DBMS > Amazon Neptune vs. Faircom EDGE vs. HugeGraph vs. Postgres-XL vs. Prometheus

System Properties Comparison Amazon Neptune vs. Faircom EDGE vs. HugeGraph vs. Postgres-XL vs. Prometheus

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

A fast-speed and highly-scalable Graph DBMS

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

Open-source Time Series DBMS and monitoring system

Primary database model

Graph DBMS
RDF store

Key-value store
Relational DBMS

Graph DBMS

Relational DBMS

Time Series 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	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

aws.amazon.com/neptune

www.faircom.com/products/faircom-edge

github.com/hugegraph
hugegraph.apache.org

www.postgres-xl.org

prometheus.io

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

hugegraph.apache.org/docs

www.postgres-xl.org/documentation

prometheus.io/docs

Developer

Amazon

FairCom Corporation

Baidu

Initial release

2017

1979

2018

2014

since 2012, originally named StormDB

2015

Current release

V3, October 2020

0.9

10 R1, October 2018

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache Version 2.0

Open Source

Mozilla public license

Open Source

Apache 2.0

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

ANSI C, C++

Java

Server operating systems

hosted

Android
Linux

ARM, x86
Raspbian
Windows

Linux
macOS
Unix

Linux
macOS

Linux
Windows

Data scheme

schema-free

Flexible Schema (defined schema, partial schema, schema free)

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

Numeric data only

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

Import of XML data possible

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

ANSI SQL queries

yes

distributed, parallel query execution

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

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

RESTful HTTP/JSON API

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

Groovy
Java
Python

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

asynchronous Gremlin script jobs

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

File partitioning

Customizable business rules for partitioning

yes

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

horizontal partitioning

Sharding

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

Synchronous and asynchronous realtime replication based on transaction logs

yes

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

yes

by Federation

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

Immediate Consistency
Tunable Consistency

Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

when using SQL

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

across SQL and NoSQL

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 user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Users, roles and permissions

fine grained access rights according to SQL-standard

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