DBMS > HEAVY.AI vs. Riak TS vs. Valentina Server vs. VelocityDB

System Properties Comparison HEAVY.AI vs. Riak TS vs. Valentina Server vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Object-relational database and reports server

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Spatial DBMS

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

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.28
Rank	#307	Overall
	#27	Time Series DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

github.com/heavyai/heavydb
www.heavy.ai

www.valentina-db.net

velocitydb.com

Technical documentation

docs.heavy.ai

www.tiot.jp/riak-docs/riak/ts/latest

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

velocitydb.com/UserGuide

Developer

HEAVY.AI, Inc.

Open Source, formerly Basho Technologies

Paradigma Software

VelocityDB Inc

Initial release

2016

2015

1999

2011

Current release

5.10, January 2022

3.0.0, September 2022

5.7.5

7.x

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++ and CUDA

Erlang

Server operating systems

Linux

Linux
OS X

Linux
OS X
Windows

Any that supports .NET

Data scheme

yes

schema-free

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

yes, limited

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

HTTP API
Native Erlang Interface

ODBC

.Net

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

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

.Net

Server-side scripts

Stored procedures

Erlang

yes

Triggers

yes

pre-commit hooks and post-commit hooks

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between datasets can be stored

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

fine grained access rights according to SQL-standard

Based on Windows Authentication

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