DBMS > Cubrid vs. Lovefield vs. RavenDB vs. RDF4J vs. Sphinx

System Properties Comparison Cubrid vs. Lovefield vs. RavenDB vs. RDF4J vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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

Embeddable relational database for web apps written in pure JavaScript

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	1.31
Rank	#169	Overall
	#78	Relational DBMS

Score	0.32
Rank	#290	Overall
	#132	Relational DBMS

Score	3.01
Rank	#101	Overall
	#17	Document stores

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

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

google.github.io/lovefield

ravendb.net

rdf4j.org

sphinxsearch.com

Technical documentation

cubrid.org/manuals

github.com/google/lovefield/blob/master/docs/spec_index.md

ravendb.net/docs

rdf4j.org/documentation

sphinxsearch.com/docs

Developer

CUBRID Corporation, CUBRID Foundation

Google

Hibernating Rhinos

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

Sphinx Technologies Inc.

Initial release

2008

2014

2010

2004

2001

Current release

11.0, January 2021

2.1.12, February 2017

5.4, July 2022

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

AGPL version 3, commercial license available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

GPL version 2, commercial licence 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

C, C++, Java

JavaScript

Java

C++

Server operating systems

Linux
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
macOS
Raspberry Pi
Windows

Linux
OS X
Unix
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

yes

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

full-text index on all search fields

SQL

Support of SQL

yes

SQL-like query language

via JavaScript builder pattern

SQL-like query language (RQL)

SQL-like query language (SphinxQL)

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Proprietary protocol

Supported programming languages

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

JavaScript

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

in-memory storage is supported as well

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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