DBMS > IBM Db2 Event Store vs. Postgres-XL vs. RDF4J vs. SAP Advantage Database Server

System Properties Comparison IBM Db2 Event Store vs. Postgres-XL vs. RDF4J vs. SAP Advantage Database Server

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Editorial information provided by DB-Engines

Name

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Distributed Event Store optimized for Internet of Things use cases

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

Low-cost RDBMS with access to ISAM and FoxPro data structures

Primary database model

Event Store
Time Series DBMS

Relational DBMS

RDF store

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

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

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

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

www.postgres-xl.org

rdf4j.org

www.sap.com/products/advantage-database-server.html

Technical documentation

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

www.postgres-xl.org/documentation

rdf4j.org/documentation

Developer

IBM

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

Sybase, SAP

Initial release

2017

2014

since 2012, originally named StormDB

2004

1993

Current release

2.0

10 R1, October 2018

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Mozilla public license

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS

Linux
OS X
Unix
Windows

Linux
Windows

Data scheme

yes

RDF Schemas

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

distributed, parallel query execution

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

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

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
PHP
Python

Delphi
Perl
PHP

Server-side scripts

Stored procedures

yes

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

none

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

MVCC

ACID

Isolation support depends on the API used

ACID

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

yes

In-memory capabilities

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

yes

caching of (temporary) tables can be configured

User concepts

Access control

fine grained access rights according to SQL-standard

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