NoSQL Document Databases Assessment: Couchbase, CouchDB, and

MongoDB

Inês Carvalho

, Filipe Sá

1,3 b

and Jorge Bernardino

1,2 c

Polytechnic of Coimbra – ISEC, Rua Pedro Nunes, Quinta da Nora, 3030-199 Coimbra, Portugal

CISUC – Centre of Informatics and Systems of University of Coimbra, Pinhal de Marrocos, 3030-290 Coimbra, Portugal

CISeD – Research Centre in Digital Services of Polytechnic of Viseu, Campus Politécnico, 3504-510 Viseu, Portugal

Keywords: NoSQL Document Databases, OSSpal Methodology, Couchbase, CouchDB, MongoDB.

Abstract: NoSQL document databases emerged as an alternative to relational databases to deal with large volumes of

data. In this paper, we assess the top three free and open-source NoSQL document databases: Couchbase,

CouchDB, and MongoDB. Through this analysis, we identify the main characteristics of each database. The

OSSpal methodology, which combines quantitative and qualitative measures to assess open-source software

was used. This methodology defines seven categories: functionality, operational software characteristics,

software technology attributes, documentation, support and service, community and adoption, and

development process. At the end, it is obtained a score that identify which is the best NoSQL document

database.

1 INTRODUCTION

The NoSQL Databases, also known as non-relational

or not only SQL, emerged in the late 2000. NoSQL

databases started as an alternative to relational

databases and began to gain more popularity when the

leading Web 2.0 companies, such as Amazon and

Google, needed to deal with the data volume problem.

These companies created their own databases,

Dynamo and Big Table, which inspire many current

databases (Leavitt, 2010; Elmasri and Navathe,

2016).

NoSQL databases are characterized into four

types (Elmasri and Navathe, 2016):

 Key-Value Store databases;

 Column-Oriented databases;

 Document Store databases;

 Graph databases.

NoSQL databases are based on the BASE

properties (Basically Available, Soft State,

Eventually Consistent), which are characterized by

focusing on the availability of data even in the

presence of multiple failures and little consistency.

https://orcid.org/0000-0002-7981-4263

https://orcid.org/0000-0002-7846-8397

https://orcid.org/0000-0001-9660-2011

Relational databases are known for being based on

ACID properties (Atomic, Consistent, Isolated,

Durable), which are characterized by being consistent

in their transactions and their data (Abramova,

Bernardino, and Furtado, 2014).

Document databases store and organize their data

in the form of document collections as JSON

(JavaScript Object Notation) format rather than basic

columns/rows. Documents from the same collection

must be similar and may have different fields unlike

tables in relational databases. Document databases do

not have a pre-defined structure and do not depend on

each other (Tannir, 2013; Elmasri and Navathe,

2016).

The main objective of this work is to evaluate the

three most popular free and open source NoSQL

document databases: Couchbase, CouchDB, and

MongoDB. This choice is according to the DB-

Engines Ranking (2022). For this, the OSSpal

methodology was used, which allows evaluating

open-source software using qualitative and

quantitative measures.

The OSSpal methodology emerged from the

evolution of the OpenBRR methodology, which is

Carvalho, I., Sá, F. and Bernardino, J.

NoSQL Document Databases Assessment: Couchbase, CouchDB, and MongoDB.

DOI: 10.5220/0011352700003269

In Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022), pages 557-564

ISBN: 978-989-758-583-8; ISSN: 2184-285X

557

considered the best methodology to apply in the

evaluation of open-source platforms (Ferreira, 2018).

This methodology makes it possible to obtain a

classification for each database. Based on this

classification, we intended to know, which is the best

of the three databases.

The rest of this paper is organized as follows.

Section 2 explains the OSSpal methodology. Section

3 describes the three NoSQL document databases.

Section 4 presents the evaluation of the databases

with the application of the OSSpal methodology.

Finally, section 5 presents the conclusions and future

work.

