DBMS > DataFS vs. JanusGraph vs. OpenEdge vs. Oracle NoSQL

System Properties Comparison DataFS vs. JanusGraph vs. OpenEdge vs. Oracle NoSQL

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

A Graph DBMS optimized for distributed clusters

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

Application development environment with integrated database management system

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Primary database model

Object oriented DBMS

Graph DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	2.02
Rank	#125	Overall
	#12	Graph DBMS

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Website

newdatabase.com

janusgraph.org

www.progress.com/openedge

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

dev.mobiland.com/Overview.xsp

docs.janusgraph.org

documentation.progress.com/output/ua/OpenEdge_latest

docs.oracle.com/en/database/other-databases/nosql-database/index.html

Developer

Mobiland AG

Linux Foundation; originally developed as Titan by Aurelius

Progress Software Corporation

Oracle

Initial release

2018

2017

1984

2011

Current release

1.1.263, October 2022

0.6.3, February 2023

OpenEdge 12.2, March 2020

23.3, December 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

Server operating systems

Windows

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
Solaris
Windows

Linux
Solaris SPARC/x86

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

Typing

predefined data types such as float or date

yes

optional

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

close to SQL 92

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
C
C#
C++
VB.Net

Clojure
Java
Python

Progress proprietary ABL (Advanced Business Language)

C
C#
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Triggers

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

yes

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

horizontal partitioning

since Version 11.4

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

Concurrency

Support for concurrent manipulation of data

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

off heap cache

User concepts

Access control

Windows-Profile

User authentification and security via Rexster Graph Server

Users and groups

Access rights for users and roles

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