DBMS > GeoSpock vs. Ingres vs. Microsoft Access vs. Microsoft Azure Cosmos DB vs. OpenQM

System Properties Comparison GeoSpock vs. Ingres vs. Microsoft Access vs. Microsoft Azure Cosmos DB vs. OpenQM

Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Spatial and temporal data processing engine for extreme data scale

Well established RDBMS

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Globally distributed, horizontally scalable, multi-model database service

QpenQM is a high-performance, self-tuning, multi-value DBMS

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Multivalue DBMS

Secondary database models

Time Series DBMS

Spatial DBMS

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

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Website

geospock.com

www.actian.com/databases/ingres

www.microsoft.com/en-us/microsoft-365/access

azure.microsoft.com/services/cosmos-db

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

Technical documentation

docs.actian.com/ingres

developer.microsoft.com/en-us/access

learn.microsoft.com/azure/cosmos-db

Developer

GeoSpock

Actian Corporation

Microsoft

Rocket Software, originally Martin Phillips

Initial release

1974

originally developed at University Berkely in early 1970s

1992

2014

1993

Current release

2.0, September 2019

11.2, May 2022

1902 (16.0.11328.20222), March 2019

3.4-12

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

commercial

Open Source

GPLv2, extended commercial license available

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, Javascript

C++

Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Windows

Not a real database server, but making use of DLLs

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Data scheme

yes

schema-free

yes

with some exceptions

Typing

predefined data types such as float or date

yes

JSON types

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

temporal, categorical

yes

All properties auto-indexed by default

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

but not compliant to any SQL standard

SQL-like query language

APIs and other access methods

JDBC

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

ADO.NET
DAO
ODBC
OLE DB

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Supported programming languages

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

.Net
Basic
C
Java
Objective C
PHP
Python

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

JavaScript

yes

Triggers

yes

since Access 2010 using the ACE-engine

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

horizontal partitioning

Ingres Star to access multiple databases simultaneously

none

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Ingres Replicator

none

yes

Implicit feature of the cloud service

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

but no files for transaction logging

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

In-memory capabilities

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

User concepts

Access control

Access rights for users can be defined per table

fine grained access rights according to SQL-standard

a simple user-level security was built in till version Access 2003

Access rights can be defined down to the item level

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