2 OSSpal METHODOLOGY

The OSSpal methodology aims to help organizations

choose free and open-source software. It combines

qualitative and quantitative measures for software

evaluation in several categories, which results in an

accepted value that allows comparing the software

platforms (Ferreira, 2018).

The original version of OSSpal methodology

consists of seven categories (Wasserman et al., 2017;

Oliveira and Bernardino, 2019):

 Functionality: Does the software meet the

average user’s requirements?

 Operational Software Characteristics: Is the

software secure and scalable? Is it easy to

install, configure, deploy, and maintain? How

well does the software perform? Does it have a

pleasant interface, and is it easy for the end-

user to use?

 Support and Services: Does it have commercial

support and, or community support? Are there

people or organizations that can provide

training or consulting services?

 Documentation: Are there tutorials and

documentation for the software?

 Software Technology Attributes: How is the

software architecture? Is the software modular,

portable, flexible, extensible, open, complete,

error-free, and easy to integrate? What is the

quality of software design, code, and testing?

 Community and Adoption: Does the market,

industry, and community adoption of the

software? Is the software community active?

 Development Process: How professional are

the development process and the project

organization?

The assessment process for all categories, except

for the functionality category, consists on four steps:

1. Identify and build a list of software components

to be analyzed;

2. Weights must be assigned to the categories and

measures:

2.1. Assign a percentage of importance to each

category, making a total of 100%;

2.2. For each characteristic within a category, it

is necessary to order and classify the

characteristic according to its importance;

2.3. For each characteristic within a category,

assign a percentage according to its

importance, making a total of 100% of all

the characteristics of a category;

3. Collect data for each characteristic used in each

category and calculate their weight in a range of

1 to 5 (1 – Unacceptable, 2 – Poor, 3 –

Acceptable, 4 – Very Good, 5 – Excellent);

4. Calculate the result based on the qualification of

each category with its weighting factor.

The category Functionality serves to analyze and

evaluate the characteristics that the platforms have or

should have. To evaluate this category, we must

follow these steps:

1. Define the characteristics to be analyzed,

evaluating them from 1 to 3 (from least important

to most important);

2. Rank the characteristics in a cumulative sum

from 1 to 3;

3. Standardize the previous result on a scale of 1 to

5, according to the follow:

 Under 65%, Score = 1 (Unacceptable);

 65% - 80%, Score = 2 (Poor);

 80% - 90%, Score = 3 (Acceptable);

 90% - 96%, Score = 4 (Good);

 Over 96%, Score = 5 (Excellent).

3 NoSQL DOCUMENT

DATABASES

There is currently a wide variety and growing number

of NoSQL Document databases. Document

databases, store and organize their data in the form of

document collections. They were created to be easily

scaled as they grow, and their main advantage is

dealing with unstructured data such as text files,

email, and multimedia files (Elmasri and Navathe,

2016).

We selected the top three free and open source

Document databases according to the DB-Engines

Ranking: MongoDB, Couchbase, and CouchDB

(DB-Engines Ranking, 2022).

DATA 2022 - 11th International Conference on Data Science, Technology and Applications

558

In the following subsections, we describe the main

characteristics, advantages, and weaknesses of each

database.

3.1 Couchbase

Couchbase is a document database developed in C++

by Couchbase Inc in 2011. It is designed for web and

mobile applications. Couchbase is a schema-free

database, where the documents are in JSON format

and stored in buckets. A bucket is a collection of

documents. Couchbase has their own query language,

which is N1QL. Couchbase has three versions:

Couchbase Server, Couchbase Mobile, and

Developer SDK. Couchbase can be downloaded at

https://www.couchbase.com

In Figure 1, we can see the interface of

Couchbase. It offers access to the features of the

database, such as queries and indexes, and it shows

the performance of the database.

Couchbase Server is organized into a set of

services managed by the Couchbase Server cluster.

