DBMS > Dragonfly vs. Faircom DB vs. Google Cloud Firestore vs. IBM Cloudant

System Properties Comparison Dragonfly vs. Faircom DB vs. Google Cloud Firestore vs. IBM Cloudant

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Editorial information provided by DB-Engines

Name

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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Database as a Service offering based on Apache CouchDB

Primary database model

Key-value store

Key-value store
Relational DBMS

Document store

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

Score	0.49
Rank	#261	Overall
	#38	Key-value stores

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	7.36
Rank	#53	Overall
	#9	Document stores

Score	2.75
Rank	#104	Overall
	#19	Document stores

Website

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

www.faircom.com/products/faircom-db

firebase.google.com/products/firestore

www.ibm.com/products/cloudant

Technical documentation

www.dragonflydb.io/docs

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

firebase.google.com/docs/firestore

cloud.ibm.com/docs/Cloudant

Developer

DragonflyDB team and community contributors

FairCom Corporation

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Initial release

2023

1979

2017

2010

Current release

1.0, March 2023

V12, November 2020

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Restricted, free version available

commercial

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++

ANSI C, C++

Erlang

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Data scheme

scheme-free

schema free, schema optional, schema required, partial schema,

schema-free

Typing

predefined data types such as float or date

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

yes, ANSI SQL Types, JSON, typed binary structures

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.

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

RESTful HTTP/JSON API

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

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C#
Java
JavaScript
Objective-C
PHP
Ruby

Server-side scripts

Stored procedures

Lua

yes

.Net, JavaScript, C/C++

yes, Firebase Rules & Cloud Functions

View functions (Map-Reduce) in JavaScript

Triggers

publish/subscribe channels provide some trigger functionality

yes

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of command blocks and scripts

tunable from ACID to Eventually Consistent

yes

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Optimistic locking

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

Access rights for users can be defined per database

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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