DBMS > GeoSpock vs. OpenQM vs. OrigoDB vs. Splice Machine vs. Valentina Server

System Properties Comparison GeoSpock vs. OpenQM vs. OrigoDB vs. Splice Machine vs. Valentina Server

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

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

A fully ACID in-memory object graph database

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Object-relational database and reports server

Primary database model

Relational DBMS

Multivalue DBMS

Document store
Object oriented DBMS

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

geospock.com

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

origodb.com

splicemachine.com

www.valentina-db.net

Technical documentation

origodb.com/docs

splicemachine.com/how-it-works

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

GeoSpock

Rocket Software, originally Martin Phillips

Robert Friberg et al

Splice Machine

Paradigma Software

Initial release

1993

2009

under the name LiveDB

2014

1999

Current release

2.0, September 2019

3.4-12

3.1, March 2021

5.7.5

License

Commercial or Open Source

commercial

Open Source

GPLv2, extended commercial license available

Open Source

AGPL 3.0, commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java, Javascript

Java

Server operating systems

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux
Windows

Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

with some exceptions

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

temporal, categorical

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

JDBC

.NET Client API
HTTP API
LINQ

JDBC
Native Spark Datasource
ODBC

ODBC

Supported programming languages

.Net
Basic
C
Java
Objective C
PHP
Python

.Net

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

yes

Java

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

yes

horizontal partitioning

client side managed; servers are not synchronized

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Write ahead log

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 can be defined per table

Access rights can be defined down to the item level

Role based authorization

Access rights for users, groups and roles according to SQL-standard

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

Cambridge-based data analytics startup GeoSpock lands €4.6 million
2 October 2020, EU-Startups

nChain leads investment round in extreme-scale data firm GeoSpock
2 October 2020, CoinGeek

GeoSpock’s extreme-scale data mission in $5.4m funding boost
8 October 2020, Cambridge Independent

UK-based database GeoSpock bags $5.4m, to expand into
6 October 2020, Tech in Asia

Big data processing techniques to streamline analytics
5 October 2018, TechTarget

provided by Google News

Machine learning data pipeline outfit Splice Machine files for insolvency
26 August 2021, The Register

Splice Machine Launches Feature Store to Simplify Feature Engineering
19 January 2021, Datanami

New Splice Machine RDBMS unites OLTP and OLAP
18 November 2015, CIO

Big Data News: Splice Machine, Carpathia, Altiscale, DataGravity
11 February 2014, Data Center Knowledge

Hadoop-based RDBMS Now Available from Splice
12 May 2014, Datanami

provided by Google News

A Look at Valentina — SitePoint
18 April 2014, SitePoint

MySQL GUI Tools for Windows and Ubuntu/Linux: Top 8 free or open source
7 December 2018, H2S Media

provided by Google News

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