DBMS > FatDB vs. Lovefield vs. Newts vs. SAP Advantage Database Server vs. Stardog

System Properties Comparison FatDB vs. Lovefield vs. Newts vs. SAP Advantage Database Server vs. Stardog

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Embeddable relational database for web apps written in pure JavaScript

Time Series DBMS based on Cassandra

Low-cost RDBMS with access to ISAM and FoxPro data structures

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

Primary database model

Document store
Key-value store

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

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

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

Website

google.github.io/lovefield

opennms.github.io/newts

www.sap.com/products/advantage-database-server.html

www.stardog.com

Technical documentation

github.com/google/lovefield/blob/master/docs/spec_index.md

github.com/OpenNMS/newts/wiki

docs.stardog.com

Developer

FatCloud

Google

OpenNMS Group

Sybase, SAP

Stardog-Union

Initial release

2012

2014

1993

2010

Current release

2.1.12, February 2017

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

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

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

JavaScript

Java

Server operating systems

Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Windows

Linux
Windows

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

yes

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

Via inetgration in SQL Server

SQL-like query language

via JavaScript builder pattern

yes

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

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HTTP REST
Java API

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

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

Supported programming languages

JavaScript

Java

Delphi
Perl
PHP

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

via applications

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via applications

Using read-only observers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

selectable replication factor

based on Cassandra

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

caching of (temporary) tables can be configured

yes

User concepts

Access control

Can implement custom security layer via applications

fine grained access rights according to SQL-standard

Access rights for users and roles

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