DBMS > AnzoGraph DB vs. GBase vs. RavenDB vs. Stardog vs. Transbase

System Properties Comparison AnzoGraph DB vs. GBase vs. RavenDB vs. Stardog vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Open Source Operational and Transactional Enterprise NoSQL Document Database

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

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

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

cambridgesemantics.com/anzograph

www.gbase.cn

ravendb.net

www.stardog.com

www.transaction.de/en/products/transbase.html

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

ravendb.net/docs

docs.stardog.com

www.transaction.de/en/products/transbase/features.html

Developer

Cambridge Semantics

General Data Technology Co., Ltd.

Hibernating Rhinos

Stardog-Union

Transaction Software GmbH

Initial release

2018

2004

2010

1987

Current release

2.3, January 2021

GBase 8a, GBase 8s, GBase 8c

5.4, July 2022

7.3.0, May 2020

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

AGPL version 3, commercial license available

commercial

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

commercial

free development license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C, Java, Python

Java

C and C++

Server operating systems

Linux

Linux
macOS
Raspberry Pi
Windows

Linux
macOS
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

yes

schema-free

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

SPARQL and SPARQL* as primary query language. Cypher preview.

Standard with numerous extensions

SQL-like query language (RQL)

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

yes

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

ADO.NET
C API
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

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

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C++
Java
Python

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

yes

user defined functions and aggregates, HTTP Server extensions in Java

yes

Triggers

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

yes

Multi-source replication

Multi-source replication in HA-Cluster

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

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.

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

relationships in graphs

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

ACID

yes

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

User concepts

Access control

Access rights for users and roles

yes

Authorization levels configured per client per database

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

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