DBMS > Dragonfly vs. Microsoft SQL Server vs. Milvus vs. RDF4J

System Properties Comparison Dragonfly vs. Microsoft SQL Server vs. Milvus vs. RDF4J

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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

Microsofts flagship relational DBMS

A DBMS designed for efficient storage of vector data and vector similarity searches

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Secondary database models

Document store
Graph DBMS
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	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	2.78
Rank	#103	Overall
	#3	Vector DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

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

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

milvus.io

rdf4j.org

Technical documentation

www.dragonflydb.io/docs

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

milvus.io/docs/overview.md

rdf4j.org/documentation

Developer

DragonflyDB team and community contributors

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2023

1989

2019

2004

Current release

1.0, March 2023

SQL Server 2022, November 2022

2.3.4, January 2024

License

Commercial or Open Source

Open Source

BSL 1.1

commercial

restricted free version is available

Open Source

Apache Version 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

Java

Server operating systems

Linux

Linux
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X
Unix
Windows

Data scheme

scheme-free

yes

RDF Schemas

Typing

predefined data types such as float or date

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

yes

Vector, Numeric and String

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

APIs and other access methods

Proprietary protocol

RESP - REdis Serialization Protocol

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

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

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

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

C++
Go
Java
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

Lua

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

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable 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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Role based access control and fine grained access rights

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
» more

Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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