DBMS > FatDB vs. HugeGraph vs. IBM Db2 warehouse vs. mSQL vs. PostgreSQL

System Properties Comparison FatDB vs. HugeGraph vs. IBM Db2 warehouse vs. mSQL vs. PostgreSQL

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

A fast-speed and highly-scalable Graph DBMS

Cloud-based data warehousing service

mSQL (Mini SQL) is a simple and lightweight RDBMS

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Document store
Key-value store

Graph DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	645.54
Rank	#4	Overall
	#4	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

www.ibm.com/products/db2/warehouse

hughestech.com.au/products/msql

www.postgresql.org

Technical documentation

hugegraph.apache.org/docs

www.postgresql.org/docs

Developer

FatCloud

Baidu

IBM

Hughes Technologies

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2012

2018

2014

1994

1989

1989: Postgres, 1996: PostgreSQL

Current release

0.9

4.4, October 2021

16.3, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java

Server operating systems

Windows

Linux
macOS
Unix

hosted

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

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

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.

Import/export of XML data possible

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

Via inetgration in SQL Server

yes

A subset of ANSI SQL is implemented

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

yes

standard with numerous extensions

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

Java API
RESTful HTTP API
TinkerPop Gremlin

.NET Client API
JDBC
ODBC
OLE DB

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

Groovy
Java
Python

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

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

yes

via applications

asynchronous Gremlin script jobs

PL/SQL, SQL PL

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

none

partitioning by range, list and (since PostgreSQL 11) by hash

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

depending on used storage backend, e.g. Cassandra and HBase

yes

none

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Eventual Consistency

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

Can implement custom security layer via applications

Users, roles and permissions

fine grained access rights according to SQL-standard

More information provided by the system vendor

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