DBMS > IBM Db2 Event Store vs. Quasardb vs. RavenDB vs. TimesTen

System Properties Comparison IBM Db2 Event Store vs. Quasardb vs. RavenDB 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, high-performance timeseries database

Open Source Operational and Transactional Enterprise NoSQL Document Database

In-Memory RDBMS compatible to Oracle

Primary database model

Event Store
Time Series DBMS

Time Series DBMS

Document store

Relational DBMS

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

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

quasar.ai

ravendb.net

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

Technical documentation

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

doc.quasar.ai/master

ravendb.net/docs

docs.oracle.com/database/timesten-18.1

Developer

IBM

quasardb

Hibernating Rhinos

Oracle, TimesTen Performance Software, HP

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

Initial release

2017

2009

2010

1998

Current release

2.0

3.14.1, January 2024

5.4, July 2022

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free developer edition available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

AGPL version 3, commercial license available

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++

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

BSD
Linux
OS X
Windows

Linux
macOS
Raspberry Pi
Windows

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

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

SQL-like query language

SQL-like query language (RQL)

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Source-replica replication with selectable replication factor

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, Cluster-wide transaction available

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 using LevelDB

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

Transient mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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