DBMS > Cubrid vs. JanusGraph vs. Microsoft SQL Server vs. Oracle Berkeley DB vs. Oracle NoSQL

System Properties Comparison Cubrid vs. JanusGraph vs. Microsoft SQL Server vs. Oracle Berkeley DB vs. Oracle NoSQL

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

Microsofts flagship relational DBMS

Widely used in-process key-value store

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store
Key-value store
Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Website

cubrid.com (korean)
cubrid.org (english)

janusgraph.org

www.microsoft.com/en-us/sql-server

www.oracle.com/database/technologies/related/berkeleydb.html

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

cubrid.org/manuals

docs.janusgraph.org

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/cd/E17076_05/html/index.html

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

CUBRID Corporation, CUBRID Foundation

Linux Foundation; originally developed as Titan by Aurelius

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Oracle

Initial release

2008

2017

1989

1994

2011

Current release

11.0, January 2021

0.6.3, February 2023

SQL Server 2022, November 2022

18.1.40, May 2020

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

commercial license available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Cloud-based only

Only available as a cloud service

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Implementation language

C, C++, Java

Java

C++

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

Linux
Windows

Linux
OS X
Unix
Windows

Linux
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Solaris SPARC/x86

Data scheme

yes

schema-free

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

RESTful HTTP API

Supported programming languages

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

Clojure
Java
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

yes, but depending on the SQL-Server Edition

Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Access rights for users and roles

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