DBMS > Hyprcubd vs. RDF4J vs. SiteWhere vs. Splice Machine

System Properties Comparison Hyprcubd vs. RDF4J vs. SiteWhere vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Serverless Time Series DBMS

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

M2M integration platform for persisting/querying time series data

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

Primary database model

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

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

hyprcubd.com (offline)

rdf4j.org

github.com/sitewhere/sitewhere

splicemachine.com

Technical documentation

rdf4j.org/documentation

sitewhere1.sitewhere.io/index.html

splicemachine.com/how-it-works

Developer

Hyprcubd, Inc.

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

SiteWhere

Splice Machine

Initial release

2004

2010

2014

Current release

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Common Public Attribution License Version 1.0

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Server operating systems

hosted

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

RDF Schemas

predefined scheme

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

APIs and other access methods

gRPC (https)

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP REST

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java
PHP
Python

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

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on HBase

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on HBase

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

Eventual Consistency

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

token access

Users with fine-grained authorization concept

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

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