DBMS > Hyprcubd vs. Quasardb vs. Splice Machine vs. Tkrzw

System Properties Comparison Hyprcubd vs. Quasardb vs. Splice Machine vs. Tkrzw

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Editorial information provided by DB-Engines

Name

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

Description

Distributed, high-performance timeseries database

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

hyprcubd.com (offline)

quasar.ai

splicemachine.com

dbmx.net/tkrzw

Technical documentation

doc.quasar.ai/master

splicemachine.com/how-it-works

Developer

Hyprcubd, Inc.

quasardb

Splice Machine

Mikio Hirabayashi

Initial release

2009

2014

2020

Current release

3.14.1, January 2024

3.1, March 2021

0.9.3, August 2020

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

AGPL 3.0, commercial license available

Open Source

Apache Version 2.0

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

C++

Java

C++

Server operating systems

hosted

BSD
Linux
OS X
Windows

Linux
OS X
Solaris
Windows

Linux
macOS

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

yes

integer and binary

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

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

gRPC (https)

HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Yes, via Full Spark Integration

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

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

using specific database classes

User concepts

Access control

token access

Cryptographically strong user authentication and audit trail

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

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