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A literature review on NoSQL database for big data processing

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DOI: 10.14419/ijet.v7i2.12113

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International Journal of Engineering & Technology, 7 (2) (2018) 902-906

International Journal of Engineering & Technology

Website: www.sciencepubco.com/index.php/IJET doi: 10.14419/ijet.v7i2.12113

Research paper

A literature review on NoSQL database for big data processing

Md. Razu Ahmed 1, Mst. Arifa Khatun 1, Md. Asraf Ali 1 *, Kenneth Sundaraj 2

1 Department of Software Engineering, Daffodil International University, Dhaka, Bangladesh

2 Faculty of Electronics and Computer Engineering, Universiti Teknikal Malaysia Melaka, Malaysia

*Corresponding author E-mail: [email protected]

Abstract

Objective: Aim of the present study was to literature review on the NoSQL Database for Big Data processing including the structural

issues and the real-time data mining techniques to extract the estimated valuable information.

Methods: We searched the Springer Link and IEEE Xplore online databases for articles published in English language during the last

seven years (between January 2011 and December 2017). We specifically searched for two keywords (“NoSQL” and “Big Data”) to find

the articles. The inclusion criteria were articles on the use of performance comparison on valuable information processing in the field of

Big Data through NoSQL databases.

Results: In the 18 selected articles, this review identified 8 articles which provided various suitable recommendations on NoSQL databases

for specific area focus on the value chain of Big Data, 5 articles described the performance comparison of different NoSQL databases, 2

articles presented the background of basics characteristics data model for NoSQL, 1 article denoted the storage in respect of cloud compu-

ting and 2 articles focused the transactions of NoSQL.

Conclusion: In this literature, we presented the NoSQL databases for Big Data processing including its transactional and structural issues.

Additionally, we highlight research directions and challenges in relation to Big Data processing. Therefore, we believe that the information

contained in this review will incredible support and guide the progress of the Big Data processing.

Keywords: Big Data; Data Processing; Hadoop; Mongo DB; NoSQL.

1. Introduction

We are living an era of data ocean. The past 25 years, Data has

raised in a massive scale in diverse fields including software based

medical rehabilitation system [1] and sports coaching [2]. Accord-

ing to the report of International Data Corporation (IDC), the over-

all created data in the world will reach 44 ZB or trillion gigabytes

during the time of 2013 to 2020 [3]. This vast amount of data refers

to a Big Data that is a global buzzword. However, if we do not pro-

cess Big Data, the outcome of their invisible data would be missed.

It is one of the big problem for Big Data analysis and giant compa-

nies [3]. It cannot be effectively & efficiently managed using tradi-

tional database management tools [4]. To handle this problem, users

have a number of options how to reach the problem related with

such data. For example, to store and process vast scale datasets us-

ers can use various database technology including NoSQL data-

bases [5]. NoSQL originally started off a simple combination of two

words ‘No’ and ‘SQL’ explained as the ellipsis of not only SQL [6].

Hence, NoSQL is a generic term used to refer to any data store or

process that does not follow the traditional model of relational da-

tabase management system [7]. There are 4 basic types of NoSQL

databases includes key-value store, document-based store, and

graph-based store [8].The key-value store NoSQL database basi-

cally, uses a hash table in which there exists a unique key and value

to a specific data. The values are identified and retrieved through a

key, and stored values can be numbers, strings, JSON, XML,

HTML, binaries, images, videos and few others [7]. Document-

Store NoSQL Database, stores each record and data within a single

document. A document-store NoSQL database is used for storing,

recovering, and handling semi-structured data [8]. In column-ori-

ented NoSQL database, data is stored in cells grouped in columns

of data. Columns are logically grouped into column families. A

Graph-based NoSQL database that uses relationships and nodes to

represent and store data. NoSQL database system are emerging be-

side main internet and IT company, such as Google, Amazon, Fa-

cebook, Alibaba, IBM; which company are dealing with huge

amount of data with traditional Relational Database System could

not handle. Therefore, the aim of the study was to help users, espe-

cially to obtain an independent understanding of the strengths and

weakness of NoSQL database approach and where we able to im-

prove for managing huge volume of data.

