DBMS > Firebase Realtime Database vs. GridGain vs. SQLite vs. VelocityDB vs. VoltDB

System Properties Comparison Firebase Realtime Database vs. GridGain vs. SQLite vs. VelocityDB vs. VoltDB

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

Widely used embeddable, in-process RDBMS

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store

Key-value store
Relational DBMS

Relational DBMS

Graph DBMS
Object oriented DBMS

Relational DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

firebase.google.com/products/realtime-database

Technical documentation

firebase.google.com/docs/database

www.gridgain.com/docs/index.html

www.sqlite.org/docs.html

velocitydb.com/UserGuide

docs.voltdb.com

Developer

Google

acquired by Google 2014

GridGain Systems, Inc.

Dwayne Richard Hipp

VelocityDB Inc

VoltDB Inc.

Initial release

2012

2007

2000

2011

2010

Current release

GridGain 8.5.1

3.45.3 (15 April 2024), April 2024

7.x

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Public Domain

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java, C++, .Net

Java, C++

Server operating systems

hosted

Linux
OS X
Solaris
Windows

server-less

Any that supports .NET

Linux
OS X

for development

Data scheme

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

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

yes

SQL-92 is not fully supported

yes

only a subset of SQL 99

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

.Net

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Java
JavaScript
Objective-C

C#
C++
Java
PHP
Python
Ruby
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

.Net

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

limited functionality with using 'rules'

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

Java

Triggers

Callbacks are triggered when data changes

yes (cache interceptors and events)

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

FOREIGN KEY constraints are not supported

Transaction concepts

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

yes

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

Based on Windows Authentication

Users and roles with access to stored procedures

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