DBMS > Apache Doris vs. EventStoreDB vs. Microsoft Azure Table Storage vs. RDF4J

System Properties Comparison Apache Doris vs. EventStoreDB vs. Microsoft Azure Table Storage vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

Industrial-strength, open-source database solution built from the ground up for event sourcing.

A Wide Column Store for rapid development using massive semi-structured datasets

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

Primary database model

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

Score	0.57
Rank	#244	Overall
	#113	Relational DBMS

Score	1.10
Rank	#179	Overall
	#1	Event Stores

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

doris.apache.org
github.com/apache/doris

www.eventstore.com

azure.microsoft.com/en-us/services/storage/tables

rdf4j.org

Technical documentation

github.com/apache/doris/wiki

developers.eventstore.com

rdf4j.org/documentation

Developer

Apache Software Foundation, originally contributed from Baidu

Event Store Limited

Microsoft

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

Initial release

2017

2012

2004

Current release

1.2.2, February 2023

21.2, February 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux

Linux
Windows

hosted

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
MySQL client

RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Java

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

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

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

optimistic locking

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights based on private key authentication or shared access signatures

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