DBMS > JanusGraph vs. MonetDB vs. mSQL vs. Oracle NoSQL vs. TigerGraph

System Properties Comparison JanusGraph vs. MonetDB vs. mSQL vs. Oracle NoSQL vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

A Graph DBMS optimized for distributed clusters

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

A relational database management system that stores data in columns

mSQL (Mini SQL) is a simple and lightweight RDBMS

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Graph DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

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

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

janusgraph.org

www.monetdb.org

hughestech.com.au/products/msql

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

www.tigergraph.com

Technical documentation

docs.janusgraph.org

www.monetdb.org/Documentation

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

docs.tigergraph.com

Developer

Linux Foundation; originally developed as Titan by Aurelius

MonetDB BV

Hughes Technologies

Oracle

Initial release

2017

2004

1994

2011

2017

Current release

0.6.3, February 2023

Dec2023 (11.49), December 2023

4.4, October 2021

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

commercial

free licenses can be provided

Open Source

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

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix
Windows

FreeBSD
Linux
OS X
Solaris
Windows

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

Linux
Solaris SPARC/x86

Linux

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

optional

yes

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

SQL

Support of SQL

yes

SQL 2003 with some extensions

A subset of ANSI SQL is implemented

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

SQL-like DML and DDL statements

SQL-like query language (GSQL)

APIs and other access methods

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
ODBC

RESTful HTTP API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Clojure
Java
Python

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

C
C++
Delphi
Java
Perl
PHP
Tcl

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

C++
Java

Server-side scripts

Stored procedures

yes

yes, in SQL, C, R

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding via remote tables

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication available in experimental status

none

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

none

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Relationships in graphs

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

ACID

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

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Access rights for users and roles

Role-based access control

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