DBMS > Cubrid vs. Datomic vs. GridGain vs. VelocityDB

System Properties Comparison Cubrid vs. Datomic vs. GridGain vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

GridGain is an in-memory computing platform, built on Apache Ignite

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Graph DBMS
Object oriented DBMS

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

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

www.datomic.com

www.gridgain.com

velocitydb.com

Technical documentation

cubrid.org/manuals

docs.datomic.com

www.gridgain.com/docs/index.html

velocitydb.com/UserGuide

Developer

CUBRID Corporation, CUBRID Foundation

Cognitect

GridGain Systems, Inc.

VelocityDB Inc

Initial release

2008

2012

2007

2011

Current release

11.0, January 2021

1.0.6735, June 2023

GridGain 8.5.1

7.x

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Java

Java, Clojure

Java, C++, .Net

Server operating systems

Linux
Windows

All OS with a Java VM

Linux
OS X
Solaris
Windows

Any that supports .NET

Data scheme

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

.Net

Supported programming languages

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

Clojure
Java

C#
C++
Java
PHP
Python
Ruby
Scala

.Net

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Transaction Functions

yes (compute grid and cache interceptors can be used instead)

Triggers

yes

By using transaction functions

yes (cache interceptors and events)

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

But extensive use of caching in the application peers

yes (replicated cache)

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Security Hooks for custom implementations

Based on Windows Authentication

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