DBMS > EXASOL vs. Graph Engine vs. RDF4J vs. StarRocks

System Properties Comparison EXASOL vs. Graph Engine vs. RDF4J vs. StarRocks

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Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Relational DBMS

Graph DBMS
Key-value store

RDF store

Relational DBMS

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

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	1.04
Rank	#189	Overall
	#88	Relational DBMS

Website

Technical documentation

www.exasol.com/resources

www.graphengine.io/docs/manual

rdf4j.org/documentation

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Exasol

Microsoft

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

The Linux Foundation

since Feb 2023

Initial release

2000

2010

2004

2020

Current release

2.5.3, March 2023

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

.NET and C

Java

C++, Java

Server operating systems

.NET

Linux
OS X
Unix
Windows

Linux

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
MySQL protocol

Supported programming languages

Java
Lua
Python
R

C#
C++
F#
Visual Basic

Java
PHP
Python

Java

Server-side scripts

Stored procedures

user defined functions

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop 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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

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

Role based access control and fine grained access rights

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