DBMS > Kinetica vs. ReductStore vs. RethinkDB

System Properties Comparison Kinetica vs. ReductStore vs. RethinkDB

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Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

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.

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Relational DBMS

Time Series DBMS

Document store

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.05
Rank	#384	Overall
	#44	Time Series DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Website

www.kinetica.com

github.com/reductstore
www.reduct.store

rethinkdb.com

Technical documentation

docs.kinetica.com

www.reduct.store/docs

rethinkdb.com/docs

Developer

Kinetica

ReductStore LLC

The Linux Foundation

since July 2017

Initial release

2012

2023

2009

Current release

7.1, August 2021

1.9, March 2024

2.4.1, August 2020

License

Commercial or Open Source

commercial

Open Source

Business Source License 1.1

Open Source

Apache Version 2

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, C++

C++, Rust

C++

Server operating systems

Linux

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

string, binary, float, bool, date, geometry

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

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP API

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

C++
JavaScript (Node.js)
Python
Rust

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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

GPU vRAM or System RAM

User concepts

Access control

Access rights for users and roles on table level

yes

users and table-level permissions

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More resources

DB-Engines blog posts

Meet some database management systems you are likely to hear more about in the future
4 August 2014, Paul Andlinger

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Recent citations in the news

Kinetica Delivers Real-Time Vector Similarity Search
21 March 2024, insideBIGDATA

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26 March 2024, Datanami

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18 March 2024, GlobeNewswire

Kinetica ramps up RAG for generative AI, empowering enterprises with real-time operational data
18 March 2024, SiliconANGLE News

Kinetica Delivers Real-Time Vector Similarity Search
22 March 2024, Geospatial World

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