DBMS > ReductStore vs. Tkrzw vs. Transwarp KunDB

System Properties Comparison ReductStore vs. Tkrzw vs. Transwarp KunDB

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Editorial information provided by DB-Engines

Name

Description

Designed to manage unstructured time-series data efficiently, providing unique features such as storing time-stamped blobs with labels, customizable data retention policies, and a straightforward FIFO quota system.

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

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

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

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

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	0.00
Rank	#385	Overall
	#61	Key-value stores

Score	0.04
Rank	#362	Overall
	#154	Relational DBMS

Website

github.com/reductstore
www.reduct.store

dbmx.net/tkrzw

www.transwarp.cn/en/product/kundb

Technical documentation

www.reduct.store/docs

Developer

ReductStore LLC

Mikio Hirabayashi

Transwarp

Initial release

2023

2020

Current release

1.9, March 2024

0.9.3, August 2020

License

Commercial or Open Source

Open Source

Business Source License 1.1

Open Source

Apache Version 2.0

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++, Rust

C++

Server operating systems

Docker
Linux
macOS
Windows

Linux
macOS

Data scheme

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

APIs and other access methods

HTTP API

Supported programming languages

C++
JavaScript (Node.js)
Python
Rust

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

yes

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