DBMS > BoltDB vs. Cubrid vs. DataFS vs. GridGain

System Properties Comparison BoltDB vs. Cubrid vs. DataFS vs. GridGain

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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

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Primary database model

Key-value store

Relational DBMS

Object oriented DBMS

Key-value store
Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

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

Website

github.com/boltdb/bolt

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

newdatabase.com

www.gridgain.com

Technical documentation

cubrid.org/manuals

dev.mobiland.com/Overview.xsp

www.gridgain.com/docs/index.html

Developer

CUBRID Corporation, CUBRID Foundation

Mobiland AG

GridGain Systems, Inc.

Initial release

2013

2008

2018

2007

Current release

11.0, January 2021

1.1.263, October 2022

GridGain 8.5.1

License

Commercial or Open Source

Open Source

MIT License

Open Source

Apache Version 2.0

commercial

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

Java, C++, .Net

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Windows

Windows

Linux
OS X
Solaris
Windows

Data scheme

schema-free

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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

.NET Client API
Proprietary client DLL
WinRT client

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

Supported programming languages

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

.Net
C
C#
C++
VB.Net

C#
C++
Java
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

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

Triggers

yes

no, except callback-events from server when changes happened

yes (cache interceptors and events)

Partitioning methods

Methods for storing different data on different nodes

none

Proprietary Sharding system

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Windows-Profile

Security Hooks for custom implementations

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