DBMS > Bangdb vs. Cubrid vs. JanusGraph vs. mSQL vs. Quasardb

System Properties Comparison Bangdb vs. Cubrid vs. JanusGraph vs. mSQL vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

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

mSQL (Mini SQL) is a simple and lightweight RDBMS

Distributed, high-performance timeseries database

Primary database model

Document store
Graph DBMS
Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

bangdb.com

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

janusgraph.org

hughestech.com.au/products/msql

quasar.ai

Technical documentation

Developer

Sachin Sinha, BangDB

CUBRID Corporation, CUBRID Foundation

Linux Foundation; originally developed as Titan by Aurelius

Hughes Technologies

quasardb

Initial release

2012

2008

2017

1994

2009

Current release

BangDB 2.0, October 2021

11.0, January 2021

0.6.3, February 2023

4.4, October 2021

3.14.1, January 2024

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

free licenses can be provided

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C, C++, Java

Java

C++

Server operating systems

Linux

Linux
Windows

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

integer and binary

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.

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

with tags

SQL

Support of SQL

SQL like support with command line tool

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like query language

APIs and other access methods

Proprietary protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
ODBC

HTTP API

Supported programming languages

C
C#
C++
Java
Python

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

Clojure
Java
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

yes

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

none

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

yes

none

Source-replica replication with selectable replication factor

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

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Eventual Consistency
Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

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

yes

by using LevelDB

In-memory capabilities

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

yes, run db with in-memory only mode

yes

Transient mode

User concepts

Access control

yes (enterprise version only)

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

Cryptographically strong user authentication and audit trail

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