DBMS > GBase vs. Microsoft SQL Server vs. NuoDB vs. Riak TS vs. Solr

System Properties Comparison GBase vs. Microsoft SQL Server vs. NuoDB vs. Riak TS vs. Solr

Editorial information provided by DB-Engines

Name

Description

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Microsofts flagship relational DBMS

NuoDB is a webscale distributed database that supports SQL and ACID transactions

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

Primary database model

Relational DBMS

Time Series DBMS

Search engine

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS

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

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

www.gbase.cn

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

www.3ds.com/nuodb-distributed-sql-database

solr.apache.org

Technical documentation

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

doc.nuodb.com

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

solr.apache.org/resources.html

Developer

General Data Technology Co., Ltd.

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

Open Source, formerly Basho Technologies

Apache Software Foundation

Initial release

2004

1989

2013

2015

2006

Current release

GBase 8a, GBase 8s, GBase 8c

SQL Server 2022, November 2022

3.0.0, September 2022

9.6.0, April 2024

License

Commercial or Open Source

commercial

restricted free version is available

commercial

limited edition free

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, Java, Python

C++

Erlang

Java

Server operating systems

Linux

Linux
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux
OS X

All OS with a Java VM

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

Data scheme

yes

schema-free

yes

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

Typing

predefined data types such as float or date

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

Standard with numerous extensions

yes

yes, limited

Solr Parallel SQL Interface

APIs and other access methods

ADO.NET
C API
JDBC
ODBC

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

ADO.NET
JDBC
ODBC

HTTP API
Native Erlang Interface

Java API
RESTful HTTP/JSON API

Supported programming languages

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

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

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

Server-side scripts

Stored procedures

user defined functions

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

Java, SQL

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

horizontal partitioning (by range, list and hash) and vertical partitioning

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

data is dynamically stored/cached on the nodes where it is read/written

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

yes, but depending on the SQL-Server Edition

yes

Managed transparently by NuoDB

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

Eventual 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

ACID

tunable commit protocol

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

Temporary table

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

yes

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