The services are the Eventing, Indexing, Full-Text

Search, Analytics, Mobile, Data, and Query.

Couchbase has a clustered architecture. The

cluster consists in a group of nodes, using a peer-to-

peer topology, where services are run and managed

on each node. Components of a Couchbase node

include the cluster manager and, optionally, the data,

query, index, analytics, search, and other services.

There are also the underlying managed cache and

storage components (Couchbase, 2020).

Figure 1: Couchbase Interface. Source: Couchbase Inc

(2022).

Nodes can be added or removed through a

rebalance process that redistributes the data evenly

across all nodes. This process is done online and

requires no system downtime. Couchbase supports

live-cluster topology. This ability to map services

tosets of nodes is called Multi-Dimensional Scaling

(MDS) (Hubail et al., 2019; Couchbase Inc, 2022).

In Figure 2, we can see that, the Couchbase

cluster consists of one or more instances of

Couchbase Server, each running on an independent

node. Data and services are shared across the cluster.

The application servers communicate with the cluster

overall, but because they are also aware of the

individual node topology, they can adapt as needed

(Couchbase, 2020).

The cluster manager supervises server

configuration and interaction between servers. It

manages replication and rebalancing operations in

Couchbase. The cluster manager executes locally on

each cluster node, but it elects a cluster-wide

orchestrator node to oversee cluster conditions and

execute cluster management functions. If the

orchestrator node crashes, existing nodes will detect

that it is no longer available and will elect a new

orchestrator immediately (Couchbase, 2020).

Figure 2: Couchbase Server Cluster. Source: Couchbase

(2020).

The main advantages and characteristics of

Couchbase are (Couchbase Inc, 2022; DB-Engines

Ranking, 2022):

 Supports multiple programming languages,

such as .Net, C, Go, Java, JavaScript, PHP,

Python, Ruby, and Scala;

 Provides triggers, secondary indexes, server-

side scripts, CRUD operations, and

MapReduce;

 It supports horizontal and vertical scaling;

 Uses MVCC (MultiVersion Concurrency

Control);

 It supports master-master replication and

master-slave replication;

 Does have a SQL-like query language (N1QL).

NoSQL Document Databases Assessment: Couchbase, CouchDB, and MongoDB

559

The main weaknesses of Couchbase are

(Couchbase Inc, 2022; DB-Engines Ranking, 2022):

 It is not being ACID compliant;

 Indexing takes up a lot of RAM.

3.2 CouchDB

CouchDB is a document database developed in

Erlang by Apache Software Foundation in 2005.

CouchDB is ideal for web applications that can

handle a large amount of data. It is suitable for CRM

(Customer Relationship Management) and CMS

(Content Management System) (Nayak, Poriya and

Poojary, 2013; CouchDB, 2022).

CouchDB is a schema-free database that provides

a built-in web application called FULTON, which can

be used for administration. CouchDB can be

downloaded at https://couchdb.apache.org/. In Figure

3, we can see the CouchDB interface. It gives access

to the database features and shows the existed

databases and a few details about them.

Figure 3: CouchDB Interface. Source: JavaPoint (2021).

In CouchDB, documents are stored in JSON

format and JavaScript is used as a query language. It

uses multi-version concurrency control (MVCC),

which allows simultaneous access to multiple users.

When the network connection is not possible to

establish, CouchDB keeps working (Anderson,

Lehanardt, and Slater, 2010; CouchDB, 2022).

CouchDB design is based on web architecture

and the concepts of resources, methods, and

representations. In Figure 4, we can see that all

clients communicate with the server by making an

HTTP request and receive either JSON or HTTP

responses. The HTTP request is used to extract data

from documents. The CouchDB is based on a B-tree

and the data is accessed by keys or key ranges which

map directly to the underlying B-tree operations. B-

trees are a generalization of binary search trees. It is

a tree data structure, which stores data and allows

searches, sequential access, insertions, and deletions

(Manyam et al., 2012; CouchDB, 2022).

