DBMS > BigObject vs. Cachelot.io vs. Kingbase vs. ObjectBox vs. Oracle Berkeley DB

System Properties Comparison BigObject vs. Cachelot.io vs. Kingbase vs. ObjectBox vs. Oracle Berkeley DB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

In-memory caching system

An enterprise-class RDBMS compatible with PostgreSQL and Oracle and widely used in China.

Extremely fast embedded database for small devices, IoT and Mobile

Widely used in-process key-value store

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	0.45
Rank	#262	Overall
	#123	Relational DBMS

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

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

Website

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

Technical documentation

docs.bigobject.io

docs.objectbox.io

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

Developer

BigObject, Inc.

BeiJing KINGBASE Information technologies inc.

ObjectBox Limited

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2015

1999

2017

1994

Current release

V8.0, August 2021

18.1.40, May 2020

License

Commercial or Open Source

commercial

free community edition available

Open Source

Simplified BSD License

commercial

Open Source

Apache License 2.0

Open Source

commercial license available

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 and Java

C and C++

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

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
Linux
OS X

Linux
Windows

Android
iOS
Linux
macOS
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

yes

schema-free

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Standard with numerous extensions

yes

SQL interfaced based on SQLite is available

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Memcached protocol

ADO.NET
gokb
JDBC
kdbndp
ODBC
PDI
PDO
Pro*C
psycopg2
QT

Proprietary native API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

.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

Server-side scripts

Stored procedures

Lua

user defined functions

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning (by range, list and hash) and vertical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

online/offline synchronization between client and server

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

yes

More information provided by the system vendor

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