DBMS > Dragonfly vs. Faircom DB vs. LokiJS vs. MySQL

System Properties Comparison Dragonfly vs. Faircom DB vs. LokiJS vs. MySQL

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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.

In-memory JavaScript DBMS

Widely used open source RDBMS

Primary database model

Key-value store

Key-value store
Relational DBMS

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

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

Score	0.49
Rank	#260	Overall
	#42	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

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

www.faircom.com/products/faircom-db

github.com/techfort/LokiJS

www.mysql.com

Technical documentation

www.dragonflydb.io/docs

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

techfort.github.io/LokiJS

dev.mysql.com/doc

Developer

DragonflyDB team and community contributors

FairCom Corporation

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2023

1979

2014

1995

Current release

1.0, March 2023

V12, November 2020

8.4.0, April 2024

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

Restricted, free version available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

C++

ANSI C, C++

JavaScript

C and C++

Server operating systems

Linux

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

easily portable to other OSs

server-less, requires a JavaScript environment (browser, Node.js)

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

scheme-free

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

schema-free

yes

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.

yes

Secondary indexes

yes

via views

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

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

JavaScript API

ADO.NET
JDBC
ODBC
Proprietary native 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

JavaScript

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

Lua

yes

.Net, JavaScript, C/C++

View functions in JavaScript

yes

proprietary syntax

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

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).

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

none

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

Atomic execution of command blocks and scripts

tunable from ACID to Eventually Consistent

atomic operations within a single collection possible

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

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

yes

Serialization of a DB in a Node.JS/Cordova/PhoneGap environment. Usage of the IndexedDB-API in a browser.

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

Users with fine-grained authorization concept

no user groups or roles

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