DBMS > Amazon Aurora vs. EXASOL vs. Hazelcast vs. Riak KV vs. SQLite

System Properties Comparison Amazon Aurora vs. EXASOL vs. Hazelcast vs. Riak KV vs. SQLite

Editorial information provided by DB-Engines

Name

Description

MySQL and PostgreSQL compatible cloud service by Amazon

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

A widely adopted in-memory data grid

Distributed, fault tolerant key-value store

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Key-value store

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

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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

Score	8.50
Rank	#49	Overall
	#31	Relational DBMS

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	4.44
Rank	#80	Overall
	#9	Key-value stores

Score	116.01
Rank	#10	Overall
	#7	Relational DBMS

Website

aws.amazon.com/rds/aurora

www.exasol.com

hazelcast.com

www.sqlite.org

Technical documentation

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

www.exasol.com/resources

hazelcast.org/imdg/docs

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

www.sqlite.org/docs.html

Developer

Amazon

Exasol

Hazelcast

OpenSource, formerly Basho Technologies

Dwayne Richard Hipp

Initial release

2015

2000

2008

2009

2000

Current release

5.3.6, November 2023

3.2.0, December 2022

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache version 2, commercial enterprise edition

Open Source

Public Domain

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

Erlang

Server operating systems

hosted

All OS with a Java VM

Linux
OS X

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

the object must implement a serialization strategy

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

JCache
JPA
Memcached protocol
RESTful HTTP API

HTTP API
Native Erlang Interface

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

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
Lua
Python
R

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

user defined functions

yes

Event Listeners, Executor Services

Erlang

Triggers

yes

Events

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Replicated Map

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

Access rights for users, groups and roles according to SQL-standard

Role-based access control

yes, using Riak Security

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