DBMS > AnzoGraph DB vs. DataFS vs. Drizzle vs. Hawkular Metrics

System Properties Comparison AnzoGraph DB vs. DataFS vs. Drizzle vs. Hawkular Metrics

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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

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Secondary database models

Graph DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Website

cambridgesemantics.com/anzograph

newdatabase.com

www.hawkular.org

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

dev.mobiland.com/Overview.xsp

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Cambridge Semantics

Mobiland AG

Drizzle project, originally started by Brian Aker

Community supported by Red Hat

Initial release

2018

2008

2014

Current release

2.3, January 2021

1.1.263, October 2022

7.2.4, September 2012

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

GNU GPL

Open Source

Apache 2.0

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

C++

Java

Server operating systems

Linux

Windows

FreeBSD
Linux
OS X

Linux
OS X
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

yes

with proprietary extensions

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

.NET Client API
Proprietary client DLL
WinRT client

JDBC

HTTP REST

Supported programming languages

C++
Java
Python

.Net
C
C#
C++
VB.Net

C
C++
Java
PHP

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

Triggers

no, except callback-events from server when changes happened

hooks for callbacks inside the server can be used.

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Proprietary Sharding system

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

not needed 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.

yes

User concepts

Access control

Access rights for users and roles

Windows-Profile

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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More resources

DB-Engines blog posts

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