DBMS > EDB Postgres vs. Faircom DB vs. gStore vs. Hazelcast

System Properties Comparison EDB Postgres vs. Faircom DB vs. gStore vs. Hazelcast

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Editorial information provided by DB-Engines

Name

Description

The EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

A native Graph DBMS to store and maintain very large RDF datasets.

A widely adopted in-memory data grid

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS
RDF store

Key-value store

Secondary database models

Document store
Spatial DBMS

Document store

JSON support with IMDG 3.12

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

Score	1.91
Rank	#130	Overall
	#60	Relational DBMS

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.14
Rank	#342	Overall
	#34	Graph DBMS
	#16	RDF stores

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Website

www.enterprisedb.com

www.faircom.com/products/faircom-db

en.gstore.cn

hazelcast.com

Technical documentation

www.enterprisedb.com/docs

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

en.gstore.cn/#/enDocs

hazelcast.org/imdg/docs

Developer

EnterpriseDB

FairCom Corporation

Hazelcast

Initial release

2005

1979

2016

2008

Current release

14, December 2021

V12, November 2020

1.2, November 2023

5.3.6, November 2023

License

Commercial or Open Source

commercial

BSD for PostgreSQL-components

commercial

Restricted, free version available

Open Source

BSD

Open Source

Apache Version 2; commercial licenses available

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

ANSI C, C++

C++

Java

Server operating systems

Linux
Windows

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux

All OS with a Java VM

Data scheme

yes

schema free, schema optional, schema required, partial schema,

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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.

yes

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

yes

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

yes

standard with numerous extensions

yes, ANSI SQL with proprietary extensions

SQL-like query language

APIs and other access methods

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

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

HTTP API
SPARQL 1.1

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

C++
Java
JavaScript (Node.js)
PHP
Python

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

user defined functions

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

yes

.Net, JavaScript, C/C++

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

by hash, list or range

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

yes

one or two-phase-commit; repeatable reads; read commited

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

fine grained access rights according to SQL-standard

Fine grained access rights according to SQL-standard with additional protections for files

Users, roles and permissions, Role-Based Access Control (RBAC) supported

Role-based access control

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