DBMS > Amazon Aurora vs. Apache IoTDB vs. Drizzle vs. MySQL

System Properties Comparison Amazon Aurora vs. Apache IoTDB vs. Drizzle vs. MySQL

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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

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

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

Widely used open source RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store

Document store
Spatial DBMS

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

Score	7.57
Rank	#51	Overall
	#32	Relational DBMS

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

aws.amazon.com/rds/aurora

iotdb.apache.org

www.mysql.com

Technical documentation

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

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

dev.mysql.com/doc

Developer

Amazon

Apache Software Foundation

Drizzle project, originally started by Brian Aker

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2015

2018

2008

1995

Current release

1.1.0, April 2023

7.2.4, September 2012

8.4.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GNU GPL

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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

C++

C and C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

FreeBSD
Linux
OS X

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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

yes

SQL-like query language

yes

with proprietary extensions

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
Native API

JDBC

ADO.NET
JDBC
ODBC
Proprietary native API

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

C
C#
C++
Go
Java
Python
Scala

C
C++
Java
PHP

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

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

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

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

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

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

fine grained access rights according to SQL-standard

yes

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Users with fine-grained authorization concept

no user groups or roles

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