DBMS > Cubrid vs. Ehcache vs. Google BigQuery vs. JaguarDB vs. RDF4J

System Properties Comparison Cubrid vs. Ehcache vs. Google BigQuery vs. JaguarDB vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

A widely adopted Java cache with tiered storage options

Large scale data warehouse service with append-only tables

Performant, highly scalable DBMS for AI and IoT applications

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

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key-value store
Vector DBMS

RDF store

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

cubrid.com (korean)
cubrid.org (english)

www.ehcache.org

cloud.google.com/bigquery

www.jaguardb.com

rdf4j.org

Technical documentation

cubrid.org/manuals

www.ehcache.org/documentation

cloud.google.com/bigquery/docs

www.jaguardb.com/support.html

rdf4j.org/documentation

Developer

CUBRID Corporation, CUBRID Foundation

Terracotta Inc, owned by Software AG

Google

DataJaguar, Inc.

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

Initial release

2008

2009

2010

2015

2004

Current release

11.0, January 2021

3.10.0, March 2022

3.3 July 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

GPL V3.0

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C, C++, Java

Java

C++

the server part. Clients available in other languages

Java

Server operating systems

Linux
Windows

All OS with a Java VM

hosted

Linux

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

JCache

RESTful HTTP/JSON API

JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

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

Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

Java
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

yes

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

by using Terracotta Server

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

by using Terracotta Server

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

supports JTA and can work as an XA resource

Since BigQuery is designed for querying data

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using a tiered cache-storage approach

yes

in-memory storage is supported as well

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

rights management via user accounts

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