DBMS > Amazon Neptune vs. Amazon Redshift vs. HugeGraph vs. Linter vs. Postgres-XL

System Properties Comparison Amazon Neptune vs. Amazon Redshift vs. HugeGraph vs. Linter vs. Postgres-XL

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Large scale data warehouse service for use with business intelligence tools

A fast-speed and highly-scalable Graph DBMS

RDBMS for high security requirements

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

Primary database model

Secondary database models

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	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Website

aws.amazon.com/neptune

aws.amazon.com/redshift

github.com/hugegraph
hugegraph.apache.org

linter.ru

www.postgres-xl.org

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.aws.amazon.com/redshift

hugegraph.apache.org/docs

www.postgres-xl.org/documentation

Developer

Amazon

Amazon (based on PostgreSQL)

Baidu

relex.ru

Initial release

2017

2012

2018

1990

2014

since 2012, originally named StormDB

Current release

0.9

10 R1, October 2018

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Mozilla public license

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++

Server operating systems

hosted

Linux
macOS
Unix

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
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

XML type, but no XML query functionality

Secondary indexes

restricted

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

distributed, parallel query execution

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

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 supporting JDBC/ODBC

Groovy
Java
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in Python

asynchronous Gremlin script jobs

yes

proprietary syntax with the possibility to convert from PL/SQL

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

depending on used storage backend, e.g. Cassandra and HBase

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

depending on used storage backend, e.g. Cassandra and HBase

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

informational only, not enforced by the system

yes

edges in graph

yes

Transaction concepts

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

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)

fine grained access rights according to SQL-standard

Users, roles and permissions

fine grained access rights according to SQL-standard

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