DBMS > Blazegraph vs. Drizzle vs. FatDB vs. Hyprcubd

System Properties Comparison Blazegraph vs. Drizzle vs. FatDB vs. Hyprcubd

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Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

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

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

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

Description

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

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

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

Serverless Time Series DBMS

Primary database model

Graph DBMS
RDF store

Relational DBMS

Document store
Key-value store

Time Series DBMS

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

Score	0.77
Rank	#222	Overall
	#20	Graph DBMS
	#8	RDF stores

Website

blazegraph.com

hyprcubd.com (offline)

Technical documentation

wiki.blazegraph.com

Developer

Blazegraph

Drizzle project, originally started by Brian Aker

FatCloud

Hyprcubd, Inc.

Initial release

2006

2008

2012

Current release

2.1.5, March 2019

7.2.4, September 2012

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

GNU GPL

commercial

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

Java

C++

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X

Windows

hosted

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

yes

time, int, uint, float, string

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

SQL

Support of SQL

SPARQL is used as query language

yes

with proprietary extensions

Via inetgration in SQL Server

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

JDBC

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

gRPC (https)

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

C
C++
Java
PHP

Server-side scripts

Stored procedures

yes

via applications

Triggers

hooks for callbacks inside the server can be used.

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Relationships in Graphs

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.

User concepts

Access control

Security and Authentication via Web Application Container (Tomcat, Jetty)

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Can implement custom security layer via applications

token access

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More resources

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Recent citations in the news

This AI Paper Introduces A Comprehensive RDF Dataset With Over 26 Billion Triples Covering Scholarly Data Across All Scientific Disciplines
19 August 2023, MarkTechPost

Back to the future: Does graph database success hang on query language?
5 March 2018, ZDNet

Harnessing GPUs Delivers a Big Speedup for Graph Analytics
15 December 2015, Datanami

Representation Learning on RDF* and LPG Knowledge Graphs
24 September 2020, Towards Data Science

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26 September 2016, InfoWorld

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