DBMS > FatDB vs. MySQL vs. RavenDB vs. RDFox vs. Spark SQL

System Properties Comparison FatDB vs. MySQL vs. RavenDB vs. RDFox vs. Spark SQL

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.

Description

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

Widely used open source RDBMS

Open Source Operational and Transactional Enterprise NoSQL Document Database

High performance knowledge graph and semantic reasoning engine

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Key-value store

Relational DBMS

Key/Value like access via memcached API

Document store

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	0.23
Rank	#308	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

www.mysql.com

ravendb.net

www.oxfordsemantic.tech

spark.apache.org/sql

Technical documentation

dev.mysql.com/doc

ravendb.net/docs

docs.oxfordsemantic.tech

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

FatCloud

Oracle

since 2010, originally MySQL AB, then Sun

Hibernating Rhinos

Oxford Semantic Technologies

Apache Software Foundation

Initial release

2012

1995

2010

2017

2014

Current release

8.4.0, April 2024

5.4, July 2022

6.0, Septermber 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

AGPL version 3, commercial license available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

C++

Scala

Server operating systems

Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS
Raspberry Pi
Windows

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

RDF schemas

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

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

yes

with proprietary extensions

SQL-like query language (RQL)

SQL-like DML and DDL statements

APIs and other access methods

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

ADO.NET
JDBC
ODBC
Proprietary native API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

RESTful HTTP API
SPARQL 1.1

JDBC
ODBC

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

C
Java

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

yes

proprietary syntax

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication
Source-replica replication

Multi-source replication

replication via a shared file system

none

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

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 stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID, Cluster-wide transaction available

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Can implement custom security layer via applications

Users with fine-grained authorization concept

no user groups or roles

Authorization levels configured per client per database

Roles, resources, and access types

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