DBMS > IBM Cloudant vs. JaguarDB vs. MarkLogic vs. SQLite vs. XTDB

System Properties Comparison IBM Cloudant vs. JaguarDB vs. MarkLogic vs. SQLite vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

Performant, highly scalable DBMS for AI and IoT applications

Operational and transactional Enterprise NoSQL database

Widely used embeddable, in-process RDBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store

Key-value store
Vector DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Document store

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#15	Vector DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.ibm.com/products/cloudant

www.jaguardb.com

www.progress.com/marklogic

www.sqlite.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

cloud.ibm.com/docs/Cloudant

www.jaguardb.com/support.html

www.progress.com/marklogic/documentation

www.sqlite.org/docs.html

www.xtdb.com/docs

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

DataJaguar, Inc.

MarkLogic Corp.

Dwayne Richard Hipp

Juxt Ltd.

Initial release

2010

2015

2001

2000

2019

Current release

3.3 July 2023

11.0, December 2022

3.46.0 (23 May 2024), May 2024

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

GPL V3.0

commercial

restricted free version is available

Open Source

Public Domain

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Erlang

C++

the server part. Clients available in other languages

C++

Clojure

Server operating systems

hosted

Linux

Linux
OS X
Windows

server-less

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Schema can be enforced

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

yes, extensible-data-notation format

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

yes

SQL92

yes

SQL-92 is not fully supported

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

HTTP REST
JDBC

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Clojure
Java

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

yes

via XQuery or JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication

yes

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

atomic operations within a document possible

ACID

can act as a resource manager in an XA/JTA transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, with Range Indexes

yes

User concepts

Access control

Access rights for users can be defined per database

rights management via user accounts

Role-based access control at the document and subdocument levels

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