DBMS > Alibaba Cloud PolarDB vs. EsgynDB vs. IBM Db2 Event Store vs. KairosDB vs. RDF4J

System Properties Comparison Alibaba Cloud PolarDB vs. EsgynDB vs. IBM Db2 Event Store vs. KairosDB vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

A cloud-native relational database compatible with MySQL, PostgreSQL, and Oracle. Designed for business critical applications.

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

Distributed Event Store optimized for Internet of Things use cases

Distributed Time Series DBMS based on Cassandra or H2

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

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Time Series DBMS

RDF store

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

Score	1.27
Rank	#160	Overall
	#71	Relational DBMS

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	0.17
Rank	#316	Overall
	#2	Event Stores
	#26	Time Series DBMS

Score	0.54
Rank	#239	Overall
	#20	Time Series DBMS

Score	0.80
Rank	#205	Overall
	#7	RDF stores

Website

www.alibabacloud.com/product/polardb

www.esgyn.cn

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

github.com/kairosdb/kairosdb

rdf4j.org

Technical documentation

www.alibabacloud.com/help/en/polardb/product-overview

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

kairosdb.github.io

rdf4j.org/documentation

Developer

Alibaba

Esgyn

IBM

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

Initial release

2015

2017

2013

2004

Current release

2.0

1.2.2, November 2018

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Apache 2.0

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C++, Java

C and C++

Java

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

schema-free

yes

RDF Schemas

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

Secondary indexes

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Graphite protocol
HTTP REST
Telnet API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

Java
Python

All languages supporting JDBC/ODBC/ADO.Net

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

Java
JavaScript

Node.js
PHP
Python

Java
PHP
Python

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

Multi-source replication between multi datacenters

Active-active shard replication

selectable replication factor

based on Cassandra

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

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

yes

No - written data is immutable

yes

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

yes

in-memory storage is supported as well

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

simple password-based access control

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Licensing and pricing models

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