DBMS > atoti vs. RDF4J vs. Transwarp Hippo

System Properties Comparison atoti vs. RDF4J vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Object oriented DBMS

RDF store

Vector DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	0.00
Rank	#383	Overall
	#13	Vector DBMS

Website

atoti.io

rdf4j.org

www.transwarp.cn/en/subproduct/hippo

Technical documentation

docs.atoti.io

rdf4j.org/documentation

Developer

ActiveViam

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

Initial release

2004

2023

Current release

1.0, May 2023

License

Commercial or Open Source

commercial

free versions available

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

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Implementation language

Java

C++

Server operating systems

Linux
OS X
Unix
Windows

Linux
macOS

Data scheme

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

SQL

Support of SQL

Multidimensional Expressions (MDX)

APIs and other access methods

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API

Supported programming languages

Java
PHP
Python

C++
Java
Python

Server-side scripts

Stored procedures

Python

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Role based access control and fine grained access rights

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