DBMS > Couchbase vs. Dragonfly vs. Microsoft Azure SQL Database vs. NuoDB vs. RDF4J

System Properties Comparison Couchbase vs. Dragonfly vs. Microsoft Azure SQL Database vs. NuoDB vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

Database as a Service offering with high compatibility to Microsoft SQL Server

NuoDB is a webscale distributed database that supports SQL and ACID transactions

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

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

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

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.couchbase.com

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

azure.microsoft.com/en-us/products/azure-sql/database

www.3ds.com/nuodb-distributed-sql-database

rdf4j.org

Technical documentation

docs.couchbase.com

www.dragonflydb.io/docs

docs.microsoft.com/en-us/azure/azure-sql

doc.nuodb.com

rdf4j.org/documentation

Developer

Couchbase, Inc.

DragonflyDB team and community contributors

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

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

Initial release

2011

2023

2010

2013

2004

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

1.0, March 2023

V12

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

BSL 1.1

commercial

limited edition free

Open Source

Eclipse Distribution License (EDL), v1.0.

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, C++, Go and Erlang

C++

Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

schema-free

scheme-free

yes

RDF Schemas

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

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

Proprietary protocol

RESP - REdis Serialization Protocol

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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#
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Java
PHP
Python

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

Lua

Transact SQL

Java, SQL

yes

Triggers

yes

via the TAP protocol

publish/subscribe channels provide some trigger functionality

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

data is dynamically stored/cached on the nodes where it is read/written

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Source-replica replication

yes, with always 3 replicas available

yes

Managed transparently by NuoDB

none

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

selectable on a per-operation basis

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts

ACID

tunable commit protocol

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

yes

MVCC

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

Ephemeral buckets

yes

Temporary table

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Password-based authentication

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

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