DBMS > Drizzle vs. Oracle Coherence vs. RDF4J vs. Snowflake

System Properties Comparison Drizzle vs. Oracle Coherence vs. RDF4J vs. Snowflake

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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.

Description

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

Oracles in-memory data grid solution

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

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

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

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.oracle.com/java/coherence

rdf4j.org

www.snowflake.com

Technical documentation

docs.oracle.com/en/middleware/standalone/coherence

rdf4j.org/documentation

docs.snowflake.net/manuals/index.html

Developer

Drizzle project, originally started by Brian Aker

Oracle

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

Snowflake Computing Inc.

Initial release

2008

2007

2004

2014

Current release

7.2.4, September 2012

14.1, August 2023

License

Commercial or Open Source

Open Source

GNU GPL

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

FreeBSD
Linux
OS X

All OS with a Java VM

Linux
OS X
Unix
Windows

hosted

Data scheme

yes

schema-free

yes

RDF Schemas

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

JCache
JPA
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

CLI Client
JDBC
ODBC

Supported programming languages

C
C++
Java
PHP

.Net
C++
Java

Java
PHP
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

hooks for callbacks inside the server can be used.

yes

Live Events

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes, with selectable consistency level

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

yes

in-memory storage is supported as well

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

authentification to access the cache via certificates or http basic authentication

Users with fine-grained authorization concept, user roles and pluggable authentication

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