Figure 4: The architecture of CouchDB. Source: Manyam

et al (2012).

The main advantages and characteristics of

CouchDB are (CouchDB, 2022; DB-Engines

Ranking, 2022):

 It supports horizontal scaling;

 Provides triggers, secondary indexes, server-

side scripts, CRUD operations, and

MapReduce:

 Supports multiple programming languages,

such as C, C#, Erlang, Java, JavaScript, PHP,

PL/SQL, Python, Ruby, Haskell, and more;

 Uses master-master replication;

 Ideal for web applications;

 It can handle a large amount of data.

The main weaknesses of CouchDB are

(CouchDB, 2022; DB-Engines Ranking, 2022):

 Does not have an SQL-like query language;

 Views are temporary;

 Does not have support for ad-hoc queries.

3.3 MongoDB

MongoDB is a document database developed in C++

by 10gen in 2009. It is used for content management

systems, archiving, real-time analytics, and mobile

applications. MongoDB has four versions: Atlas, a

multi-cloud database platform; Enterprise Advanced;

Community Edition, and Realm, which provides data

services for mobile and web. MongoDB offers the

GUI (Graphical User Interface), the MongoDB

Compass, and a built-in web application called

Mongo Express. MongoDB can be downloaded at

https://www-mongodb.com.

In Figure 5, we can see the interface of MongoDB

Compass, where it is possible to create, change and

delete the databases and collections. Also, is possible

to explore and manipulate data, create queries,

aggregations pipelines, views, indexes, build schema

validation rules, and more (MongoDB, 2021).

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Figure 5: MongoDB Compass Interface. Source: Fiori

(2021).

MongoDB is a schema-free database that

provides the MongoDB Query Language. It stores

data in JSON-like files (BSON), which is a binary

format at JSON, and it is considered more efficient in

storage space.

MongoDB uses sharding that is a method for

distributing or partitioning data across multiple

machines and enables horizontal scaling. MongoDB

native sharding gives the following options

(MongoDB, 2021a):

 Ranged sharding: involves dividing data into

ranges based on the shard key values. Each

chunk is then assigned a range based on the

shard key values;

 Hashed sharding: this option guarantees a

uniform distribution of writes across shards.

The hashed sharding involves computing a

hash of the shard key fields value. Then, each

chunk is then assigned a range based on the

hashed shard key values;

 Zoned sharding: on sharded clusters, it is

possible to create zones of sharded data based

on the shard key, and associate each zone with

one or more shards in the cluster. A shard can

associate with any number of zones. MongoDB

migrates chunks covered by a zone only to

those shards associated with the zone. Each

zone covers one or more ranges of shard key

values.

In Figure 6, we can see the MongoDB cluster

architecture. The main core components in

MongoDB architecture are (Edward and Sabharwal,

2015; MongoDB, 2021):

 Mongod: it handles with all the data requests, it

manages the data format, and performs

operations for background management;

 Mongos: it is used in sharding. It acts as a

routing service that processes queries from the

application layer and determines where in the

sharded cluster the requested data is located;

 Mongo: is the interactive MongoDB Shell.

Figure 6: MongoDB cluster architecture. Source: Edward

and Sabharwal (2015).

The main advantages and characteristics of

MongoDB are (MongoDB, 2021; DB-Engines

Ranking, 2022):

 It has a flexible structure;

 Very easily scalable, it offers vertical,

horizontal, and tiered scaling;

 It has an amicable interface and provides a

GUI;

 It is ACID compliant;

 Provides indexing, ad-hoc queries, CRUD

operations, and MapReduce;

 Aggregation pipeline that allows for

transformation and analyzis of data;

 Provides native drivers for programming

languages and frameworks;

 MongoDB replica sets enable the creation of up

to fifty copies of data, which can be stored

across multiple separate nodes, data centers,

and geographic regions;

 Through native sharding, MongoDB allows the

scale of the database as the applications grow

across multiple nodes to handle write-intensive

workloads and growing data sizes;

 MongoDB sharding is automatic and built into

the database;

 MongoDB architecture is based on four

essentials: availability, workload isolation,

scalability, and data locality;

 It provides a replica set and a failover

mechanism.

