DBMS > DataFS vs. HugeGraph vs. IBM Db2 Event Store

System Properties Comparison DataFS vs. HugeGraph vs. IBM Db2 Event Store

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Editorial information provided by DB-Engines

Name

Description

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.

A fast-speed and highly-scalable Graph DBMS

Distributed Event Store optimized for Internet of Things use cases

Primary database model

Object oriented DBMS

Graph DBMS

Event Store
Time Series DBMS

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#379	Overall
	#19	Object oriented DBMS

Score	0.27
Rank	#337	Overall
	#31	Graph DBMS

Score	0.21
Rank	#351	Overall
	#3	Event Stores
	#31	Time Series DBMS

Website

newdatabase.com

github.com/hugegraph
hugegraph.apache.org

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

Technical documentation

dev.mobiland.com/Overview.xsp

hugegraph.apache.org/docs

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

Developer

Mobiland AG

Baidu

IBM

Initial release

2018

2017

Current release

1.1.263, October 2022

0.9

2.0

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free developer edition available

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

Java

C and C++

Server operating systems

Windows

Linux
macOS
Unix

Linux

Linux, macOS, Windows for the developer addition

Data scheme

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

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.

Secondary indexes

yes

also supports composite index and range index

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
C
C#
C++
VB.Net

Groovy
Java
Python

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

Server-side scripts

Stored procedures

asynchronous Gremlin script jobs

yes

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

yes

depending on used storage backend, e.g. Cassandra and HBase

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on used storage backend, e.g. Cassandra and HBase

Active-active shard replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

Transaction concepts

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

ACID

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

Windows-Profile

Users, roles and permissions

fine grained access rights according to SQL-standard

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