DBMS > Coveo vs. Cubrid vs. IBM Db2 Event Store vs. Tkrzw

System Properties Comparison Coveo vs. Cubrid vs. IBM Db2 Event Store vs. Tkrzw

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Editorial information provided by DB-Engines

Name

Description

AI-powered hosted search, recommendation and personalization platform providing tools for both low-code and full-code development

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Distributed Event Store optimized for Internet of Things use cases

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Search engine

Relational DBMS

Event Store
Time Series DBMS

Key-value store

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

Score	2.11
Rank	#118	Overall
	#11	Search engines

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

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

Score	0.07
Rank	#372	Overall
	#57	Key-value stores

Website

www.coveo.com

cubrid.com (korean)
cubrid.org (english)

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

dbmx.net/tkrzw

Technical documentation

docs.coveo.com

cubrid.org/manuals

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

Developer

Coveo

CUBRID Corporation, CUBRID Foundation

IBM

Mikio Hirabayashi

Initial release

2012

2008

2017

2020

Current release

11.0, January 2021

2.0

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

free developer edition available

Open Source

Apache Version 2.0

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, C++, Java

C and C++

C++

Server operating systems

hosted

Linux
Windows

Linux

Linux, macOS, Windows for the developer addition

Linux
macOS

Data scheme

hybrid - fields need to be configured prior to indexing, but relationships can be exploited at query time without pre-configuration

yes

schema-free

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

SQL

Support of SQL

yes

through the embedded Spark runtime

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Supported programming languages

C#
Java
JavaScript
Python

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

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

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

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 capabilities

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

yes

using specific database classes

User concepts

Access control

granular access controls, API key management, content filters

fine grained access rights according to SQL-standard

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