DBMS > Amazon Aurora vs. Faircom DB vs. Postgres-XL vs. Riak KV

System Properties Comparison Amazon Aurora vs. Faircom DB vs. Postgres-XL vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

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	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	0.53
Rank	#254	Overall
	#117	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

aws.amazon.com/rds/aurora

www.faircom.com/products/faircom-db

www.postgres-xl.org

Technical documentation

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

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

www.postgres-xl.org/documentation

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Amazon

FairCom Corporation

OpenSource, formerly Basho Technologies

Initial release

2015

1979

2014

since 2012, originally named StormDB

2009

Current release

V12, November 2020

10 R1, October 2018

3.2.0, December 2022

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Mozilla public license

Open Source

Apache version 2, commercial enterprise edition

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

ANSI C, C++

Erlang

Server operating systems

hosted

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
macOS

Linux
OS X

Data scheme

yes

schema free, schema optional, schema required, partial schema,

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

XML type, but no XML query functionality

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

yes, ANSI SQL with proprietary extensions

yes

distributed, parallel query execution

APIs and other access methods

ADO.NET
JDBC
ODBC

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

HTTP API
Native Erlang Interface

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

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

horizontal partitioning

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

tunable from ACID to Eventually Consistent

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Yes, tunable from durable to delayed durability to in-memory

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

Fine grained access rights according to SQL-standard with additional protections for files

fine grained access rights according to SQL-standard

yes, using Riak Security

More information provided by the system vendor

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Amazon - the rising star in the DBMS market
3 August 2015, Matthias Gelbmann

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