DBMS > Couchbase vs. EsgynDB vs. Microsoft Azure SQL Database vs. Stardog vs. Valentina Server

System Properties Comparison Couchbase vs. EsgynDB vs. Microsoft Azure SQL Database vs. Stardog vs. Valentina Server

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

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

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

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

Object-relational database and reports server

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	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

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

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

www.couchbase.com

www.esgyn.cn

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

www.stardog.com

www.valentina-db.net

Technical documentation

docs.couchbase.com

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

docs.stardog.com

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Couchbase, Inc.

Esgyn

Microsoft

Stardog-Union

Paradigma Software

Initial release

2011

2015

2010

1999

Current release

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

V12

7.3.0, May 2020

5.7.5

License

Commercial or Open Source

Open Source

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

commercial

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

commercial

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

C++

Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

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

yes

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

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

ADO.NET
JDBC
ODBC

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

ODBC

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

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

Java Stored Procedures

Transact SQL

user defined functions and aggregates, HTTP Server extensions in Java

yes

Triggers

yes

via the TAP protocol

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication between multi datacenters

yes, with always 3 replicas available

Multi-source replication in HA-Cluster

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

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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

Ephemeral buckets

yes

User concepts

Access control

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

fine grained access rights according to SQL-standard

Access rights for users and roles

fine grained access rights according to SQL-standard

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