DBMS > RDF4J vs. SAP SQL Anywhere vs. Splice Machine

System Properties Comparison RDF4J vs. SAP SQL Anywhere vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

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

Primary database model

RDF store

Relational DBMS

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

Score	1.02
Rank	#226	Overall
	#9	RDF stores

Score	5.61
Rank	#74	Overall
	#40	Relational DBMS

Score	0.88
Rank	#236	Overall
	#110	Relational DBMS

Website

rdf4j.org

www.sap.com/products/technology-platform/sql-anywhere.html

splicemachine.com

Technical documentation

rdf4j.org/documentation

help.sap.com/docs/SAP_SQL_Anywhere

splicemachine.com/how-it-works

Developer

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

SAP

formerly Sybase

Splice Machine

Initial release

2004

1992

2014

Current release

17, July 2015

3.1, March 2021

License

Commercial or Open Source

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

RDF Schemas

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

ADO.NET
HTTP API
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java
PHP
Python

C
C#
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

yes, in C/C++, Java, .Net or Perl

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Database mirroring

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

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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 access rights according to SQL-standard

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

More information provided by the system vendor

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