DBMS > Faircom EDGE vs. HugeGraph vs. InfinityDB vs. mSQL vs. ToroDB

System Properties Comparison Faircom EDGE vs. HugeGraph vs. InfinityDB vs. mSQL vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A fast-speed and highly-scalable Graph DBMS

A Java embedded Key-Value Store which extends the Java Map interface

mSQL (Mini SQL) is a simple and lightweight RDBMS

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Key-value store
Relational DBMS

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.00
Rank	#378	Overall
	#57	Key-value stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Website

www.faircom.com/products/faircom-edge

github.com/hugegraph
hugegraph.apache.org

boilerbay.com

hughestech.com.au/products/msql

github.com/torodb/server

Technical documentation

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

hugegraph.apache.org/docs

boilerbay.com/infinitydb/manual

Developer

FairCom Corporation

Baidu

Boiler Bay Inc.

Hughes Technologies

8Kdata

Initial release

1979

2018

2002

1994

2016

Current release

V3, October 2020

0.9

4.0

4.4, October 2021

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

AGPL-V3

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

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
macOS
Unix

All OS with a Java VM

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

All OS with a Java 7 VM

Data scheme

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

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

yes

string, integer, double, boolean, date, object_id

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

Secondary indexes

yes

also supports composite index and range index

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

ANSI SQL queries

A subset of ANSI SQL is implemented

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

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

JDBC
ODBC

Supported programming languages

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

Groovy
Java
Python

Java

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

asynchronous Gremlin script jobs

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

yes

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

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

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

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

none

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

edges in graph

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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

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

Users, roles and permissions

Access rights for users and roles

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