DBMS > AllegroGraph vs. EsgynDB vs. Hawkular Metrics vs. Trafodion

System Properties Comparison AllegroGraph vs. EsgynDB vs. Hawkular Metrics vs. Trafodion

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

High performance, persistent RDF store with additional support for Graph DBMS

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

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

Transactional SQL-on-Hadoop DBMS

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	1.06
Rank	#187	Overall
	#30	Document stores
	#17	Graph DBMS
	#7	RDF stores
	#7	Vector DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Website

Technical documentation

franz.com/agraph/support/documentation/current/agraph-introduction.html

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

trafodion.apache.org/documentation.html

Developer

Franz Inc.

Esgyn

Community supported by Red Hat

Apache Software Foundation, originally developed by HP

Initial release

2004

2015

2014

Current release

8.0, December 2023

2.3.0, February 2019

License

Commercial or Open Source

commercial

Limited community edition free

commercial

Open Source

Apache 2.0

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

Java

C++, Java

Server operating systems

Linux
OS X
Windows

Linux

Linux
OS X
Windows

Linux

Data scheme

yes

RDF schemas

yes

schema-free

yes

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.

bulk load of XML files possible

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

yes

APIs and other access methods

RESTful HTTP API
SPARQL

ADO.NET
JDBC
ODBC

HTTP REST

ADO.NET
JDBC
ODBC

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

All languages supporting JDBC/ODBC/ADO.Net

Go
Java
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

Java Stored Procedures

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

with Federation

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Multi-source replication between multi datacenters

selectable replication factor

based on Cassandra

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

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

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Knowledge Graph Platform Leader FedShard - Designed for Entity-Event Knowledge Graph...
» more

Competitive advantages

AllegroGraph is uniquely suited to support adhoc queries through SPARQL, Prolog and...
» more

News

How a Neuro-Symbolic AI Approach Can Improve Trust in AI Apps
23 May 2024

Can Neuro-Symbolic AI Solve AI’s Weaknesses?
17 April 2024

100 Companies That Matter in KM – Franz Inc.
3 April 2024

Exploring AllegroGraph v8 – Unleashing the Power of Neuro-Symbolic AI (Recorded Webinar)
9 February 2024

What is Neuro-Symbolic AI?
23 January 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Q&A: Can Neuro-Symbolic AI Solve AI’s Weaknesses?
8 April 2024, TDWI

AI predictions for 2024 unveil exciting technological horizons
21 November 2023, Wire19

Neuro-Symbolic AI: The Peak of Artificial Intelligence
16 November 2021, AiThority

The Foundation of Data Fabrics and AI: Semantic Knowledge Graphs - DataScienceCentral.com
19 May 2022, Data Science Central

Franz Releases the First Neuro-Symbolic AI Platform Merging Knowledge Graphs, Generative AI, and Vector Storage
11 December 2023, Datanami

provided by Google News

Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived… • DEVCLASS
5 June 2019, DevClass

provided by Google News

Evaluating HTAP Databases for Machine Learning Applications
2 November 2016, KDnuggets

Low-latency, distributed database architectures are critical for emerging fog applications
7 April 2022, Embedded Computing Design

provided by Google News

Share this page