2. Methods

2.1. Article searching procedure

We used a systematic searching procedure to identify all of the

available articles that discuss the storing and managing data for Big

Data situation using NoSQL databases. In our systematic searching

procedure, we searched two keywords from the Springer Link and

IEEE Xplore digital databases in order to assess the article. Firstly,

we used the keyword “NoSQL” to find journal articles published in

the English language between years 2011 to 2017. We then used the

keyword “Big Data” within obtained set of search results to further

narrow the set of analyzed journal article.

2.2. Article inclusion and exclusion benchmark

International Journal of Engineering & Technology 903

For the final preference of articles that applied the NoSQL database

system for Big Data management. We used some benchmark to in-

clude and exclude articles from the set of articles that were selected

through the search of IEEE Explore and Springer Link online data-

bases. To include and exclude articles from the set of articles found

through our systematic searching technique, we read the title, ab-

stract, methodology and results of each article. We considered only

those articles that were written in English and that used NoSQL Da-

tabases. The exclusion criteria were the following: 1) Article that

applied NoSQL database other than Big Data management, 2) Ar-

ticle that managed Big Data using other than NoSQL database.

2.3. Data extraction

We carefully read and analyzed all of included articles to minutes

the key information. We followed a standard data extraction from

for the particular analysis of each article. Two of the authors of this

study (RA and AK) used our designed standard data extraction form

to track the key information and compared them in order to confirm

the accuracy of the extracted records. Each article was evaluated for

the following key information: (1) Performance comparison for dif-

ferent NoSQL databases for Big Data processing, (2) Overview of

different NoSQL databases and Big Data processing technologies,

(3) Database Engine Ranking, (4) Data Models of NoSQL Data-

bases, (5) Transactions on NoSQL databases.

2.4. Research questions

The final set of articles was used to answer the following questions:

1) Which is the best NoSQL Databases system for Big Data Pro-

cessing? 2) What is the main structural issue in the Big Data pro-

cessing? 3) Are there any transactions possible on NoSQL Data-

bases?

3. Results

3.1. Article search results

We used our systematic article searching procedure and found 18

articles that have published in reputed journals and conferences be-

tween January 2011 and December 2017. We then scanned all the

articles particularly and identified the key points of each. We search

for two keywords (“NoSQL Database” and “Big Data”) to find of

the articles. The search of the Springer Link and IEEE Xplore elec-

tronics databases using this keyword “NoSQL” retrieved 395 and

568 articles respectively.

Fig. 1: Article Search Results.

We then refined search using the keyword “Big Data”, and this

search resulted in the retrieval of 280 and 245 from the Springer

Link and IEEE Xplore databases, particularly. We then read the ti-

tle, abstract, keyword, and methodology of each article, and based

on our inclusion & exclusion criteria, we selected 10 articles from

the IEEE Xplore database and 8 articles from the Springer Link Da-

tabase for analyzing the present study. The article search result is

summarized in Figure 1. Thus, as a result of this searching proce-

dure, a total of 18 articles that discussed NoSQL database for Big

Data management.

3.2. Descriptive analysis

From the collected of 18 articles, we found 8 articles [8-15] that

were related to general overview of NoSQL databases and Big

Data, 5 articles [16-20] that discussed performance comparison and

evaluation

between different NoSQL databases, 2 articles [21-22] that were

associated to NoSQL data model and classifying NoSQL databases

based on the Consistency, Availability, tolerance of network Parti-

tion of database which is known as CAP theorem, [23] this research

has concentrated on the storage aspect of cloud computing systems

using NoSQL. Other

two [24-25] study reported the SQL query condition transformation

to any NoSQL based database using Espresso Heuristic algorithm

and analyzed the effects of transaction on data consistency & effi-

ciency. However, the outcomes of the 18 articles on NoSQL used

in Big Data processing are summarized in Table 1.

