DBMS > Amazon Aurora vs. ArcadeDB vs. Drizzle vs. Microsoft SQL Server vs. OrigoDB

System Properties Comparison Amazon Aurora vs. ArcadeDB vs. Drizzle vs. Microsoft SQL Server vs. OrigoDB

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Microsofts flagship relational DBMS

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Relational DBMS

Document store
Object oriented DBMS

Secondary database models

Document store

Document store
Graph DBMS
Spatial DBMS

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

Score	8.50
Rank	#49	Overall
	#31	Relational DBMS

Score	0.09
Rank	#355	Overall
	#48	Document stores
	#37	Graph DBMS
	#52	Key-value stores
	#34	Time Series DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Website

aws.amazon.com/rds/aurora

arcadedb.com

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

origodb.com

Technical documentation

docs.aws.amazon.com/AmazonRDS/latest/AuroraUserGuide/CHAP_Aurora.html

docs.arcadedb.com

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

origodb.com/docs

Developer

Amazon

Arcade Data

Drizzle project, originally started by Brian Aker

Microsoft

Robert Friberg et al

Initial release

2015

2021

2008

1989

2009

under the name LiveDB

Current release

September 2021

7.2.4, September 2012

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GNU GPL

commercial

restricted free version is available

Open Source

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++

Server operating systems

hosted

All OS with a Java VM

FreeBSD
Linux
OS X

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language, no joins

yes

with proprietary extensions

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

JDBC

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

.NET Client API
HTTP API
LINQ

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

Java

C
C++
Java
PHP

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

.Net

Server-side scripts

Stored procedures

yes

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

yes

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

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

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes, but depending on the SQL-Server Edition

Source-replica replication

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

yes

relationship in graphs

yes

depending on model

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

Write ahead log

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

Role based authorization

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