DBMS > IRONdb vs. Microsoft SQL Server vs. Oracle NoSQL vs. Riak KV

System Properties Comparison IRONdb vs. Microsoft SQL Server vs. Oracle NoSQL vs. Riak KV

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Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Microsofts flagship relational DBMS

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Distributed, fault tolerant key-value store

Primary database model

Time Series DBMS

Relational DBMS

Document store
Key-value store
Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Website

www.circonus.com/solutions/time-series-database/

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

www.oracle.com/database/nosql/technologies/nosql

Technical documentation

docs.circonus.com/irondb/category/getting-started

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

docs.oracle.com/en/database/other-databases/nosql-database/index.html

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

Developer

Circonus LLC.

Microsoft

Oracle

OpenSource, formerly Basho Technologies

Initial release

2017

1989

2011

2009

Current release

V0.10.20, January 2018

SQL Server 2022, November 2022

23.3, December 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

C++

Java

Erlang

Server operating systems

Linux

Linux
Windows

Linux
Solaris SPARC/x86

Linux
OS X

Data scheme

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

yes

optional

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

SQL

Support of SQL

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

SQL-like DML and DDL statements

APIs and other access methods

HTTP API

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

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

C
C#
Go
Java
JavaScript (Node.js)
Python

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

yes, in Lua

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

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Automatic, metric affinity per node

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

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

configurable replication factor, datacenter aware

yes, but depending on the SQL-Server Edition

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

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

off heap cache

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

yes, using Riak Security

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