DBMS > Apache IoTDB vs. EJDB vs. FatDB vs. Microsoft SQL Server vs. RDF4J

System Properties Comparison Apache IoTDB vs. EJDB vs. FatDB vs. Microsoft SQL Server vs. RDF4J

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

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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

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

Microsofts flagship relational DBMS

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

Primary database model

Time Series DBMS

Document store

Document store
Key-value store

Relational DBMS

RDF store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

iotdb.apache.org

github.com/Softmotions/ejdb

www.microsoft.com/en-us/sql-server

rdf4j.org

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

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

learn.microsoft.com/en-US/sql/sql-server

rdf4j.org/documentation

Developer

Apache Software Foundation

Softmotions

FatCloud

Microsoft

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

Initial release

2018

2012

1989

2004

Current release

1.1.0, April 2023

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPLv2

commercial

restricted free version is available

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

server-less

Windows

Linux
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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

SQL-like query language

Via inetgration in SQL Server

yes

APIs and other access methods

JDBC
Native API

in-process shared library

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

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Java
PHP
Python

Server-side scripts

Stored procedures

yes

via applications

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

none

selectable replication factor

yes, but depending on the SQL-Server Edition

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

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

yes

User concepts

Access control

yes

Can implement custom security layer via applications

fine grained access rights according to SQL-standard

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