904 International Journal of Engineering & Technology

Table 1: Key Points of Each Article

Refer-

ence Approach Study

[8-15] General overview of NoSQL and Big Data This study provides a recommendation on the suitable databases for specific

type of application requirement & focus on the value chain of Big Data.

[16- 20]

Performance comparison and evaluation on different NoSQL Databases.

Discussion on NoSQL best use cases and NoSQL Databases performance meas- urement.

[21-

22]

Classifying NoSQL Databases according to the CAP

theorem and Data Model.

This study describes the background basics characteristics data model of

NoSQL

[23] DB-Engine Ranking This research has concentrated on the storage aspect of cloud computing sys-

tems, in particular, NoSQL Databases

[24- 25]

Transactional for MongoDB, Riak and NoSQL ‘s SQL condition based on Espresso Heuristic algorithm

To analyzed the effects of transaction on data consistency and efficiency and SQL query condition transformation for any NoSQL Databases

3.3. Research question 1: which is the best NoSQL data-

bases system for big data processing?

Two studies [17, 18] provided a recommendation on the suitable

NoSQL databases for specific type of applications. E.Tang et al.

[18] experimented between five NoSQL(Redis, MongoDB, Couch-

base, Cassandra, HBase) databases based on database type and pop-

ularity, and they created a 4-node cluster for each database in ex-

periment (using YCSB = Yahoo! Cloud Serving Benchmark). They

showed loading time while 100000 records load into databases. Re-

dis got top performance which is 1.31 times quicker than MongoDB

. Then, two column-based databases, Hbase and Cassandra, were

1.86 times and 1.71 times lengthier than Redis. The poor perfor-

mance was presented by Couchbase database [18]. And they were

consider workload execution whereas Redis also displayed best per-

formance with the average time execution 1.1 seconds. Other study

[17] described into their article, If data exists in XML format and

need to achieve high level consistency then MongoDB would pro-

vide the best solution, If data are unstructured and need to high per-

formances then Redis would be the best solution, and If processing

data are a high volume of data then Cassandra would be the best

solution.

3.4. Research question 2: what is the structural issue in

the big data processing?

There is some structural issue of Big Data processing. Important

things are how to effectively collect data and store data, and how to

worth for it. These two things are definitely important for Big Data

processing. Based on the above mentioned point, we found 2 stud-

ies [10, 13] that discussed the Big Data issues and challenges. M.

Chen et al. [13] discussed in their survey, The world of Big Data is

heterogeneous and continuously it changing alongside with IoT.

Most of the data are having also wireless data, but there is no per-

manent gateway to collect data from this wireless data. Moreover,

the study [10] noted that Big Data volume grows so large and di-

verse and all data does not need to store for analysis rather how it

is recognized to deal with it.

3.5. Research question 3: are there any transactions pos-

sible on NoSQL databases?

Of the 18 selected studies, two studies [24, 25] focused on the ACID

transactions. The primary models of NoSQL do not support ACID

transaction but Gonzalez-Aparicio et al. [25] developed a new

transaction system and implemented into NoSQL databases for

MongoDB and Riak for ACID transaction; These transaction sys-

tem allows join operation, provide high scalability and concurrency

of transaction. Other study [24] described the SQL syntax convert-

ing into conditional expression of a NoSQL database.

4. Discussion

We studied particularly the selected 18 articles on “NoSQL & Big

Data related technologies” for the analysis of the present processes

to identify future research in the area. The current study presents a

summary of the data found in the literature that focused on perfor-

mance, strengths and weakness of NoSQL databases for Big Data

Processing. We summarized each articles of this paper are pre-

sented as follows:

4.1. Characteristics of NoSQL databases

Most of the traditional database system are based on transactions.

These transactional features are also familiar as ACID (Atomicity,

Consistency, Isolation, Durability) [26]. However, Big transac-

tional process does not work properly with ACID system [27].

