DBMS > AllegroGraph vs. Amazon DynamoDB vs. Hawkular Metrics vs. IBM Db2 Event Store

System Properties Comparison AllegroGraph vs. Amazon DynamoDB vs. Hawkular Metrics vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

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

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Document store

with version 6.5
Graph DBMS
RDF store
Vector DBMS

Document store
Key-value store

Time Series DBMS

Event Store
Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.84
Rank	#204	Overall
	#35	Document stores
	#18	Graph DBMS
	#7	RDF stores
	#10	Vector DBMS

Score	70.06
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	0.01
Rank	#377	Overall
	#39	Time Series DBMS

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Website

allegrograph.com

aws.amazon.com/dynamodb

www.hawkular.org

www.ibm.com/products/db2-event-store

Technical documentation

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

docs.aws.amazon.com/dynamodb

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

www.ibm.com/docs/en/db2-event-store

Developer

Franz Inc.

Amazon

Community supported by Red Hat

IBM

Initial release

2004

2012

2014

2017

Current release

8.0, December 2023

2.0

License

Commercial or Open Source

commercial

Limited community edition free

commercial

free tier for a limited amount of database operations

Open Source

Apache 2.0

commercial

free developer edition available

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 and C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Windows

Linux

Linux, macOS, Windows for the developer addition

Data scheme

yes

RDF schemas

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

through the embedded Spark runtime

APIs and other access methods

RESTful HTTP API
SPARQL

RESTful HTTP API

HTTP REST

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C#
Clojure
Java
Lisp
Perl
Python
Ruby
Scala

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

Go
Java
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Server-side scripts

Stored procedures

yes

JavaScript or Common Lisp

yes

Triggers

yes

by integration with AWS Lambda

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

yes

selectable replication factor

based on Cassandra

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

AllegroGraph Named a 2024 Trend Setting Product
16 September 2024

Gartner recognized Franz Inc. as a Key Neuro-Symbolic AI Provider in 2024 Hype Cycle for AI
10 September 2024

Next-Generation Cybersecurity: Integrating Knowledge Graphs and Neuro-Symbolic AI with STIX and TAXII
10 September 2024

Pruning Cycles in UMLS Metathesaurus: A Neuro Symbolic AI Approach
9 September 2024

Awesome AI Companies – Franz Inc.
17 July 2024

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