DBMS > Faircom EDGE vs. HyperSQL vs. JanusGraph vs. Snowflake vs. TypeDB

System Properties Comparison Faircom EDGE vs. HyperSQL vs. JanusGraph vs. Snowflake vs. TypeDB

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

A Graph DBMS optimized for distributed clusters

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

Cloud-based data warehousing service for structured and semi-structured data

TypeDB is a strongly-typed database with a rich and logical type system and TypeQL as its query language

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS

Relational DBMS

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

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	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.65
Rank	#234	Overall
	#20	Graph DBMS
	#107	Relational DBMS

Website

www.faircom.com/products/faircom-edge

Technical documentation

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

hsqldb.org/web/hsqlDocsFrame.html

docs.janusgraph.org

docs.snowflake.net/manuals/index.html

typedb.com/docs

Developer

FairCom Corporation

Linux Foundation; originally developed as Titan by Aurelius

Snowflake Computing Inc.

Vaticle

Initial release

1979

2001

2017

2014

2016

Current release

V3, October 2020

2.7.2, June 2023

0.6.3, February 2023

2.26.3, January 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

based on BSD license

Open Source

Apache 2.0

commercial

Open Source

GPL Version 3, commercial licenses available

Cloud-based only

Only available as a cloud service

yes

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

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
OS X
Unix
Windows

hosted

Linux
OS X
Windows

Data scheme

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

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

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

yes

ANSI SQL queries

yes

APIs and other access methods

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

HTTP API

JDBC via HTTP
JDBC
ODBC

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

CLI Client
JDBC
ODBC

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (Visualisation software- previously TypeDB Workbase)

Supported programming languages

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

All languages supporting JDBC/ODBC
Java

Clojure
Java
Python

JavaScript (Node.js)
Python

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

Java, SQL

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

none

yes

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

yes

Sharding

by using Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

none

yes

Multi-source replication

by using Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Relationships in graphs

yes

substituted by the relationship feature

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

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.

fine grained access rights according to SQL-standard

User authentification and security via Rexster Graph Server

Users with fine-grained authorization concept, user roles and pluggable authentication

yes

at REST API level; other APIs in progress

More information provided by the system vendor

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