DBMS > IBM Db2 Event Store vs. Oracle Rdb vs. Riak KV vs. TimesTen

System Properties Comparison IBM Db2 Event Store vs. Oracle Rdb vs. Riak KV vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

Distributed, fault tolerant key-value store

In-Memory RDBMS compatible to Oracle

Primary database model

Event Store
Time Series DBMS

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational 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	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

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

www.oracle.com/database/technologies/related/rdb.html

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

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

www.oracle.com/database/technologies/related/rdb-doc.html

www.tiot.jp/riak-docs/riak/kv/latest

docs.oracle.com/database/timesten-18.1

Developer

IBM

Oracle, originally developed by Digital Equipment Corporation (DEC)

OpenSource, formerly Basho Technologies

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2017

1984

2009

1998

Current release

2.0

7.4.1.1, 2021

3.2.0, December 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free developer edition available

commercial

Open Source

Apache version 2, commercial enterprise edition

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C and C++

Erlang

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

HP Open VMS

Linux
OS X

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

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.

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP API
Native Erlang Interface

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

Erlang

PL/SQL

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

links between data sets can be stored

yes

Transaction concepts

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

yes, on a single node

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

by means of logfiles and checkpoints

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

yes, using Riak Security

fine grained access rights according to SQL-standard

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