DBMS > Dragonfly vs. Firebase Realtime Database vs. FoundationDB vs. SQLite

System Properties Comparison Dragonfly vs. Firebase Realtime Database vs. FoundationDB vs. SQLite

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Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Description

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

Ordered key-value store. Core features are complimented by layers.

Widely used embeddable, in-process RDBMS

Primary database model

Key-value store

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

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

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	1.03
Rank	#190	Overall
	#31	Document stores
	#28	Key-value stores
	#89	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

github.com/dragonflydb/dragonfly
www.dragonflydb.io

firebase.google.com/products/realtime-database

github.com/apple/foundationdb

www.sqlite.org

Technical documentation

www.dragonflydb.io/docs

firebase.google.com/docs/database

apple.github.io/foundationdb

www.sqlite.org/docs.html

Developer

DragonflyDB team and community contributors

Google

acquired by Google 2014

FoundationDB

Dwayne Richard Hipp

Initial release

2023

2012

2013

2000

Current release

1.0, March 2023

6.2.28, November 2020

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Open Source

Apache 2.0

Open Source

Public Domain

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++

Server operating systems

Linux

hosted

Linux
OS X
Windows

server-less

Data scheme

scheme-free

schema-free

some layers support schemas

yes

dynamic column types

Typing

predefined data types such as float or date

strings, hashes, lists, sets, sorted sets, bit arrays

yes

some layers support typing

yes

not rigid because of 'dynamic typing' concept.

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.

Secondary indexes

yes

SQL

Support of SQL

supported in specific SQL layer only

yes

SQL-92 is not fully supported

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

Java
JavaScript
Objective-C

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

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

Server-side scripts

Stored procedures

Lua

limited functionality with using 'rules'

in SQL-layer only

Triggers

publish/subscribe channels provide some trigger functionality

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Linearizable consistency

Foreign keys

Referential integrity

in SQL-layer only

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

via file-system locks

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

Password-based authentication

yes, based on authentication and database rules

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