DBMS > Machbase Neo vs. RDF4J vs. STSdb vs. Vitess

System Properties Comparison Machbase Neo vs. RDF4J vs. STSdb vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

TimeSeries DBMS for AIoT and BigData

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

machbase.com

rdf4j.org

github.com/STSSoft/STSdb4

vitess.io

Technical documentation

machbase.com/dbms

rdf4j.org/documentation

vitess.io/docs

Developer

Machbase

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

STS Soft SC

The Linux Foundation, PlanetScale

Initial release

2013

2004

2011

2013

Current release

V8.0, August 2023

4.0.8, September 2015

15.0.2, December 2022

License

Commercial or Open Source

commercial

free test version available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPLv2, commercial license available

Open Source

Apache Version 2.0, commercial licenses 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
macOS
Windows

Linux
OS X
Unix
Windows

Windows

Docker
Linux
macOS

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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 query language

yes

with proprietary extensions

APIs and other access methods

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

.NET Client API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Java
PHP
Python

C#
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

volatile and lookup table

yes

User concepts

Access control

simple password-based access control

Users with fine-grained authorization concept

no user groups or roles

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