DBMS > GridGain vs. HugeGraph vs. RDF4J

System Properties Comparison GridGain vs. HugeGraph vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

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

A fast-speed and highly-scalable Graph DBMS

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Key-value store
Relational DBMS

Graph DBMS

RDF store

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

Score	1.47
Rank	#154	Overall
	#26	Key-value stores
	#72	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

www.gridgain.com

github.com/hugegraph
hugegraph.apache.org

rdf4j.org

Technical documentation

www.gridgain.com/docs/index.html

hugegraph.apache.org/docs

rdf4j.org/documentation

Developer

GridGain Systems, Inc.

Baidu

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2007

2018

2004

Current release

GridGain 8.5.1

0.9

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, C++, .Net

Java

Server operating systems

Linux
OS X
Solaris
Windows

Linux
macOS
Unix

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

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

also supports composite index and range index

yes

SQL

Support of SQL

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

APIs and other access methods

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

Java API
RESTful HTTP API
TinkerPop Gremlin

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

Groovy
Java
Python

Java
PHP
Python

Server-side scripts

Stored procedures

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

asynchronous Gremlin script jobs

yes

Triggers

yes (cache interceptors and events)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Security Hooks for custom implementations

Users, roles and permissions

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More resources

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