DBMS > AlaSQL vs. Firebase Realtime Database vs. Google Cloud Bigtable vs. MySQL vs. WakandaDB

System Properties Comparison AlaSQL vs. Firebase Realtime Database vs. Google Cloud Bigtable vs. MySQL vs. WakandaDB

Editorial information provided by DB-Engines

Name

Description

JavaScript DBMS library

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

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
Relational DBMS

Document store

Key-value store
Wide column store

Relational DBMS

Key/Value like access via memcached API

Object oriented DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.42
Rank	#264	Overall
	#42	Document stores
	#122	Relational DBMS

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	2.97
Rank	#92	Overall
	#15	Key-value stores
	#8	Wide column stores

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

alasql.org

firebase.google.com/products/realtime-database

cloud.google.com/bigtable

www.mysql.com

wakanda.github.io

Technical documentation

github.com/AlaSQL/alasql

firebase.google.com/docs/database

cloud.google.com/bigtable/docs

dev.mysql.com/doc

wakanda.github.io/doc

Developer

Andrey Gershun & Mathias R. Wulff

Google

acquired by Google 2014

Google

Oracle

since 2010, originally MySQL AB, then Sun

Wakanda SAS

Initial release

2014

2012

2015

1995

2012

Current release

9.0.0, July 2024

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

MIT-License

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPLv3, extended 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

JavaScript

C and C++

C++, JavaScript

Server operating systems

server-less, requires a JavaScript environment (browser, Node.js)

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

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

Secondary indexes

yes

SQL

Support of SQL

Close to SQL99, but no user access control, stored procedures and host language bindings.

yes

with proprietary extensions

APIs and other access methods

JavaScript API

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

JavaScript

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

JavaScript

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

proprietary syntax

yes

Triggers

yes

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

Multi-source 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

none

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

only for local storage and DOM-storage

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

by using IndexedDB, SQL.JS or proprietary FileStorage

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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