Hence, ACID system shown to be a problem in different distributed

systems that are not fully solvable. Therefore, Eric Brewer [28] in-

troduced the CAP theorem (Figure 1) which is more efficient in dif-

ferent distributed systems. But, later the study [26] noted that the

CAP theorem is accepted only two attributes among the three re-

quirements (AP, CP, CA) for Big Data processing at a time. The

more details are following:

• Available and Partition-Tolerant (AP): Achieve "eventual consistency" through reiteration and authentication. Exam-

ple: Voldemort, Couch DB, Cassandra etc.

• Consistent and Partition-Tolerant (CP): CP system have trou- ble with availability whereas keeping data consistent across

partitioned nodes. Example: MongoDB, Redis, BigTable etc.

• Consistent and Available (CA): CA system have problem with partitions and typically deal with replication. Example:

Vertica, MySQL etc.

Fig. 2: Illustration of the CAP Theorem {Source: [29]}.

By the above discussion, it is very important to know about The

CAP theorem for designing any distributed system. For example,

when transactional and ACID issues are coming in NoSQL data-

base, there is no other option without CAP theorem. Later, Gonza-

lez-Aparicio et al [25] developed a new transaction system using

three components: i) Transmission Processing Engine (TPE), ii)

Data Management Store (DMS), and iii) Times Stamp Manager

(TSM). Also, they implemented their developed technique into

NoSQL databases for MongoDB and Riak; where TPE allows join

operation which are not previously supported in NoSQL, whereas

DMS and TSM are applied in order to provide high scalability and

concurrency of transactions. Other study [24] described the Es-

presso heuristic algorithm for converting SQL syntax to a condi-

tional expression of a specific NoSQL Database (MongoDB). How-

ever, some of NoSQL databases support ACID transaction, such as

International Journal of Engineering & Technology 905

Mark Logic [30] is a solution that works like relational and NoSQL

databases.

4.2. Comparison of NoSQL databases

In this present study, we provide evaluation of four categories

NoSQL databases including document based (MongoDB, Couch-

Base), column based (Cassandra, HBase), key value base (Redis)

and graph based (Neoj4). Moreover, MongoDB, CouchBase, Cas-

sandra, HBase, Redis are the most prominent NoSQL databases that

are evaluated using YCSB [16, 18]. Surya et al. [16] showed their

experiment for MongoDB, CouchBase, Cassandra, HBase using

YCSB, where Cassandra had a better performance than others while

the execution of workload is 50% read and 50% write. They also

found that HBase had a better performance with a small datasets of

database, whereas MongoDB had the best performance while work-

ing with 100% read and 100% blind write [16]. EnqingTang et al.

[18] described their experiment for performance measurement be-

tween different NoSQL databases including Radis, MongoDB,

CouchBase using YCSB and noted that Redis is particularly appro-

priate for loading and executing workloads for small datasets, but

Redis have poor performance for the issues of vast amount of da-

tasets. The study [8] worked with various NoSQL databases includ-

ing Redis, MongoDB, CouchBase and noted that Redis is the best

suited for the analysis of small amount of datasets in order to get

high performance, and for processing large amount of data Mon-

goDB is the best choice, whereas CouchBase is better for fault-tol-

erant database environment. However, CoucheBase is applied for

working concurrently read and write operation in large scale data

sets but performance is not good enough, in this case it is recom-

mended to apply Cassandra [17].

4.3. Big data processing using NoSQL

Generally, Big Data is an issue when the size of the dataset crosses

the ability of existing software in order to analyze dataset including

data collecting, processing, retrieving and managing. According to

the study of [31] the Big Data frequently described using 5 Vs( Vol-

ume, Velocity, Variety, Veracity, Value) are following:

• Volume (great volume): The large amount of data sets are created in every second. As a result, data sets are used to in-

crease time over time. This increasingly makes data sets too

large in order to store and analyses for traditional database

technology. But, considering the Big Data technology, we are

able to store and use these kind of large data sets with the

help of distributed systems, where parts of the data is stored

in different locations and brought together by software.

• Velocity (rapid procreation): Velocity refers to the speed at which new data is generated and the speed at which data

moves around. Big data technology allows us to analyze the

data while it is being generated, even without putting it into

databases.

