DBMS > Apache HugeGraph vs. GigaSpaces vs. PostgreSQL

System Properties Comparison Apache HugeGraph vs. GigaSpaces vs. PostgreSQL

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

A fast-speed and highly-scalable Graph DBMS

High performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented Transactions

Widely used open source RDBMS

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

Primary database model

Graph DBMS

Document store
Object oriented DBMS

Values are user defined objects

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

Graph DBMS
Search engine

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	0.17
Rank	#317	Overall
	#27	Graph DBMS

Score	1.17
Rank	#166	Overall
	#27	Document stores
	#6	Object oriented DBMS

Score	667.25
Rank	#4	Overall
	#4	Relational DBMS

Website

github.com/hugegraph
hugegraph.apache.org

www.gigaspaces.com

www.postgresql.org

Technical documentation

hugegraph.apache.org/docs

docs.gigaspaces.com/latest/landing.html

www.postgresql.org/docs

Developer

Baidu

Gigaspaces Technologies

PostgreSQL Global Development Group

www.postgresql.org/developer

Initial release

2018

2000

1989

1989: Postgres, 1996: PostgreSQL

Current release

0.9

15.5, September 2020

16.4, August 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; Commercial licenses available

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

PostgreSQL Flex @ STACKIT offers managed PostgreSQL Instances with adjustable CPU, RAM, storage amount and speed and several extensions available, in enterprise grade to perfectly match all application requirements. All 100% GDPR-compliant.

Implementation language

Java

Java, C++, .Net

Server operating systems

Linux
macOS
Unix

Linux
macOS
Solaris
Windows

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

Data scheme

yes

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.

XML can be used for describing objects metadata

yes

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

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

SQL-99 for query and DML statements

yes

standard with numerous extensions

APIs and other access methods

Java API
RESTful HTTP API
TinkerPop Gremlin

GigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data

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

Supported programming languages

Groovy
Java
Python

.Net
C++
Java
Python
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

user defined functions

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

Triggers

yes, event driven architecture

yes

Partitioning methods

Methods for storing different data on different nodes

yes

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

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Multi-source replication

synchronous or asynchronous
Source-replica replication

synchronous or asynchronous

Source-replica replication

other methods possible by using 3rd party extensions

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

Map-Reduce pattern can be built with XAP task executors

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Consistency level configurable: ALL, QUORUM, ANY

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

Users, roles and permissions

Role-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.