DBMS > Citus vs. Hive vs. Kinetica vs. MySQL vs. PostGIS

System Properties Comparison Citus vs. Hive vs. Kinetica vs. MySQL vs. PostGIS

Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

Fully vectorized database across both GPUs and CPUs

Widely used open source RDBMS

Spatial extension of PostgreSQL

Primary database model

Relational DBMS

Key/Value like access via memcached API

Spatial DBMS

Secondary database models

Document store

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

Relational DBMS

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

Score	2.15
Rank	#123	Overall
	#60	Relational DBMS

Score	62.59
Rank	#18	Overall
	#12	Relational DBMS

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	23.90
Rank	#29	Overall
	#1	Spatial DBMS

Website

Technical documentation

docs.citusdata.com

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

docs.kinetica.com

dev.mysql.com/doc

postgis.net/documentation

Developer

Apache Software Foundation

initially developed by Facebook

Kinetica

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2010

2012

1995

2005

Current release

8.1, December 2018

3.1.3, April 2022

7.1, August 2021

8.3.0, January 2024

3.4.2, February 2024

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Apache Version 2

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL v2.0

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Java

C, C++

C and C++

Server operating systems

Linux

All OS with a Java VM

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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.

yes

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

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

JDBC
ODBC
Thrift

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

C++
Java
PHP
Python

C++
Java
JavaScript (Node.js)
Python

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

yes

user defined functions and integration of map-reduce

user defined functions

yes

proprietary syntax

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

selectable replication factor

Source-replica replication

Multi-source replication
Source-replica replication

yes

based on PostgreSQL

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

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

Access rights for users and roles on table level

Users with fine-grained authorization concept

no user groups or roles

yes

based on PostgreSQL

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.