DBMS > Atos Standard Common Repository vs. Microsoft SQL Server vs. Riak TS vs. Solr vs. SWC-DB

System Properties Comparison Atos Standard Common Repository vs. Microsoft SQL Server vs. Riak TS vs. Solr vs. SWC-DB

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Microsofts flagship relational DBMS

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

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

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

Time Series DBMS

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

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

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

solr.apache.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

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

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

solr.apache.org/resources.html

Developer

Atos Convergence Creators

Microsoft

Open Source, formerly Basho Technologies

Apache Software Foundation

Alex Kashirin

Initial release

2016

1989

2015

2006

2020

Current release

1703

SQL Server 2022, November 2022

3.0.0, September 2022

9.6.0, April 2024

0.5, April 2021

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache Version 2

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Erlang

Java

C++

Server operating systems

Linux

Linux
Windows

Linux
OS X

All OS with a Java VM

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

Linux

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

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

schema-free

Typing

predefined data types such as float or date

optional

yes

supports customizable data types and automatic typing

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

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

yes, limited

Solr Parallel SQL Interface

SQL-like query language

APIs and other access methods

LDAP

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

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

Proprietary protocol
Thrift

Supported programming languages

All languages with LDAP bindings

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

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

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

C++

Server-side scripts

Stored procedures

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

Erlang

Java plugins

Triggers

yes

pre-commit hooks and post-commit hooks

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

Atomic execution of specific operations

ACID

optimistic locking

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

User concepts

Access control

LDAP bind authentication

fine grained access rights according to SQL-standard

yes

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