DBMS > DataFS vs. Oracle Rdb vs. Realm vs. Splice Machine

System Properties Comparison DataFS vs. Oracle Rdb vs. Realm vs. Splice Machine

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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 DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

newdatabase.com

www.oracle.com/database/technologies/related/rdb.html

realm.io

splicemachine.com

Technical documentation

dev.mobiland.com/Overview.xsp

www.oracle.com/database/technologies/related/rdb-doc.html

realm.io/docs

splicemachine.com/how-it-works

Developer

Mobiland AG

Oracle, originally developed by Digital Equipment Corporation (DEC)

Realm, acquired by MongoDB in May 2019

Splice Machine

Initial release

2018

1984

2014

Current release

1.1.263, October 2022

7.4.1.1, 2021

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, 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

Java

Server operating systems

Windows

HP Open VMS

Android
Backend: server-less
iOS
Windows

Linux
OS X
Solaris
Windows

Data scheme

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

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

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

.Net
Java

with Android only
Objective-C
React Native
Swift

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

runs within the applications so server-side scripts are unnecessary

yes

Java

Triggers

no, except callback-events from server when changes happened

yes

Change Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

In-Memory realm

yes

User concepts

Access control

Windows-Profile

yes

Access rights for users, groups and roles according to SQL-standard

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