DBMS > Bangdb vs. GridDB vs. OpenQM vs. OrigoDB vs. VelocityDB

System Properties Comparison Bangdb vs. GridDB vs. OpenQM vs. OrigoDB vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

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

QpenQM is a high-performance, self-tuning, multi-value DBMS

A fully ACID in-memory object graph database

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Time Series DBMS

Multivalue DBMS

Document store
Object oriented DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Spatial DBMS

Key-value store
Relational DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

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

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

Website

bangdb.com

griddb.net

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

origodb.com

velocitydb.com

Technical documentation

docs.bangdb.com

docs.griddb.net

origodb.com/docs

velocitydb.com/UserGuide

Developer

Sachin Sinha, BangDB

Toshiba Corporation

Rocket Software, originally Martin Phillips

Robert Friberg et al

VelocityDB Inc

Initial release

2012

2013

1993

2009

under the name LiveDB

2011

Current release

BangDB 2.0, October 2021

5.1, August 2022

3.4-12

7.x

License

Commercial or Open Source

Open Source

BSD 3

Open Source

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

Open Source

GPLv2, extended commercial license available

Open Source

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++

C++

Server operating systems

Linux

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
Windows

Any that supports .NET

Data scheme

schema-free

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

numerical, string, blob, geometry, boolean, timestamp

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

SQL92, SQL-like TQL (Toshiba Query Language)

APIs and other access methods

Proprietary protocol
RESTful HTTP API

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

.NET Client API
HTTP API
LINQ

.Net

Supported programming languages

C
C#
C++
Java
Python

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

.Net
Basic
C
Java
Objective C
PHP
Python

.Net

Server-side scripts

Stored procedures

yes

Triggers

yes, Notifications (with Streaming only)

yes

Domain Events

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

yes

horizontal partitioning

client side managed; servers are not synchronized

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate consistency within container, eventual consistency across containers

Immediate Consistency

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

ACID

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

Write ahead log

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Access rights for users can be defined per database

Access rights can be defined down to the item level

Role based authorization

Based on Windows Authentication

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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