandra on different Amazon EC2 infrastructure configurations, and an evaluation of Cassandra and HBase elasticity by measuring scaling speed and performance impact while scaling.

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Section: Selected Experimentsmentioning

confidence: 99%

Benchmarking scalability and elasticity of distributed database systems

2014

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“…Some BD examples are Cassandra used by eBay, Github, Instagram, Netflix [42], Hbase used by Facebook [43] and Twitter [44], Dynamo Amazon [45], among others. For DW, some research results like [46] present a dimensional model under MongoDB, where query efficiency is analyzed against a SQL Server.…”

Section: Nosql Databasesmentioning

confidence: 99%

Data Warehouse and Big Data Integration

Salinas¹,

Lemus²

2017

IJCSIT

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“…Huang et. al [21] use high performance networks to optimize HBase read/write performance by exploiting the Infiniband RDMA capability to match the object delivery model in HBase. These works can significantly improve HBase performance by taking advantage of novel data storage and transfer technologies.…”

Section: Related Workmentioning

confidence: 99%

HConfig: Resource Adaptive Fast Bulk Loading in HBase

Bao¹,

Liu²,

Xiao³

et al. 2014

Proceedings of the 10th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing

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Abstract-NoSQL (Not only SQL) data stores become a vital component in many big data computing platforms due to its inherent horizontal scalability. HBase is an open-source distributed NoSQL store that is widely used by many Internet enterprises to handle their big data computing applications (e.g. Facebook handles millions of messages each day with HBase). Optimizations that can enhance the performance of HBase are of paramount interests for big data applications that use HBase or Big Table like key-value stores. In this paper we study the problems inherent in misconfiguration of HBase clusters, including scenarios where the HBase default configurations can lead to poor performance. We develop HConfig, a semiautomated configuration manager for optimizing HBase system performance from multiple dimensions. Due to the space constraint, this paper will focus on how to improve the performance of HBase data loader using HConfig. Through this case study we will highlight the importance of resource adaptive and workload aware auto-configuration management and the design principles of HConfig. Our experiments show that the HConfig enhanced bulk loading can significantly improve the performance of HBase bulk loading jobs compared to the HBase default configuration, and achieve 2~3.7x speedup in throughput under different client threads while maintaining linear horizontal scalability.

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High-Performance Design of HBase with RDMA over InfiniBand

Cited by 62 publications

References 17 publications

Benchmarking scalability and elasticity of distributed database systems

Benchmarking scalability and elasticity of distributed database systems

Data Warehouse and Big Data Integration

HConfig: Resource Adaptive Fast Bulk Loading in HBase

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