DBMS > Microsoft SQL Server vs. Newts vs. Quasardb vs. Splice Machine vs. Valentina Server

System Properties Comparison Microsoft SQL Server vs. Newts vs. Quasardb vs. Splice Machine vs. Valentina Server

Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

Time Series DBMS based on Cassandra

Distributed, high-performance timeseries database

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Object-relational database and reports server

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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Trend Chart

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

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

opennms.github.io/newts

quasar.ai

splicemachine.com

www.valentina-db.net

Technical documentation

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

github.com/OpenNMS/newts/wiki

doc.quasar.ai/master

splicemachine.com/how-it-works

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Microsoft

OpenNMS Group

quasardb

Splice Machine

Paradigma Software

Initial release

1989

2014

2009

2014

1999

Current release

SQL Server 2022, November 2022

3.14.1, January 2024

3.1, March 2021

5.7.5

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache 2.0

commercial

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

Open Source

AGPL 3.0, commercial license available

commercial

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++

Java

C++

Java

Server operating systems

Linux
Windows

Linux
OS X
Windows

BSD
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

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

HTTP REST
Java API

HTTP API

JDBC
Native Spark Datasource
ODBC

ODBC

Supported programming languages

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

Java

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

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

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

yes

Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

based on Cassandra

Sharding

consistent hashing

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

selectable replication factor

based on Cassandra

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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