DBMS > Kinetica vs. Riak TS vs. Stardog vs. TimescaleDB

System Properties Comparison Kinetica vs. Riak TS vs. Stardog vs. TimescaleDB

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Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

www.kinetica.com

www.stardog.com

www.timescale.com

Technical documentation

docs.kinetica.com

www.tiot.jp/riak-docs/riak/ts/latest

docs.stardog.com

docs.timescale.com

Developer

Kinetica

Open Source, formerly Basho Technologies

Stardog-Union

Timescale

Initial release

2012

2015

2010

2017

Current release

7.1, August 2021

3.0.0, September 2022

7.3.0, May 2020

2.13.0, November 2023

License

Commercial or Open Source

commercial

Open Source

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Open Source

Apache 2.0

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++

Erlang

Java

Server operating systems

Linux

Linux
OS X

Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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.

Import/export of XML data possible

yes

Secondary indexes

yes

restricted

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes, limited

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

yes

full PostgreSQL SQL syntax

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

HTTP API
Native Erlang Interface

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

Erlang

user defined functions and aggregates, HTTP Server extensions in Java

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

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

yes

pre-commit hooks and post-commit hooks

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

Multi-source replication in HA-Cluster

Source-replica replication with hot standby and reads on replicas

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

Eventual Consistency

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

relationships in graphs

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

GPU vRAM or System RAM

yes

User concepts

Access control

Access rights for users and roles on table level

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

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