DBMS > H2 vs. HEAVY.AI vs. Microsoft Azure SQL Database vs. Trafodion

System Properties Comparison H2 vs. HEAVY.AI vs. Microsoft Azure SQL Database vs. Trafodion

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Editorial information provided by DB-Engines

Name

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

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

Database as a Service offering with high compatibility to Microsoft SQL Server

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
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	77.99
Rank	#16	Overall
	#11	Relational DBMS

Website

www.h2database.com

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

azure.microsoft.com/en-us/products/azure-sql/database

trafodion.apache.org

Technical documentation

www.h2database.com/html/main.html

docs.heavy.ai

docs.microsoft.com/en-us/azure/azure-sql

trafodion.apache.org/documentation.html

Developer

Thomas Mueller

HEAVY.AI, Inc.

Microsoft

Apache Software Foundation, originally developed by HP

Initial release

2005

2016

2010

2014

Current release

2.2.220, July 2023

5.10, January 2022

V12

2.3.0, February 2019

License

Commercial or Open Source

Open Source

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

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Apache 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

Java

C++ and CUDA

C++

C++, Java

Server operating systems

All OS with a Java VM

Linux

hosted

Linux

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

Supported programming languages

Java

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Java Stored Procedures and User-Defined Functions

Transact SQL

Java Stored Procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

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

yes, with always 3 replicas available

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

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

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

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