DBMS > AnzoGraph DB vs. Google Cloud Datastore vs. H2GIS vs. Microsoft SQL Server vs. MonetDB

System Properties Comparison AnzoGraph DB vs. Google Cloud Datastore vs. H2GIS vs. Microsoft SQL Server vs. MonetDB

Editorial information provided by DB-Engines

Name

Description

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Spatial extension of H2

Microsofts flagship relational DBMS

A relational database management system that stores data in columns

Primary database model

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	0.23
Rank	#307	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Website

cambridgesemantics.com/anzograph

cloud.google.com/datastore

www.h2gis.org

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

www.monetdb.org

Technical documentation

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

cloud.google.com/datastore/docs

www.h2gis.org/docs/home

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

www.monetdb.org/Documentation

Developer

Cambridge Semantics

Google

CNRS

Microsoft

MonetDB BV

Initial release

2018

2008

2013

1989

2004

Current release

2.3, January 2021

SQL Server 2022, November 2022

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

free trial version available

commercial

Open Source

LGPL 3.0

commercial

restricted free version is available

Open Source

Mozilla Public License 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

Linux

hosted

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, details here

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 (GQL)

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

gRPC (using protocol buffers) API
RESTful HTTP/JSON 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
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java

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

using Google App Engine

yes

based on H2

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

yes, in SQL, C, R

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

none

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

Multi-source replication using Paxos

yes

based on H2

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

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency in MPP-Cluster

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

not needed in graphs

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (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

User concepts

Access control

Access rights for users and roles

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

based on H2

fine grained access rights according to SQL-standard

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