The main weaknesses of MongoDB are the

following (MongoDB, 2021; DB-Engines Ranking,

2022):

 Does not use triggers, only in the Atlas version;

 Does not have an SQL-like query language;

 Data can easily be eliminated by mistake dues

to the lack of relations;

NoSQL Document Databases Assessment: Couchbase, CouchDB, and MongoDB

561

 Uses master-slave replication which is easier to

lose data;

 Indexing takes up a lot of RAM.

4 DATABASE EVALUATION

USING OSSpal

In this section, we assess the three databases using the

OSSpal methodology. The information for the

metrics was collected on the websites of the

respective tools, books, papers, and some platforms

such as GitHub, Stackshare, Google Scholar, and

Stack Overflow. The first step is to assign weights to

the categories in order of importance. Table 1 shows

the weights assigned to each category.

Table 1: The weight assigned to each category.

Cate

Wei

Functionalit

30%

Operational Software Characteristics 15%

Software Technolo

Attributes 15%

Documentation 15%

ort an

Service 10%

Communit

Adoption 10%

Development Process 5%

The Functionality category is considered the

most important because it gathers the most relevant

characteristics that a NoSQL Document database

should have. Therefore, it received the highest weight

(30%).

The next step is defining and evaluating important

characteristics for NoSQL Document Databases

tools to analyze the functionality category. As shown

in Table 2 a weight is assigned to each feature in the

functionality category according to its relevance (1 –

slightly important, 2 – important, and 3 – very

important).

Table 2: Weight for the characteristics of the functionality

category.

Characteristics Wei

CRUD o

erations 3

Aggregations 3

Triggers 2

Reduce 2

Indexes 2

Functions and procedures 2

hoc queries 1

With most relevance we choose the CRUD

operations, such as create, read, update, and delete.

We also choose as most relevant the aggregations,

i.e., if the databases have aggregation functions, such

as, sum, count, avg, etc. For the next most relevant

features we choose the existence of triggers, the

support of MapReduce functions, indexes, and

functions and procedures. The feature with the least

relevance is the support for ad-hoc queries.

In the second position, we have the Operational

Software Characteristics that bring together features

such as scalability, security, user interface,

replication, sharding, installation, and configuration

of databases. This category received a value of 15%,

as did the categories of Software Technology

Attributes, and Documentation. The category of

Software Technology Attributes includes features

such as the number of bugs, supported APIs,

supported operating system servers, and supported

programming languages. In the Documentation

category, the existence of various types of

documentation is considered, as a well-documented

platform helps with installation, configuration,

maintenance, and usability.

The category Support and Service obtained a

value of 10%. The characteristics of this category are

the existence of commercial and community support.

The Community and Adoption category also

received a value of 10%. It brings characteristics such

as interaction in forums, and adoption by companies.

Finally, the category with the least weight (5%)

was the Development Process. To assess this

category, we considered the number of people who

contributed to the development of the databases on

the GitHub platform.

After assigning the weights to all categories, we

obtain the results for each category (evaluation from

1 to 5), as shown in Table 3.

Table 3: Score obtained by the databases for each category.

