DBMS > Microsoft SQL Server vs. Newts vs. OrigoDB vs. Percona Server for MongoDB vs. Riak TS

System Properties Comparison Microsoft SQL Server vs. Newts vs. OrigoDB vs. Percona Server for MongoDB vs. Riak TS

Editorial information provided by DB-Engines

Name

Description

Microsofts flagship relational DBMS

Time Series DBMS based on Cassandra

A fully ACID in-memory object graph database

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Relational DBMS

Time Series DBMS

Document store
Object oriented DBMS

Document store

Time Series DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Website

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

opennms.github.io/newts

origodb.com

www.percona.com/mongodb/software/percona-server-for-mongodb

Technical documentation

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

github.com/OpenNMS/newts/wiki

origodb.com/docs

docs.percona.com/percona-distribution-for-mongodb

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Microsoft

OpenNMS Group

Robert Friberg et al

Percona

Open Source, formerly Basho Technologies

Initial release

1989

2014

2009

under the name LiveDB

2015

Current release

SQL Server 2022, November 2022

3.4.10-2.10, November 2017

3.0.0, September 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache 2.0

Open Source

GPL Version 2

Open Source

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++

Erlang

Server operating systems

Linux
Windows

Linux
OS X
Windows

Linux
Windows

Linux

Linux
OS X

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

yes, limited

APIs and other access methods

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

HTTP REST
Java API

.NET Client API
HTTP API
LINQ

proprietary protocol using JSON

HTTP API
Native Erlang Interface

Supported programming languages

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

Java

.Net

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

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

yes

JavaScript

Erlang

Triggers

yes

Domain Events

yes

pre-commit hooks and post-commit hooks

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

horizontal partitioning

client side managed; servers are not synchronized

Sharding

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

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

depending on model

links between datasets can be stored

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

Write ahead log

yes

In-memory capabilities

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

yes

via In-Memory Engine

User concepts

Access control

fine grained access rights according to SQL-standard

Role based authorization

Access rights for users and roles

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