DBMS > Amazon Aurora vs. Apache Spark (SQL) vs. Drizzle

System Properties Comparison Amazon Aurora vs. Apache Spark (SQL) vs. Drizzle

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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

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

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

Primary database model

Relational DBMS

Secondary database models

Document store

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

Score	7.48
Rank	#45	Overall
	#28	Relational DBMS

Score	17.39
Rank	#32	Overall
	#20	Relational DBMS

Website

aws.amazon.com/rds/aurora

spark.apache.org/sql

Technical documentation

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

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

Developer

Amazon

Apache Software Foundation

Drizzle project, originally started by Brian Aker

Initial release

2015

2014

2008

Current release

3.5.0 ( 2.13), September 2023

7.2.4, September 2012

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GNU GPL

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

Scala

C++

Server operating systems

hosted

Linux
OS X
Windows

FreeBSD
Linux
OS X

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 DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC

JDBC
ODBC

JDBC

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
Python
R
Scala

C
C++
Java
PHP

Server-side scripts

Stored procedures

yes

Triggers

yes

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
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

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

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

More information provided by the system vendor

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More resources

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