DBMS > H2 vs. HEAVY.AI vs. HyperSQL vs. Splice Machine

System Properties Comparison H2 vs. HEAVY.AI vs. HyperSQL vs. Splice Machine

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Editorial information provided by DB-Engines

Name

Description

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

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

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

www.h2database.com

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

hsqldb.org

splicemachine.com

Technical documentation

www.h2database.com/html/main.html

docs.heavy.ai

hsqldb.org/web/hsqlDocsFrame.html

splicemachine.com/how-it-works

Developer

Thomas Mueller

HEAVY.AI, Inc.

Splice Machine

Initial release

2005

2016

2001

2014

Current release

2.2.220, July 2023

5.10, January 2022

2.7.2, June 2023

3.1, March 2021

License

Commercial or Open Source

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Apache Version 2; enterprise edition available

Open Source

based on BSD license

Open Source

AGPL 3.0, commercial license available

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

Java

C++ and CUDA

Java

Server operating systems

All OS with a Java VM

Linux

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Linux
OS X
Solaris
Windows

Data scheme

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

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift
Vega

HTTP API

JDBC via HTTP
JDBC
ODBC

JDBC
Native Spark Datasource
ODBC

Supported programming languages

Java

All languages supporting JDBC/ODBC/Thrift
Python

All languages supporting JDBC/ODBC
Java

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

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

Java, SQL

yes

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

none

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

With clustering: 2 database servers on different computers operate on identical copies of a database

Multi-source replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

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, multi-version concurrency control (MVCC)

yes

yes, multi-version concurrency control (MVCC)

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

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

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More resources

Recent citations in the news

Big Data Analytics: A Game Changer for Infrastructure
13 July 2023, Spiceworks News and Insights

HEAVY.AI Launches HEAVY 7.0, Introducing Real-Time Machine Learning Capabilities
19 April 2023, Business Wire

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26 August 2021, The Register

Splice Machine Launches the Splice Machine Feature Store to Simplify Feature Engineering and Democratize Machine ...
19 January 2021, PR Newswire

Splice Machine Launches Feature Store to Simplify Feature Engineering
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How Splice Machine's Data Platform for Intelligent Apps Works
29 September 2020, eWeek

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18 September 2019, Towards Data Science

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