DBMS > Citus vs. Dragonfly vs. Microsoft SQL Server vs. Stardog vs. TinkerGraph

System Properties Comparison Citus vs. Dragonfly vs. Microsoft SQL Server vs. Stardog vs. TinkerGraph

Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Primary database model

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	0.08
Rank	#348	Overall
	#35	Graph DBMS

Website

www.citusdata.com

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

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

www.stardog.com

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

Technical documentation

docs.citusdata.com

www.dragonflydb.io/docs

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

docs.stardog.com

Developer

DragonflyDB team and community contributors

Microsoft

Stardog-Union

Initial release

2010

2023

1989

2010

2009

Current release

8.1, December 2018

1.0, March 2023

SQL Server 2022, November 2022

7.3.0, May 2020

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

BSL 1.1

commercial

restricted free version is available

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

Server operating systems

Linux

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

scheme-free

yes

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

yes

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

specific XML type available, but no XML query functionality

yes

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

standard, with numerous extensions

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Proprietary protocol

RESP - REdis Serialization Protocol

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

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

TinkerPop 3

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Groovy
Java

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Lua

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

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

publish/subscribe channels provide some trigger functionality

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication in HA-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

none

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Relationships in graphs

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

Durability

Support for making data persistent

yes

optional

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Password-based authentication

fine grained access rights according to SQL-standard

Access rights for users and roles

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