DBMS > atoti vs. H2 vs. HEAVY.AI vs. Hive

System Properties Comparison atoti vs. H2 vs. HEAVY.AI vs. Hive

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Primary database model

Object oriented DBMS

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.61
Rank	#243	Overall
	#11	Object oriented DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Website

atoti.io

www.h2database.com

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

hive.apache.org

Technical documentation

docs.atoti.io

www.h2database.com/html/main.html

docs.heavy.ai

cwiki.apache.org/confluence/display/Hive/Home

Developer

ActiveViam

Thomas Mueller

HEAVY.AI, Inc.

Apache Software Foundation

initially developed by Facebook

Initial release

2005

2016

2012

Current release

2.2.220, July 2023

5.10, January 2022

3.1.3, April 2022

License

Commercial or Open Source

commercial

free versions available

Open Source

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

Open Source

Apache Version 2; enterprise edition available

Open Source

Apache Version 2

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

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

Multidimensional Expressions (MDX)

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
Thrift

Supported programming languages

Java

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
PHP
Python

Server-side scripts

Stored procedures

Python

Java Stored Procedures and User-Defined Functions

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

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

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

Access rights for users, groups and roles

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More resources

DB-Engines blog posts

Why is Hadoop not listed in the DB-Engines Ranking?
13 May 2013, Paul Andlinger

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