DBMS > Dgraph vs. GridGain vs. H2GIS vs. Oracle Rdb vs. PostGIS

System Properties Comparison Dgraph vs. GridGain vs. H2GIS vs. Oracle Rdb vs. PostGIS

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

GridGain is an in-memory computing platform, built on Apache Ignite

Spatial extension of H2

Spatial extension of PostgreSQL

Primary database model

Graph DBMS

Key-value store
Relational DBMS

Spatial DBMS

Relational DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	1.55
Rank	#150	Overall
	#26	Key-value stores
	#70	Relational DBMS

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	21.72
Rank	#29	Overall
	#1	Spatial DBMS

Website

dgraph.io

www.gridgain.com

www.h2gis.org

www.oracle.com/database/technologies/related/rdb.html

postgis.net

Technical documentation

dgraph.io/docs

www.gridgain.com/docs/index.html

www.h2gis.org/docs/home

www.oracle.com/database/technologies/related/rdb-doc.html

postgis.net/documentation

Developer

Dgraph Labs, Inc.

GridGain Systems, Inc.

CNRS

Oracle, originally developed by Digital Equipment Corporation (DEC)

Initial release

2016

2007

2013

1984

2005

Current release

GridGain 8.5.1

7.4.1.1, 2021

3.4.2, February 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

LGPL 3.0

commercial

Open Source

GPL v2.0

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.

Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X
Solaris
Windows

HP Open VMS

Data scheme

schema-free

yes

Flexible Schema (defined schema, partial schema, 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

Secondary indexes

yes

SQL

Support of SQL

ANSI-99 for query and DML statements, subset of DDL

yes

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

Supported programming languages

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

Java

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

yes

based on H2

user defined functions

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

none

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

yes (replicated cache)

yes

based on H2

yes

based on PostgreSQL

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

Planned for future releases

Security Hooks for custom implementations

yes

based on H2

yes

based on PostgreSQL

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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