DBMS > HEAVY.AI vs. Infobright vs. RavenDB

System Properties Comparison HEAVY.AI vs. Infobright vs. RavenDB

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Editorial information provided by DB-Engines

Name

Description

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

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Relational DBMS

Document store

Secondary database models

Spatial DBMS

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	2.10
Rank	#126	Overall
	#61	Relational DBMS

Score	1.00
Rank	#193	Overall
	#90	Relational DBMS

Score	3.01
Rank	#101	Overall
	#17	Document stores

Website

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

ignitetech.com/softwarelibrary/infobrightdb

ravendb.net

Technical documentation

docs.heavy.ai

ravendb.net/docs

Developer

HEAVY.AI, Inc.

Ignite Technologies Inc.; formerly InfoBright Inc.

Hibernating Rhinos

Initial release

2016

2005

2010

Current release

5.10, January 2022

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

Open Source

AGPL version 3, 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

C++ and CUDA

Server operating systems

Linux

Linux
Windows

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

schema-free

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

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

SQL-like query language (RQL)

APIs and other access methods

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
ODBC

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

ACID, Cluster-wide transaction available

Concurrency

Support for concurrent manipulation of data

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

exploiting MySQL or PostgreSQL frontend capabilities

Authorization levels configured per client per database

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More resources

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