DBMS > Bangdb vs. Firebase Realtime Database vs. mSQL vs. Oracle Berkeley DB

System Properties Comparison Bangdb vs. Firebase Realtime Database vs. mSQL vs. Oracle Berkeley DB

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Editorial information provided by DB-Engines

Name

Description

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

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

mSQL (Mini SQL) is a simple and lightweight RDBMS

Widely used in-process key-value store

Primary database model

Document store
Graph DBMS
Time Series DBMS

Document store

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Secondary database models

Spatial 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	14.29
Rank	#39	Overall
	#6	Document stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

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

Website

bangdb.com

firebase.google.com/products/realtime-database

hughestech.com.au/products/msql

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

Technical documentation

docs.bangdb.com

firebase.google.com/docs/database

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

Developer

Sachin Sinha, BangDB

Google

acquired by Google 2014

Hughes Technologies

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2012

1994

Current release

BangDB 2.0, October 2021

4.4, October 2021

18.1.40, May 2020

License

Commercial or Open Source

Open Source

BSD 3

commercial

free licenses can be provided

Open Source

commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, C++

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

Server operating systems

Linux

hosted

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

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

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

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

A subset of ANSI SQL is implemented

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

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Proprietary protocol
RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

Java
JavaScript
Objective-C

C
C++
Delphi
Java
Perl
PHP
Tcl

.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

limited functionality with using 'rules'

Triggers

yes, Notifications (with Streaming only)

Callbacks are triggered when data changes

yes

only for the SQL API

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

yes

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

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)

yes, based on authentication and database rules

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