DBMS > GridDB vs. HugeGraph vs. Interbase vs. mSQL vs. OrigoDB

System Properties Comparison GridDB vs. HugeGraph vs. Interbase vs. mSQL vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

Scalable in-memory time series database optimized for IoT and Big Data

A fast-speed and highly-scalable Graph DBMS

Light-weight proven RDBMS

originally from Borland

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Primary database model

Time Series DBMS

Graph DBMS

Relational DBMS

Document store
Object oriented DBMS

Secondary database models

Key-value store
Relational DBMS

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

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	4.61
Rank	#72	Overall
	#39	Relational DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

griddb.net

github.com/hugegraph
hugegraph.apache.org

www.embarcadero.com/products/interbase

hughestech.com.au/products/msql

origodb.com

Technical documentation

docs.griddb.net

hugegraph.apache.org/docs

docs.embarcadero.com/products/interbase

origodb.com/docs

Developer

Toshiba Corporation

Baidu

Embarcadero

Hughes Technologies

Robert Friberg et al

Initial release

2013

2018

1984

1994

2009

under the name LiveDB

Current release

5.1, August 2022

0.9

InterBase 2020, December 2019

4.4, October 2021

License

Commercial or Open Source

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux

Linux
macOS
Unix

Android
iOS
Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

User defined using .NET types and collections

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.

export as XML data possible

can be achieved using .NET

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

JDBC
ODBC

.NET Client API
HTTP API
LINQ

Supported programming languages

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

Groovy
Java
Python

.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Interbase procedure and trigger language

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

depending on used storage backend, e.g. Cassandra and HBase

Interbase Change Views

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

edges in graph

yes

depending on model

Transaction concepts

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

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

Multiversion concurreny control

yes

Durability

Support for making data persistent

yes

Write ahead log

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users can be defined per database

Users, roles and permissions

fine grained access rights according to SQL-standard

Role based authorization

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

Market metrics

GitHub trending repository
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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