Category Couchbase CouchDB MongoDB

Functionality 5.0 3.0 4.0

Operational

Software

Characteristics

4.5 3.6 4.7

Software

Technology

Attributes

4.0 4.6 3.7

Documentatio

2.0 3.0 5.0

Support and

Service

4.3 4.3 5.0

Community

Adoptio

3.0 3.0 5.0

Development

Process

2.0 4.0 5.0

To analyze the features of each category, we used

the following metrics, such as:

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 In the Functionality category, we find out if the

databases supported the features described in

the Table 3, for all the versions that they offer;

 For the Operational Software Characteristics

category, the metrics used were, the number of

security certificates and safety mechanisms

that are provided by each database for the

feature security. The number of documents,

offer by each database website, that provide

information for the installation and

configuration of the database. The number of

sharding techniques supported, the types of

scalability supported, and the types of

replication topologies;

 The metrics used for the Software Technology

Attributes category were the number of

programming languages supported, the number

of operating systems supported, the number of

APIs supported, and the number of bugs that

have been reported for each database on the

GitHub platform;

 To evaluate the Documentation category, we

count the number of papers released from

January 1st, 2022 to April 15th, 2022 on the

Google Scholar;

 For the Support and Service category, we

evaluate the community support using two

metrics. The number of followers at each

database on the Stackshare platform, and the

number of questions asked about each database

on the Stack Overflow platform. And we find

out if the databases offer commercial support;

 For the Community and Adoption category, we

evaluate the number of followers in blogs

related to each database on the Stackshare

platform. And, we count the number of

companies that adopt each database;

 For the Development Process category, we

count the number of persons that have been

contributed to the development of the database

through the GitHub platform.

After analyzing Table 3, it is possible to conclude

that:

 In the Functionality category, the database that

stood out for having the all features, that we

consider most important, is the Couchbase.

Following the MongoDB, that have every

features like Couchbase, but it just supports

triggers on the Atlas version. In last place, we

got CouchDB because it does not support ad-

hoc queries and it just uses indexes on views;

 In the category of Operational Software

Characteristics, MongoDB obtained the

highest score because it is the database with

more certificates and security mechanisms,

supports more types of sharding, and more

types of scalability compared to the others

databases;

 In the category of Software Technology

Attributes, CouchDB obtained the highest

score following Couchbase. And MongoDB

obtained the lowest score, because was the

database with more bugs report on GitHub

platform;

 For the rest of the categories, we concluded that

MongoDB always obtained the highest score

than the other databases, probably because it

has been on the market for the longest time and

has the biggest development community. On

the other hand, Couchbase and CouchDB

obtained, almost, the same scores in the rest of

 the categories. Except for the Development

Process category, CouchDB got ahead of

Couchbase.

After evaluating each category, the final score is

calculated for each database. Each category must be

multiplied by its score and its respective weight

assigned in Table 1. The results are shown in Table 4.

Table 4: OSSpal final score.

Cate

Couchbase CouchDB Mon

oDB

Functionalit

1.50 0.90 1.20

Operational

Software

Characteristics

0.68 0.54 0.71

Software

Technology

Attributes

0.60 0.69 0.56

Documentation 0.30 0.45 0.75

Support and

Service

0.43 0.43 0.50

Community and

Ado

tion

0.30 0.30 0.50

Development

Process

0.10 0.20 0.25

TOTAL 3.91 3.51 4.47

With a score of 4.47, MongoDB was the database

with the highest score with the application of the

OSSpal methodology. Following Couchbase with a

score of 3.91 and CouchDB with a score of 3.51.

NoSQL Document Databases Assessment: Couchbase, CouchDB, and MongoDB

563

5 CONCLUSIONS AND FUTURE

WORK

In this paper, we selected the top three document

databases according to the DB-Engines ranking. We

created a summary table that allowed us to put

together the main characteristics of the databases.

To analyze and evaluate the NoSQL document

databases we use the OSSpal methodology that

allows the evaluation of open-source software.

The application of the OSSpal methodology was

found to be very useful as it made it possible to

compare the various databases in distinct categories.

It also allows us to conclude that the best database

with the highest score was MongoDB.

As future work, we intend to evaluate these

Document databases, MongoDB, Couchbase, and

CouchDB through their performance using the YCSB

benchmark. We also intend to evaluate more

Document NoSQL databases and compare them with

relational databases.

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