DBMS > ArcadeDB vs. Faircom EDGE vs. JanusGraph vs. Memgraph vs. mSQL

System Properties Comparison ArcadeDB vs. Faircom EDGE vs. JanusGraph vs. Memgraph vs. mSQL

Editorial information provided by DB-Engines

Name

Description

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

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

A Graph DBMS optimized for distributed clusters

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

An open source graph database built for real-time streaming and compatible with Neo4j

mSQL (Mini SQL) is a simple and lightweight RDBMS

Primary database model

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

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

Score	0.10
Rank	#358	Overall
	#48	Document stores
	#38	Graph DBMS
	#52	Key-value stores
	#35	Time Series DBMS

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

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	3.19
Rank	#94	Overall
	#8	Graph DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Website

arcadedb.com

www.faircom.com/products/faircom-edge

janusgraph.org

memgraph.com

hughestech.com.au/products/msql

Technical documentation

docs.arcadedb.com

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

docs.janusgraph.org

memgraph.com/docs

Social network pages

Developer

Arcade Data

FairCom Corporation

Linux Foundation; originally developed as Titan by Aurelius

Memgraph Ltd

Hughes Technologies

Initial release

2021

1979

2017

1994

Current release

September 2021

V3, October 2020

0.6.3, February 2023

4.4, October 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Restricted, free version available

Open Source

Apache 2.0

Open Source

BSL 1.1; commercial license for enterprise edition available

commercial

free licenses can be provided

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

ANSI C, C++

Java

C and C++

Server operating systems

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X
Unix
Windows

Linux

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

Data scheme

schema-free

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

yes

schema-free and schema-optional

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language, no joins

yes

ANSI SQL queries

A subset of ANSI SQL is implemented

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

APIs and other access methods

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

Bolt protocol
Cypher query language

JDBC
ODBC

Supported programming languages

Java

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

Clojure
Java
Python

.Net
C
C++
Elixir
Go
Haskell
Java
JavaScript
PHP
Python
Ruby
Scala

C
C++
Delphi
Java
Perl
PHP
Tcl

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

yes

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

Sharding

dynamic graph partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Synchronous and asynchronous realtime replication based on transaction logs

yes

Multi-source replication using RAFT

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency
Tunable Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

relationship in graphs

yes

when using SQL

yes

Relationships in graphs

yes

relationships in graphs

Transaction concepts

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

ACID

with snapshot isolation

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

with periodic snapshot and write-ahead logging (WAL) of changes

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.

User authentification and security via Rexster Graph Server

Users, roles and permissions

More information provided by the system vendor

Specific characteristics

Memgraph directly connects to your streaming infrastructure so you and your team...
» more

Competitive advantages

Business Source License ensures a future for the Memgraph community MAGE algorithm...
» more

Typical application scenarios

Graph algorithms in bioinformatics Social network analysis Cryptocurrency network...
» more

Licensing and pricing models

You can check out our pricing model and licenses on the company website .
» more

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