DBMS > Faircom EDGE vs. GigaSpaces vs. H2 vs. JanusGraph vs. STSdb

System Properties Comparison Faircom EDGE vs. GigaSpaces vs. H2 vs. JanusGraph vs. STSdb

Editorial information provided by DB-Engines

Name

Description

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

A Graph DBMS optimized for distributed clusters

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

Key-value store
Relational DBMS

Document store
Object oriented DBMS

Values are user defined objects

Relational DBMS

Graph DBMS

Key-value store

Secondary database models

Graph DBMS
Search engine

Spatial DBMS

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

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	1.03
Rank	#188	Overall
	#32	Document stores
	#6	Object oriented DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Website

www.faircom.com/products/faircom-edge

www.gigaspaces.com

www.h2database.com

janusgraph.org

github.com/STSSoft/STSdb4

Technical documentation

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

docs.gigaspaces.com/latest/landing.html

www.h2database.com/html/main.html

docs.janusgraph.org

Developer

FairCom Corporation

Gigaspaces Technologies

Thomas Mueller

Linux Foundation; originally developed as Titan by Aurelius

STS Soft SC

Initial release

1979

2000

2005

2017

2011

Current release

V3, October 2020

15.5, September 2020

2.2.220, July 2023

0.6.3, February 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2; Commercial licenses available

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache 2.0

Open Source

GPLv2, commercial license 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++

Java, C++, .Net

Java

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
macOS
Solaris
Windows

All OS with a Java VM

Linux
OS X
Unix
Windows

Windows

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

primitive types and user defined types (classes)

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

XML can be used for describing objects metadata

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

SQL-99 for query and DML statements

yes

APIs and other access methods

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

.NET Client API

Supported programming languages

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

.Net
C++
Java
Python
Scala

Java

Clojure
Java
Python

C#
Java

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Java Stored Procedures and User-Defined Functions

yes

Triggers

yes

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

none

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

With clustering: 2 database servers on different computers operate on identical copies of a database

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Map-Reduce pattern can be built with XAP task executors

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

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

across SQL and NoSQL

yes

yes, multi-version concurrency control (MVCC)

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

User concepts

Access control

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Role-based access control

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

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