DBMS > Amazon Neptune vs. Atos Standard Common Repository vs. EsgynDB vs. HyperSQL vs. Postgres-XL

System Properties Comparison Amazon Neptune vs. Atos Standard Common Repository vs. EsgynDB vs. HyperSQL vs. Postgres-XL

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Fast, reliable graph database built for the cloud

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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

Primary database model

Graph DBMS
RDF store

Document store
Key-value store

Relational DBMS

Secondary database models

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	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

aws.amazon.com/neptune

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.esgyn.cn

hsqldb.org

www.postgres-xl.org

Technical documentation

aws.amazon.com/neptune/developer-resources

hsqldb.org/web/hsqlDocsFrame.html

www.postgres-xl.org/documentation

Developer

Amazon

Atos Convergence Creators

Esgyn

Initial release

2017

2016

2015

2001

2014

since 2012, originally named StormDB

Current release

1703

2.7.2, June 2023

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

Mozilla public license

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

Java

C++, Java

Java

Server operating systems

hosted

Linux

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
macOS

Data scheme

schema-free

Schema and schema-less with LDAP views

yes

Typing

predefined data types such as float or date

yes

optional

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

SQL

Support of SQL

yes

distributed, parallel query execution

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

LDAP

ADO.NET
JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

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

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

All languages with LDAP bindings

All languages supporting JDBC/ODBC/ADO.Net

All languages supporting JDBC/ODBC
Java

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

Server-side scripts

Stored procedures

Java Stored Procedures

Java, SQL

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

cell division

Sharding

none

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

Multi-source replication between multi datacenters

none

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 depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

Atomic execution of specific operations

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

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)

LDAP bind authentication

fine grained access rights according to SQL-standard

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