DBMS > Kinetica vs. PostGIS vs. Postgres-XL vs. Quasardb

System Properties Comparison Kinetica vs. PostGIS vs. Postgres-XL vs. Quasardb

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Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Spatial extension of PostgreSQL

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

Distributed, high-performance timeseries database

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

Document store
Spatial DBMS

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

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	23.90
Rank	#29	Overall
	#1	Spatial DBMS

Score	0.52
Rank	#256	Overall
	#117	Relational DBMS

Score	0.19
Rank	#327	Overall
	#30	Time Series DBMS

Website

Technical documentation

docs.kinetica.com

postgis.net/documentation

www.postgres-xl.org/documentation

doc.quasar.ai/master

Developer

Kinetica

quasardb

Initial release

2012

2005

2014

since 2012, originally named StormDB

2009

Current release

7.1, August 2021

3.4.2, February 2024

10 R1, October 2018

3.14.1, January 2024

License

Commercial or Open Source

commercial

Open Source

GPL v2.0

Open Source

Mozilla public license

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++

Server operating systems

Linux

Linux
macOS

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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.

yes

XML type, but no XML query functionality

Secondary indexes

yes

with tags

SQL

Support of SQL

SQL-like DML and DDL statements

yes

distributed, parallel query execution

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

HTTP API

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

based on PostgreSQL

horizontal partitioning

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

based on PostgreSQL

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

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

ACID

MVCC

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

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

Transient mode

User concepts

Access control

Access rights for users and roles on table level

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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