DBMS > eXtremeDB vs. Firebase Realtime Database vs. KeyDB vs. MySQL

System Properties Comparison eXtremeDB vs. Firebase Realtime Database vs. KeyDB vs. MySQL

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Editorial information provided by DB-Engines

Name

Description

Natively in-memory DBMS with options for persistency, high-availability and clustering

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

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Widely used open source RDBMS

Primary database model

Relational DBMS
Time Series DBMS

Document store

Key-value store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	0.79
Rank	#212	Overall
	#100	Relational DBMS
	#18	Time Series DBMS

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	0.63
Rank	#232	Overall
	#32	Key-value stores

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Website

www.mcobject.com

firebase.google.com/products/realtime-database

github.com/Snapchat/KeyDB
keydb.dev

www.mysql.com

Technical documentation

www.mcobject.com/docs/extremedb.htm

firebase.google.com/docs/database

docs.keydb.dev

dev.mysql.com/doc

Developer

McObject

Google

acquired by Google 2014

EQ Alpha Technology Ltd.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2001

2012

2019

1995

Current release

8.2, 2021

9.0.0, July 2024

License

Commercial or Open Source

commercial

Open Source

BSD-3

Open Source

GPL version 2. Commercial licenses with extended functionallity are 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 and C++

C++

C and C++

Server operating systems

AIX
HP-UX
Linux
macOS
Solaris
Windows

hosted

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

support of XML interfaces available

yes

Secondary indexes

yes

by using the Redis Search module

yes

SQL

Support of SQL

yes

with the option: eXtremeSQL

yes

with proprietary extensions

APIs and other access methods

.NET Client API
JDBC
JNI
ODBC
Proprietary protocol
RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
C
C#
C++
Java
Lua
Python
Scala

Java
JavaScript
Objective-C

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

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

Server-side scripts

Stored procedures

yes

limited functionality with using 'rules'

Lua

yes

proprietary syntax

Triggers

yes

by defining events

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning / sharding

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Active Replication Fabric™ for IoT
Multi-source replication

by means of eXtremeDB Cluster option
Source-replica replication

by means of eXtremeDB High Availability option

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency
Strong eventual consistency with CRDTs

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

ACID

yes

Optimistic locking, atomic execution of commands blocks and scripts

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Optimistic (MVCC) and pessimistic (locking) strategies available

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

simple password-based access control and ACL

Users with fine-grained authorization concept

no user groups or roles

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