DBMS > Firebase Realtime Database vs. GridGain vs. Kdb vs. Linter vs. MySQL

System Properties Comparison Firebase Realtime Database vs. GridGain vs. Kdb vs. Linter vs. MySQL

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.

GridGain is an in-memory computing platform, built on Apache Ignite

High performance Time Series DBMS

RDBMS for high security requirements

Widely used open source RDBMS

Primary database model

Document store

Key-value store
Relational DBMS

Time Series DBMS
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Relational DBMS

Spatial DBMS

Document store
Spatial DBMS

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

Score	15.00
Rank	#39	Overall
	#6	Document stores

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	7.70
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	0.10
Rank	#350	Overall
	#153	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Website

firebase.google.com/products/realtime-database

Technical documentation

firebase.google.com/docs/database

www.gridgain.com/docs/index.html

code.kx.com

dev.mysql.com/doc

Developer

Google

acquired by Google 2014

GridGain Systems, Inc.

Kx Systems, a division of First Derivatives plc

relex.ru

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2012

2007

2000

kdb was released 2000, kdb+ in 2003

1990

1995

Current release

GridGain 8.5.1

3.6, May 2018

8.3.0, January 2024

License

Commercial or Open Source

commercial

free 32-bit version

commercial

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

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Implementation language

Java, C++, .Net

C and C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

FreeBSD
Linux
OS X
Solaris
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

table attribute 'grouped'

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language (q)

yes

with proprietary extensions

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java
JavaScript
Objective-C

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

limited functionality with using 'rules'

yes (compute grid and cache interceptors can be used instead)

user defined functions

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

proprietary syntax

Triggers

Callbacks are triggered when data changes

yes (cache interceptors and events)

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

similar paradigm used for internal processing

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

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Security Hooks for custom implementations

rights management via user accounts

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

upon request
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