DBMS > Coveo vs. IBM Db2 Event Store vs. RocksDB vs. Transwarp KunDB

System Properties Comparison Coveo vs. IBM Db2 Event Store vs. RocksDB vs. Transwarp KunDB

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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

Distributed Event Store optimized for Internet of Things use cases

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Primary database model

Search engine

Event Store
Time Series DBMS

Key-value store

Relational DBMS

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

Score	2.28
Rank	#114	Overall
	#11	Search engines

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

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.10
Rank	#344	Overall
	#151	Relational DBMS

Website

www.coveo.com

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

rocksdb.org

www.transwarp.cn/en/product/kundb

Technical documentation

docs.coveo.com

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

github.com/facebook/rocksdb/wiki

Developer

Coveo

IBM

Facebook, Inc.

Transwarp

Initial release

2012

2017

2013

Current release

2.0

8.11.4, April 2024

License

Commercial or Open Source

commercial

free developer edition available

Open Source

BSD

commercial

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 and C++

C++

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

Linux

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

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

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

C++ API
Java API

Supported programming languages

C#
Java
JavaScript
Python

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

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

yes

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

User concepts

Access control

granular access controls, API key management, content filters

fine grained access rights according to SQL-standard

yes

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