DBMS > Microsoft SQL Server vs. Quasardb vs. Rockset vs. Sadas Engine vs. Stardog

System Properties Comparison Microsoft SQL Server vs. Quasardb vs. Rockset vs. Sadas Engine vs. Stardog

Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

Distributed, high-performance timeseries database

A scalable, reliable search and analytics service in the cloud, built on RocksDB

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS
Search engine

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Trend Chart

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.82
Rank	#212	Overall
	#36	Document stores

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

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

Technical documentation

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

doc.quasar.ai/master

docs.rockset.com

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.stardog.com

Developer

Microsoft

quasardb

Rockset

SADAS s.r.l.

Stardog-Union

Initial release

1989

2009

2019

2006

2010

Current release

SQL Server 2022, November 2022

3.14.1, January 2024

8.0

7.3.0, May 2020

License

Commercial or Open Source

commercial

restricted free version is available

commercial

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

commercial

free trial version available

commercial

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

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux
Windows

BSD
Linux
OS X
Windows

hosted

AIX
Linux
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free

yes

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

integer and binary

dynamic typing

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

ingestion from XML files supported

Import/export of XML data possible

Secondary indexes

yes

with tags

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL-like query language

Read-only SQL queries, including JOINs

yes

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

APIs and other access methods

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

HTTP API

HTTP REST

JDBC
ODBC
Proprietary protocol

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

Supported programming languages

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

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

Go
Java
JavaScript (Node.js)
Python

.Net
C
C#
C++
Groovy
Java
PHP
Python

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

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

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

consistent hashing

Automatic sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

Source-replica replication with selectable replication factor

yes

none

Multi-source replication in HA-Cluster

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

Eventual Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

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

managed by 'Learn by Usage'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Cryptographically strong user authentication and audit trail

Access rights for users and organizations can be defined via Rockset console

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

Access rights for users and roles

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