DBMS > Amazon Redshift vs. Hazelcast vs. MySQL vs. NCache vs. Quasardb

System Properties Comparison Amazon Redshift vs. Hazelcast vs. MySQL vs. NCache vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A widely adopted in-memory data grid

Widely used open source RDBMS

Open-Source and Enterprise in-memory Key-Value Store

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Key-value store

Relational DBMS

Key/Value like access via memcached API

Key-value store

Time Series DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Spatial DBMS

Document store
Search engine

Using distributed Lucene

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/redshift

hazelcast.com

www.mysql.com

www.alachisoft.com/ncache

quasar.ai

Technical documentation

docs.aws.amazon.com/redshift

hazelcast.org/imdg/docs

dev.mysql.com/doc

www.alachisoft.com/resources/docs

doc.quasar.ai/master

Developer

Amazon (based on PostgreSQL)

Hazelcast

Oracle

since 2010, originally MySQL AB, then Sun

Alachisoft

quasardb

Initial release

2012

2008

1995

2005

2009

Current release

5.3.6, November 2023

8.4.0, April 2024

5.3.3, April 2024

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Enterprise Edition available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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 and C++

C#, .NET, .NET Core, Java

C++

Server operating systems

hosted

All OS with a Java VM

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

yes

integer and binary

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

yes

Secondary indexes

restricted

yes

with tags

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

with proprietary extensions

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

SQL-like query language

APIs and other access methods

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

IDistributedCache
JCache
LINQ
Proprietary native API

HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

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

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

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Event Listeners, Executor Services

yes

proprietary syntax

support for stored procedures with SQL-Server CLR

Triggers

yes

Events

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Multi-source replication
Source-replica replication

yes, with selectable consistency level

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

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

ACID

not for MyISAM storage engine

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

Users with fine-grained authorization concept

no user groups or roles

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

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Market metrics

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Licensing and pricing models

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