DBMS > AnzoGraph DB vs. EJDB vs. Quasardb vs. RDF4J vs. TimesTen

System Properties Comparison AnzoGraph DB vs. EJDB vs. Quasardb vs. RDF4J vs. TimesTen

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

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Distributed, high-performance timeseries database

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

In-Memory RDBMS compatible to Oracle

Primary database model

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	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

cambridgesemantics.com/anzograph

github.com/Softmotions/ejdb

quasar.ai

rdf4j.org

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

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

github.com/Softmotions/ejdb/blob/master/README.md

doc.quasar.ai/master

rdf4j.org/documentation

docs.oracle.com/database/timesten-18.1

Developer

Cambridge Semantics

Softmotions

quasardb

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

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2018

2012

2009

2004

1998

Current release

2.3, January 2021

3.14.1, January 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free trial version available

Open Source

GPLv2

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

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

Server operating systems

Linux

server-less

BSD
Linux
OS X
Windows

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

integer and binary

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.

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

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

SQL-like query language

yes

APIs and other access methods

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

in-process shared library

HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C++
Java
Python

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

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

Java
PHP
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

none

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

none

Source-replica replication with selectable replication factor

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

not needed in graphs

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

in-memory storage is supported as well

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Access rights for users and roles

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

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