DBMS > GeoSpock vs. Heroic vs. OpenQM vs. Valentina Server vs. Vitess

System Properties Comparison GeoSpock vs. Heroic vs. OpenQM vs. Valentina Server vs. Vitess

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

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

Object-relational database and reports server

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.34
Rank	#284	Overall
	#10	Multivalue DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

geospock.com

github.com/spotify/heroic

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

www.valentina-db.net

vitess.io

Technical documentation

spotify.github.io/heroic

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

vitess.io/docs

Developer

GeoSpock

Spotify

Rocket Software, originally Martin Phillips

Paradigma Software

The Linux Foundation, PlanetScale

Initial release

2014

1993

1999

2013

Current release

2.0, September 2019

3.4-12

5.7.5

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

GPLv2, extended commercial license available

commercial

Open Source

Apache Version 2.0, commercial licenses available

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
OS X
Windows

Docker
Linux
macOS

Data scheme

yes

schema-free

yes

with some exceptions

yes

Typing

predefined data types such as float or date

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

temporal, categorical

yes

via Elasticsearch

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

with proprietary extensions

APIs and other access methods

JDBC

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.Net
Basic
C
Java
Objective C
PHP
Python

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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 can be defined per table

Access rights can be defined down to the item level

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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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

Smart Cities, Autonomous Vehicles, Artificial General Intelligence Robotics: Q&A with Steve Marsh, GeoSpock
16 May 2018, ExchangeWire

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

provided by Google News

Review: Google Bigtable scales with ease
7 September 2016, InfoWorld

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

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

They scaled YouTube -- now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

PlanetScale Serves up Vitess-Powered Serverless MySQL
23 November 2021, The New Stack

PlanetScale offers undo button to reverse schema migration without losing data
24 March 2022, The Register

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

provided by Google News

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