DBMS > IBM Db2 Event Store vs. RDF4J vs. TinkerGraph vs. Transwarp Hippo

System Properties Comparison IBM Db2 Event Store vs. RDF4J vs. TinkerGraph vs. Transwarp Hippo

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Event Store
Time Series DBMS

RDF store

Graph DBMS

Vector DBMS

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

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	0.13
Rank	#345	Overall
	#35	Graph DBMS

Score	0.05
Rank	#386	Overall
	#14	Vector DBMS

Website

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

rdf4j.org

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

www.transwarp.cn/en/subproduct/hippo

Technical documentation

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

rdf4j.org/documentation

Developer

IBM

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2017

2004

2009

2023

Current release

2.0

1.0, May 2023

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache 2.0

commercial

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 and C++

Java

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Unix
Windows

Linux
macOS

Data scheme

yes

RDF Schemas

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and 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

yes

through the embedded Spark runtime

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

TinkerPop 3

RESTful HTTP API

Supported programming languages

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

Java
PHP
Python

Groovy
Java

C++
Java
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

No - written data is immutable

yes

Durability

Support for making data persistent

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

yes

in-memory storage is supported as well

optional

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

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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