DBMS > Amazon Neptune vs. ArangoDB vs. BigObject vs. PostgreSQL

System Properties Comparison Amazon Neptune vs. ArangoDB vs. BigObject vs. PostgreSQL

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Native multi-model DBMS for graph, document, key/value and search. All in one engine and accessible with one query language.

Analytic DBMS for real-time computations and queries

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

Primary database model

Graph DBMS
RDF store

Document store
Graph DBMS
Key-value store
Search engine

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	2.13
Rank	#111	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	2.85
Rank	#88	Overall
	#14	Document stores
	#4	Graph DBMS
	#13	Key-value stores
	#9	Search engines

Score	0.14
Rank	#333	Overall
	#145	Relational DBMS

Score	674.32
Rank	#4	Overall
	#4	Relational DBMS

Website

aws.amazon.com/neptune

arangodb.com

bigobject.io

www.postgresql.org

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.arangodb.com

docs.bigobject.io

www.postgresql.org/docs

Developer

Amazon

ArangoDB Inc.

BigObject, Inc.

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2017

2012

2015

1989

1989: Postgres, 1996: PostgreSQL

Current release

3.11.5, November 2023

16.4, August 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; Commercial license (Enterprise) available

commercial

free community edition available

Open Source

BSD

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Server operating systems

hosted

Linux
OS X
Windows

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Data scheme

schema-free

automatically recognizes schema within a collection

yes

Typing

predefined data types such as float or date

yes

string, double, boolean, list, hash

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

standard with numerous extensions

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

AQL
Foxx Framework
Graph API (Gremlin)
GraphQL query language
HTTP API
Java & SpringData
JSON style queries
VelocyPack/VelocyStream

fluentd
ODBC
RESTful HTTP API

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

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C#
C++
Clojure
Elixir
Go
Java
JavaScript (Node.js)
PHP
Python
R
Rust

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

JavaScript

Lua

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

since version 2.0

none

partitioning by range, list and (since PostgreSQL 11) by hash

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.

Source-replica replication with configurable replication factor

none

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

can be done with stored procedures in JavaScript

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

configurable per collection or per write
Immediate Consistency
OneShard (highly available, fault-tolerant deployment mode with ACID semantics)

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

relationships in graphs

yes

automatically between fact table and dimension tables

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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)

yes

fine grained access rights according to SQL-standard

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