DBMS > GridDB vs. Oracle Berkeley DB vs. Percona Server for MongoDB vs. VelocityDB

System Properties Comparison GridDB vs. Oracle Berkeley DB vs. Percona Server for MongoDB vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

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

Widely used in-process key-value store

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

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

Primary database model

Time Series DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

Graph DBMS
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	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

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

Website

griddb.net

www.oracle.com/database/technologies/related/berkeleydb.html

www.percona.com/mongodb/software/percona-server-for-mongodb

velocitydb.com

Technical documentation

docs.griddb.net

docs.oracle.com/cd/E17076_05/html/index.html

docs.percona.com/percona-distribution-for-mongodb

velocitydb.com/UserGuide

Developer

Toshiba Corporation

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Percona

VelocityDB Inc

Initial release

2013

1994

2015

2011

Current release

5.1, August 2022

18.1.40, May 2020

3.4.10-2.10, November 2017

7.x

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

commercial license available

Open Source

GPL Version 2

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, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Any that supports .NET

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

proprietary protocol using JSON

.Net

Supported programming languages

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net

Server-side scripts

Stored procedures

JavaScript

Triggers

yes

only for the SQL API

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

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

via In-Memory Engine

yes

User concepts

Access control

Access rights for users can be defined per database

Access rights for users and roles

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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