DBMS > HEAVY.AI vs. RDF4J vs. SiriDB vs. SpatiaLite

System Properties Comparison HEAVY.AI vs. RDF4J vs. SiriDB vs. SpatiaLite

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Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

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

Open Source Time Series DBMS

Spatial extension of SQLite

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	1.56
Rank	#142	Overall
	#3	Spatial DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

rdf4j.org

siridb.com

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

docs.heavy.ai

rdf4j.org/documentation

docs.siridb.com

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

HEAVY.AI, Inc.

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

Cesbit

Alessandro Furieri

Initial release

2016

2004

2017

2008

Current release

5.10, January 2022

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

MIT License

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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

C++ and CUDA

Java

C++

Server operating systems

Linux

Linux
OS X
Unix
Windows

Linux

server-less

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

JDBC
ODBC
Thrift
Vega

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

Java
PHP
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

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

simple rights management via user accounts

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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