DBMS > Firebase Realtime Database vs. Google Cloud Bigtable vs. MySQL vs. Oracle Berkeley DB vs. WakandaDB

System Properties Comparison Firebase Realtime Database vs. Google Cloud Bigtable vs. MySQL vs. Oracle Berkeley DB vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Widely used open source RDBMS

Widely used in-process key-value store

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store

Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Object oriented DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

firebase.google.com/products/realtime-database

cloud.google.com/bigtable

www.mysql.com

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

wakanda.github.io

Technical documentation

firebase.google.com/docs/database

cloud.google.com/bigtable/docs

dev.mysql.com/doc

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

wakanda.github.io/doc

Developer

Google

acquired by Google 2014

Google

Oracle

since 2010, originally MySQL AB, then Sun

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Wakanda SAS

Initial release

2012

2015

1995

1994

2012

Current release

8.4.0, April 2024

18.1.40, May 2020

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

commercial license available

Open Source

AGPLv3, extended commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

C and C++

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

C++, JavaScript

Server operating systems

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

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

yes

with proprietary extensions

yes

SQL interfaced based on SQLite is available

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API

Supported programming languages

Java
JavaScript
Objective-C

C#
C++
Go
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
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

JavaScript

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

proprietary syntax

yes

Triggers

Callbacks are triggered when data changes

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

Internal replication in Colossus, and regional replication between two clusters in different zones

Multi-source replication
Source-replica replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

Atomic single-row operations

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

yes, based on authentication and database rules

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Users with fine-grained authorization concept

no user groups or roles

yes

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