DBMS > Hyprcubd vs. Kyligence Enterprise vs. Lovefield vs. Splice Machine

System Properties Comparison Hyprcubd vs. Kyligence Enterprise vs. Lovefield vs. Splice Machine

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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

Serverless Time Series DBMS

A distributed analytics engine for big data, built on top of Apache Kylin

Embeddable relational database for web apps written in pure JavaScript

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

Primary database model

Time Series DBMS

Relational DBMS

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

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Website

hyprcubd.com (offline)

kyligence.io/kyligence-enterprise

google.github.io/lovefield

splicemachine.com

Technical documentation

github.com/google/lovefield/blob/master/docs/spec_index.md

splicemachine.com/how-it-works

Developer

Hyprcubd, Inc.

Kyligence, Inc.

Google

Splice Machine

Initial release

2016

2014

Current release

2.1.12, February 2017

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

JavaScript

Java

Server operating systems

hosted

Linux

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux
OS X
Solaris
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

ANSI SQL for queries (using Apache Calcite)

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

gRPC (https)

JDBC
ODBC
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

Supported programming languages

JavaScript

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

Server-side scripts

Stored procedures

yes

Java

Triggers

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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)

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

using MemoryDB

yes

User concepts

Access control

token access

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

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