DBMS > Amazon Neptune vs. Hazelcast vs. Microsoft Azure SQL Database vs. MySQL vs. Postgres-XL

System Properties Comparison Amazon Neptune vs. Hazelcast vs. Microsoft Azure SQL Database vs. MySQL vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

A widely adopted in-memory data grid

Database as a Service offering with high compatibility to Microsoft SQL Server

Widely used open source RDBMS

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

Primary database model

Graph DBMS
RDF store

Key-value store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

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

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

aws.amazon.com/neptune

hazelcast.com

azure.microsoft.com/en-us/products/azure-sql/database

www.mysql.com

www.postgres-xl.org

Technical documentation

aws.amazon.com/neptune/developer-resources

hazelcast.org/imdg/docs

docs.microsoft.com/en-us/azure/azure-sql

dev.mysql.com/doc

www.postgres-xl.org/documentation

Developer

Amazon

Hazelcast

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2017

2008

2010

1995

2014

since 2012, originally named StormDB

Current release

5.3.6, November 2023

V12

8.4.0, April 2024

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Mozilla public license

Cloud-based only

Only available as a cloud service

yes

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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

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

schema-free

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

the object must implement a serialization strategy

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

yes

distributed, parallel query execution

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

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

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

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

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

Transact SQL

yes

proprietary syntax

user defined functions

Triggers

yes

Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

yes

Replicated Map

yes, with always 3 replicas available

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

not for MyISAM storage engine

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

not for MyISAM storage engine

ACID

MVCC

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

with encyption-at-rest

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Role-based access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

fine grained access rights according to SQL-standard

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