• Variety (various types): With the Big Data technology, we are able to join differed types of data (structured and unstruc-

tured) including messages, social media conversations, pho-

tos, sensor data, video or voice recordings and bring them to-

gether with more traditional, structured data.

• Veracity: It refers to the messiness or constancy of the data. With many forms of big data, quality and accuracy are less

controllable (just think of Twitter posts with hash tags, ab-

breviations, typos and colloquial speech as well as the relia-

bility and accuracy of content) but Big Data and analytics

technology allows us to work with these type of data. The

volumes often make up for the lack of quality or accuracy.

• Value (huge value but very low density): Value is also im- portant to take into account when looking at Big Data. It is

well and good having access to Big Data but unless turns it

into a value otherwise it is useless. It is noted that businesses

make a business case for any attempt to collect and leverage

big data. It is so easy to fall into the buzz trap and embark on

Big Data initiatives without a clear understanding of costs

and benefits.

Moreover, the study [32] added another 2 Vs (Variability,

Visualization) with the above noted 5V’s are following:

• Variability: Variability is different from variety. For exam- ple, a coffee shop may offer 6 different blends of coffee, but

if we get the same blend every day and it tastes different

every day, that is variability. The same is true for data sets, if

the meaning is constantly changing, it can have a huge impact

on data homogenization.

• Visualization: Visualization is complex in today’s world. Us- ing charts and graphs to visualize huge amounts of complex

data is much more effective in conveying meaning than

spreadsheets and reports chock-full of numbers and formulas.

However, the amount of data has been generating more and more

and therefore data sets analysis become more complex. This chal-

lenge is not only collecting and managing vast amount of data, it

also more challenging to extract valuable data [33]. Hence, extract-

ing valuable data is important issue that need to process by four

phases are presented in Figure 4.

Fig. 3: Big Data Life Cycle.

In the generation phase of Big Data processing, various type of raw

data sets are created by different sources. The second phase is ac-

quisition that involve for data collecting and pre-processing in order

to transmit the data sets as the Big Data to the storage phase. Gen-

erally, storage phase is used to store the Big Data using Distributed

file system (DFS). Recently, there are different types of DFS avail-

able for big data processing such as HDFS, GFS, and TFS. Finally,

the Big Data production is the most important for analysis of Big

Data sets which is used to perform through batch-based (Map-Re-

duce), BSP-based, or stream based data processing techniques [34].

However, real-time analysis and small data sets analysis are com-

plex in Hadoop Distributed System. Thus, the real time and non-

files data set analysis is used to perform through NoSQL database.

Hence, to handle the vast amount of data sets, it is require to con-

sider some issues and challenges are following:

• Extremely difficult to effectively collecting and processing of big data.

• If data volume grows so large and diverse, then there is no particular technique to deal with it.

• Slower Decision making, automatic data mining process will be more improved.

• To collecting wireless data, there is no stable gateway. In summary, we highlight research directions and challenges in re-

lation to Big Data processing and storage management system by

NoSQL. Which is emerging impact of Big Data analysis. In NoSQL

databases, transactional issue into NoSQL database and structural

gap between cloud infrastructures can be more improved. Here, we

describe major databases features that require further research in

terms of Big Data management.

5. Conclusion

In this paper, we have described NoSQL database in Big Data tech-

nologies. We categorized different NoSQL databases, particularly

CAP theorem and then we discussed about the Big Data life cycle.

Moreover, we have discussed the strengths, weakness, comparison

and evaluation on various NoSQL databases. The pointing out from

our discussion would be helpful to the business leader for selecting

an appropriate NoSQL database in order to store and manage the

906 International Journal of Engineering & Technology

Big Data. In addition, we have focused research directions and chal-

lenges in relation to the Big Data in storage and management sys-

tem through NoSQL. Although, several techniques have been ap-

plied for Big Data processing through NoSQL, their use for real-

time data mining process and to extract estimated valuable infor-

mation may still be compromised by the factor highlighted in the

present study.

6. Competing interest

The authors declared that they have no competitive interest

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