DBMS > AnzoGraph DB vs. IRONdb vs. Microsoft SQL Server vs. MonetDB

System Properties Comparison AnzoGraph DB vs. IRONdb vs. Microsoft SQL Server vs. MonetDB

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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

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

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

Microsofts flagship relational DBMS

A relational database management system that stores data in columns

Primary database model

Graph DBMS
RDF store

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Website

cambridgesemantics.com/anzograph

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

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

www.monetdb.org

Technical documentation

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

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

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

www.monetdb.org/Documentation

Developer

Cambridge Semantics

Circonus LLC.

Microsoft

MonetDB BV

Initial release

2018

2017

1989

2004

Current release

2.3, January 2021

V0.10.20, January 2018

SQL Server 2022, November 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

free trial version available

commercial

restricted free version is available

Open Source

Mozilla Public License 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

C++

Server operating systems

Linux

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

Schema-free and OWL/RDFS-schema support

schema-free

yes

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

Secondary indexes

yes

SQL

Support of SQL

SPARQL and SPARQL* as primary query language. Cypher preview.

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

yes

SQL 2003 with some extensions

APIs and other access methods

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

HTTP API

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

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

C++
Java
Python

.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++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

yes, in Lua

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

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Automatic, metric affinity per node

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

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication in MPP-Cluster

configurable replication factor, datacenter aware

yes, but depending on the SQL-Server Edition

none

Source-replica replication available in experimental status

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

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

In-memory capabilities

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

yes

User concepts

Access control

Access rights for users and roles

fine grained access rights according to SQL-standard

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