DBMS > Kinetica vs. Microsoft SQL Server vs. Realm vs. Solr vs. UniData,UniVerse

System Properties Comparison Kinetica vs. Microsoft SQL Server vs. Realm vs. Solr vs. UniData,UniVerse

Editorial information provided by DB-Engines

Name

Description

Fully vectorized database across both GPUs and CPUs

Microsofts flagship relational DBMS

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

A widely used distributed, scalable search engine based on Apache Lucene

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	3.16
Rank	#97	Overall
	#2	Multivalue DBMS

Website

www.kinetica.com

www.microsoft.com/en-us/sql-server

realm.io

solr.apache.org

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

docs.kinetica.com

learn.microsoft.com/en-US/sql/sql-server

realm.io/docs

solr.apache.org/resources.html

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

Kinetica

Microsoft

Realm, acquired by MongoDB in May 2019

Apache Software Foundation

Rocket Software

Initial release

2012

1989

2014

2006

1985

Current release

7.1, August 2021

SQL Server 2022, November 2022

9.6.0, April 2024

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++

C++

Java

Server operating systems

Linux

Linux
Windows

Android
Backend: server-less
iOS
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

Dynamic Fields enables on-the-fly addition of new fields

schema-free

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

optional

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

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Java API
RESTful HTTP/JSON API

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
Java

with Android only
Objective-C
React Native
Swift

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

user defined functions

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

runs within the applications so server-side scripts are unnecessary

Java plugins

yes

Triggers

yes

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

yes

Change Listeners

yes

User configurable commands triggered on index changes

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, but depending on the SQL-Server Edition

none

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

ACID

configurable

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

In-Memory realm

yes

User concepts

Access control

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

yes

Access rights according to SQL-standard and operating system based

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