DBMS > Datastax Enterprise vs. openGauss vs. RDF4J

System Properties Comparison Datastax Enterprise vs. openGauss vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

DataStax Enterprise (DSE) is the always-on, scalable data platform built on Apache Cassandra and designed for hybrid Cloud. DSE integrates graph, search, analytics, administration, developer tooling, and monitoring into a unified platform.

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

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

Primary database model

Wide column store

Relational DBMS

RDF store

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Search engine
Vector DBMS

Document store
Spatial DBMS

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

Score	4.00
Rank	#66	Overall
	#4	Wide column stores

Score	0.96
Rank	#187	Overall
	#85	Relational DBMS

Score	0.68
Rank	#223	Overall
	#9	RDF stores

Website

www.datastax.com/products/datastax-enterprise

gitee.com/opengauss
opengauss.org

rdf4j.org

Technical documentation

docs.datastax.com

docs.opengauss.org/en
gitee.com/opengauss/docs

rdf4j.org/documentation

Developer

DataStax

Huawei and openGauss community

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

Initial release

2011

2019

2004

Current release

6.8, April 2020

3.0, March 2022

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C, C++, Java

Java

Server operating systems

Linux
OS X

Linux

Linux
OS X
Unix
Windows

Data scheme

schema-free

yes

RDF Schemas

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

SQL-like DML and DDL statements (CQL); Spark SQL

ANSI SQL 2011

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language)
TinkerPop Gremlin

with DSE Graph

JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

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

C
C++
Java

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

horizontal partitioning (by range, list and hash)

none

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware, advanced replication for edge computing

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

consistency level can be individually decided with each write operation

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Access rights for users can be defined per object

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

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