DBMS > JaguarDB vs. Quasardb vs. RDF4J vs. Spark SQL

System Properties Comparison JaguarDB vs. Quasardb vs. RDF4J vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Performant, highly scalable DBMS for AI and IoT applications

Distributed, high-performance timeseries database

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Key-value store
Vector DBMS

Time Series DBMS

RDF store

Relational DBMS

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

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#14	Vector DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

www.jaguardb.com/support.html

doc.quasar.ai/master

rdf4j.org/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

DataJaguar, Inc.

quasardb

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

Apache Software Foundation

Initial release

2015

2009

2004

2014

Current release

3.3 July 2023

3.14.1, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

GPL V3.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

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

C++

the server part. Clients available in other languages

C++

Java

Scala

Server operating systems

Linux

BSD
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC

Supported programming languages

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

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

Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

by using LevelDB

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

Transient mode

User concepts

Access control

rights management via user accounts

Cryptographically strong user authentication and audit trail

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