DBMS > Drizzle vs. HugeGraph vs. Hyprcubd vs. Kinetica vs. RDF4J

System Properties Comparison Drizzle vs. HugeGraph vs. Hyprcubd vs. Kinetica vs. RDF4J

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

A fast-speed and highly-scalable Graph DBMS

Serverless Time Series DBMS

Fully vectorized database across both GPUs and CPUs

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

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Website

github.com/hugegraph
hugegraph.apache.org

hyprcubd.com (offline)

www.kinetica.com

rdf4j.org

Technical documentation

hugegraph.apache.org/docs

docs.kinetica.com

rdf4j.org/documentation

Developer

Drizzle project, originally started by Brian Aker

Baidu

Hyprcubd, Inc.

Kinetica

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

Initial release

2008

2018

2012

2004

Current release

7.2.4, September 2012

0.9

7.1, August 2021

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache Version 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

C, C++

Java

Server operating systems

FreeBSD
Linux
OS X

Linux
macOS
Unix

hosted

Linux

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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.

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

JDBC

Java API
RESTful HTTP API
TinkerPop Gremlin

gRPC (https)

JDBC
ODBC
RESTful HTTP API

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

Groovy
Java
Python

C++
Java
JavaScript (Node.js)
Python

Java
PHP
Python

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

user defined functions

yes

Triggers

hooks for callbacks inside the server can be used.

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

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

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

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

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

edges in graph

yes

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

GPU vRAM or System RAM

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Users, roles and permissions

token access

Access rights for users and roles on table level

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More resources

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