DBMS > Dragonfly vs. EsgynDB vs. Microsoft SQL Server vs. mSQL vs. RavenDB

System Properties Comparison Dragonfly vs. EsgynDB vs. Microsoft SQL Server vs. mSQL vs. RavenDB

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

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Microsofts flagship relational DBMS

mSQL (Mini SQL) is a simple and lightweight RDBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Key-value store

Relational DBMS

Document store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

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

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Website

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

www.esgyn.cn

www.microsoft.com/en-us/sql-server

hughestech.com.au/products/msql

ravendb.net

Technical documentation

www.dragonflydb.io/docs

learn.microsoft.com/en-US/sql/sql-server

ravendb.net/docs

Developer

DragonflyDB team and community contributors

Esgyn

Microsoft

Hughes Technologies

Hibernating Rhinos

Initial release

2023

2015

1989

1994

2010

Current release

1.0, March 2023

SQL Server 2022, November 2022

4.4, October 2021

5.4, July 2022

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

restricted free version is available

commercial

free licenses can be provided

Open Source

AGPL version 3, commercial license available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

C++, Java

C++

Server operating systems

Linux

Linux
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
macOS
Raspberry Pi
Windows

Data scheme

scheme-free

yes

schema-free

Typing

predefined data types such as float or date

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

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

yes

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

SQL-like query language (RQL)

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP 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

All languages supporting JDBC/ODBC/ADO.Net

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

Lua

Java Stored Procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

yes, but depending on the SQL-Server Edition

none

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

none

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

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

ACID

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

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

fine grained access rights according to SQL-standard

Authorization levels configured per client per database

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