DBMS > HEAVY.AI vs. NSDb vs. RDF4J vs. Sadas Engine

System Properties Comparison HEAVY.AI vs. NSDb vs. RDF4J vs. Sadas Engine

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Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

Secondary database models

Spatial DBMS

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

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	0.03
Rank	#379	Overall
	#156	Relational DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

nsdb.io

rdf4j.org

www.sadasengine.com

Technical documentation

docs.heavy.ai

nsdb.io/Architecture

rdf4j.org/documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

HEAVY.AI, Inc.

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

SADAS s.r.l.

Initial release

2016

2017

2004

2006

Current release

5.10, January 2022

8.0

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

free trial version available

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++ and CUDA

Java, Scala

Java

C++

Server operating systems

Linux

Linux
macOS

Linux
OS X
Unix
Windows

AIX
Linux
Windows

Data scheme

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

gRPC
HTTP REST
WebSocket

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

JDBC
ODBC
Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

Java
Scala

Java
PHP
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Durability

Support for making data persistent

yes

Using Apache Lucene

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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