DBMS > Amazon Neptune vs. Neo4j vs. PostgreSQL vs. YottaDB

System Properties Comparison Amazon Neptune vs. Neo4j vs. PostgreSQL vs. YottaDB

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Scalable, ACID-compliant graph database designed with a high-performance distributed cluster architecture, available in self-hosted and cloud offerings

Widely used open source RDBMS

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

A fast and solid embedded Key-value store

Primary database model

Graph DBMS
RDF store

Graph DBMS

Relational DBMS

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

Key-value store

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

Relational DBMS

using the Octo plugin

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	47.91
Rank	#20	Overall
	#1	Graph DBMS

Score	674.32
Rank	#4	Overall
	#4	Relational DBMS

Score	0.20
Rank	#311	Overall
	#46	Key-value stores

Website

aws.amazon.com/neptune

neo4j.com

www.postgresql.org

yottadb.com

Technical documentation

aws.amazon.com/neptune/developer-resources

neo4j.com/docs

www.postgresql.org/docs

yottadb.com/resources/documentation

Developer

Amazon

Neo4j, Inc.

PostgreSQL Global Development Group

www.postgresql.org/developer

YottaDB, LLC

Initial release

2017

2007

1989

1989: Postgres, 1996: PostgreSQL

2001

Current release

5.23, August 2024

16.4, August 2024

License

Commercial or Open Source

commercial

Open Source

GPL version3, commercial licenses available

Open Source

BSD

Open Source

AGPL 3.0

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, Scala

Server operating systems

hosted

Linux

Can also be used server-less as embedded Java database.
OS X
Solaris
Windows

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

Docker
Linux

Data scheme

schema-free

schema-free and schema-optional

yes

schema-free

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

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

Secondary indexes

yes

pluggable indexing subsystem, by default Apache Lucene

yes

SQL

Support of SQL

yes

standard with numerous extensions

by using the Octo plugin

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

Bolt protocol
Cypher query language
Java API
Neo4j-OGM

Object Graph Mapper
RESTful HTTP API
Spring Data Neo4j
TinkerPop 3

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

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
Clojure
Elixir
Go
Groovy
Haskell
Java
JavaScript
Perl
PHP
Python
Ruby
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

yes

User defined Procedures and Functions

user defined functions

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

Triggers

yes

via event handler

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes using Neo4j Fabric

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.

Causal Clustering using Raft protocol

available in in Enterprise Version only

Source-replica replication

other methods possible by using 3rd party extensions

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Causal and Eventual Consistency configurable in Causal Cluster setup
Immediate Consistency in stand-alone mode

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

optimistic locking

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)

Users, roles and permissions. Pluggable authentication with supported standards (LDAP, Active Directory, Kerberos)

fine grained access rights according to SQL-standard

Users and groups based on OS-security mechanisms

More information provided by the system vendor

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