Survey on NoSQL database

Han, Jing; Haihong, E; Guan, Le; Du, Jian

doi:10.1109/icpca.2011.6106531

Cited by 248 publications

(59 citation statements)

References 2 publications

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“…We think ActCache can be applied to systems with which a certain file is accessed extremely frequently and the file is easily replaced by huge amount of accesses to other files. Redis [9] is not expected to be suitable for our method because it is mainly used for in-memory data structure store. HBase [9] may be suitable for our method since it stores data in files.…”

Section: Discussionmentioning

confidence: 99%

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Improving Dynamic Scaling Performance of Cassandra

Yamaguchi

Morimitsu

2017

IEICE Trans. Inf. & Syst.

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SUMMARYLoad size for a service on the Internet changes remarkably every hour. Thus, it is expected for service system scales to change dynamically according to load size. KVS (key-value store) is a scalable DBMS (database management system) widely used in largescale Internet services. In this paper, we focus on Cassandra, a popular open-source KVS implementation, and discuss methods for improving dynamic scaling performance. First, we evaluate node joining time, which is the time to complete adding a node to a running KVS system, and show that its bottleneck process is disk I/O. Second, we analyze disk accesses in the nodes and indicate that some heavily accessed files cause a large number of disk accesses. Third, we propose two methods for improving elasticity, which means decreasing node adding and removing time, of Cassandra. One method reduces disk accesses significantly by keeping the heavily accessed file in the page cache. The other method optimizes I/O scheduler behavior. Lastly, we evaluate elasticity of our methods. Our experimental results demonstrate that the methods can improve the scaling-up and scaling-down performance of Cassandra.

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Section: Discussionmentioning

confidence: 99%

“…Redis [9] is not expected to be suitable for our method because it is mainly used for in-memory data structure store. HBase [9] may be suitable for our method since it stores data in files. In many cases of HBase, files are stored in HDFS.…”

Section: Discussionmentioning

confidence: 99%

Improving Dynamic Scaling Performance of Cassandra

Yamaguchi

Morimitsu

2017

IEICE Trans. Inf. & Syst.

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“…As a result, key-value NoSQLs are much more flexible, usually offer higher query speed, and provide high concurrency [8]. Studies show that combining HPC with grid and cloud computing is not only feasible [13], but applying NoSQL into an HPC environment could also be beneficial [9].…”

Section: Related Workmentioning

confidence: 99%

Using Redis Supported by Nvram in HPC Applications

Malinowski

2017

csci

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Nowadays, the efficiency of storage systems is a bottleneck in many modern HPC clusters. High performance in the traditional approach -processing using files -is often difficult to obtain because of a model's complexity and its read/write patterns. An alternative approach is to apply a key-value database, which usually has low latency and scales well. On the other hand, many key-value stores suffer from a limitation of memory capacity and vulnerability to serious failures, which is caused by processing in RAM. Moreover, some research suggests that scientific data models are not applicable to the storage structures of key-value databases. In this paper, the author proposes a resolution to the above-mentioned issues by replacing RAM with NVRAM. A practical example is based on Redis NoSQL. The article also contains three domain-specific APIs that show the idea behind transforming from an HPC data model to Redis structures as well as two micro-benchmarks results.

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“…Nonrelational databases can be categorized as key-value, column-oriented, documentoriented, and graph databases [5], [21]. Considering the distinct types and diversity of healthcare data, the temporal sequence characteristics, and the continuously increasing number of data items, document-oriented and columnoriented NoSQL databases are most suitable for constructing healthcare databases.…”

Section: Related Workmentioning

confidence: 99%

Temporal Event Tracing on Big Healthcare Data Analytics

Lin

Huang

Chou

et al. 2014

2014 IEEE International Congress on Big Data

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This study presents a comprehensive method for rapidly processing, storing, retrieving, and analyzing big healthcare data. Based on NoSQL (not only SQL), a patientdriven data architecture is suggested to enable the rapid storing and flexible expansion of data. Thus, the schema differences of various hospitals can be overcome, and the flexibility for field alterations and addition is ensured. The timeline mode can easily be used to generate a visual representation of patient records, providing physicians with a reference for patient consultation. The sharding-key is used for data partitioning to generate data on patients of various populations. Subsequently, data reformulation is conducted as a first step, producing additional temporal and spatial data, providing cloud computing methods based on queryMapReduce-shard, and enhancing the search performance of data mining. Target data can be rapidly searched and filtered, particularly when analyzing temporal events and interactive effects.

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Survey on NoSQL database

Cited by 248 publications

References 2 publications

Improving Dynamic Scaling Performance of Cassandra

Improving Dynamic Scaling Performance of Cassandra

Using Redis Supported by Nvram in HPC Applications

Temporal Event Tracing on Big Healthcare Data Analytics

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