DBMS > Hyprcubd vs. Machbase Neo vs. Spark SQL vs. Trafodion vs. Valentina Server

System Properties Comparison Hyprcubd vs. Machbase Neo vs. Spark SQL vs. Trafodion vs. Valentina Server

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Serverless Time Series DBMS

TimeSeries DBMS for AIoT and BigData

Spark SQL is a component on top of 'Spark Core' for structured data processing

Transactional SQL-on-Hadoop DBMS

Object-relational database and reports server

Primary database model

Time Series DBMS

Relational DBMS

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

Score	0.12
Rank	#339	Overall
	#30	Time Series DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

hyprcubd.com (offline)

Technical documentation

machbase.com/dbms

spark.apache.org/docs/latest/sql-programming-guide.html

trafodion.apache.org/documentation.html

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

Developer

Hyprcubd, Inc.

Machbase

Apache Software Foundation

Apache Software Foundation, originally developed by HP

Paradigma Software

Initial release

2013

2014

1999

Current release

V8.0, August 2023

3.5.0 ( 2.13), September 2023

2.3.0, February 2019

5.7.5

License

Commercial or Open Source

commercial

free test version available

Open Source

Apache 2.0

Open Source

Apache 2.0

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

Scala

C++, Java

Server operating systems

hosted

Linux
macOS
Windows

Linux
OS X
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

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

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

APIs and other access methods

gRPC (https)

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC

ODBC

Supported programming languages

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

Java
Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

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

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

volatile and lookup table

yes

User concepts

Access control

token access

simple password-based access control

fine grained access rights according to SQL-standard

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