A comparison between several NoSQL databases with comments and notes

Tudorica, Bogdan George; Bucur, Cristian

doi:10.1109/roedunet.2011.5993686

Cited by 169 publications

(83 citation statements)

References 2 publications

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“…Carlson proposed that how to use Redis [6] on the system and also explain simple interaction with it using example of key-value data storage system which is useful in solving real problems. A comparison between several NoSQL databases with comments and notes [9]; to offer high performance on the basis of their speed.…”

Section: Related Workmentioning

confidence: 99%

In-Memory Data processing using Redis Database

Kaur¹,

Kaur²

2018

IJCA

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In present, in-memory data processing is becoming more popular due to examine a huge amount of information in shorter duration of time. Previously all servers utilize their own particular memory which is time consuming. To resolve this problem by using distributed cache, servers using cache memory for storing and retrieving data frequently. In present Big data processing, in-memory enumerate has become famous due to increase capacity and high throughput of main memory. Both relational and NoSQL databases are inmemory database that provides different mechanism for data storage and retrieval. In this paper, they make use of an inmemory key-value data storage system is Redis which works on a large data. Also, Redis server makes use of cache memory for increasing scalability and high throughput of main memory. Redis database helps in getting data from applications more frequently which improves the system performance as compared to relational database.

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Section: Related Workmentioning

confidence: 99%

In-Memory Data processing using Redis Database

Kaur¹,

Kaur²

2018

IJCA

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“…The currently popular NoSQL DBMS (such as MongoDB [5]) either does not support the ACID transaction management model or supports in only partially [6]. A database designer interacts with simple-structure documents, identifiable by a unique id (key-value model).…”

Section: Problem Formulationmentioning

confidence: 99%

“…1 the "automaton" table will have 20 entries; starting states are coded as (0,1) and (0,4), final states -as (3,0) and (5,0). Here are the contents of table "automaton" {(0,1), (1,5), (1,3), (1,2), (1,6), (2,2), (2,3), (2,5), (2,6), (0,4), (4,2), (4,6), (4,5), (4,3), (6,2), (6,6), (6,3), (6,5), (3,0), (5,0)}.…”

Section: Coding the Machine In The Parent-child Modelmentioning

confidence: 99%

Employing finite-state machines in data integrity problems

2016

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Abstract. This paper explores the issue of group integrity of tuple subsets regarding corporate integrity constraints in relational databases. A solution may be found by applying the finite-state machine theory to guarantee group integrity of data. We present a practical guide to coding such an automaton. After creating SQL queries to manipulate data and control its integrity for real data domains, we study the issue of query performance, determine the level of transaction isolation, and generate query plans.

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“…Relational databases provide ACID properties (atomicity, consistency, isolation, and durability), which guarantee reliable database transactions. At the same time, this limits the scalability of the databases [6,7]. As stated in Eric Brewer's CAP theorem [8], a system can have only two properties out of these three properties: consistency, availability, and partition-tolerance.…”

Section: Introductionmentioning

confidence: 99%

“…They generally relax the ACID constraints and provide BASE properties (basically available, soft state, and eventual consistency) instead [10]. The lower level of ACID compliance is traded off for higher availability [6,11], flexibility [12] and scalability.…”

Section: Introductionmentioning

confidence: 99%

Are NoSQL Data Stores Useful for Bioinformatics Researchers

Shao

Conrad

2015

IJRITCC

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Abstract-The big data challenge in bioinformatics is approaching. Data storage and processing, instead of experimental technologies, are becoming the slower and more costly part of research. Biological data typically have large size and a variety of structures. The ability to efficiently store and retrieve the data is important in bioinformatics research. Traditionally, large datasets are either stored as disk-based flat-files or in relational databases. These systems become more complicated to plan, maintain and adjust to big data applications as they follow rigid table schema and often lack scalability, e.g. for data aggregation. Meanwhile, non-relational databases (NoSQL) emerge to provide alternative, flexible and more scalable data stores.In this study, we aim to quantitatively compare the latencies of different data stores on storing and querying proteomics datasets. We show benchmarks for typical relational and non-relational systems for both, in-memory and disk-based configurations and compare them to a simple flat-file based approach. We will focus on the latencies of storing and querying proteomics mass spectrometry datasets and the actual space consumption inside the data stores. Experiments are carried out on a local desktop with medium-sized data, which is the typical experimental settings of individual bioinformatics researchers. Results show that there are significant latency differences among the considered data stores (up to 30 folds). In certain use cases, flat file system can achieve comparable performance with the data stores.

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A comparison between several NoSQL databases with comments and notes

Cited by 169 publications

References 2 publications

In-Memory Data processing using Redis Database

In-Memory Data processing using Redis Database

Employing finite-state machines in data integrity problems

Are NoSQL Data Stores Useful for Bioinformatics Researchers

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