DBMS > Apache Hive vs. ReductStore vs. Transwarp KunDB

System Properties Comparison Apache Hive vs. ReductStore vs. Transwarp KunDB

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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.

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

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	53.09
Rank	#18	Overall
	#12	Relational DBMS

Score	0.02
Rank	#371	Overall
	#40	Time Series DBMS

Score	0.02
Rank	#375	Overall
	#156	Relational DBMS

Website

hive.apache.org

github.com/reductstore
www.reduct.store

www.transwarp.cn/en/product/kundb

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

www.reduct.store/docs

Developer

Apache Software Foundation

initially developed by Facebook

ReductStore LLC

Transwarp

Initial release

2012

2023

Current release

3.1.3, April 2022

1.9, March 2024

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Business Source License 1.1

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++, Rust

Server operating systems

All OS with a Java VM

Docker
Linux
macOS
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
Thrift

HTTP API

Supported programming languages

C++
Java
PHP
Python

C++
JavaScript (Node.js)
Python
Rust

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

User concepts

Access control

Access rights for users, groups and roles

yes

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