DBMS > HEAVY.AI vs. InfinityDB vs. RavenDB

System Properties Comparison HEAVY.AI vs. InfinityDB 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

A Java embedded Key-Value Store which extends the Java Map interface

Open Source Operational and Transactional Enterprise NoSQL Document Database

Primary database model

Relational DBMS

Key-value store

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	0.07
Rank	#359	Overall
	#54	Key-value stores

Score	3.01
Rank	#101	Overall
	#17	Document stores

Website

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

boilerbay.com

ravendb.net

Technical documentation

docs.heavy.ai

boilerbay.com/infinitydb/manual

ravendb.net/docs

Developer

HEAVY.AI, Inc.

Boiler Bay Inc.

Hibernating Rhinos

Initial release

2016

2002

2010

Current release

5.10, January 2022

4.0

5.4, July 2022

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

commercial

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

Java

Server operating systems

Linux

All OS with a Java VM

Linux
macOS
Raspberry Pi
Windows

Data scheme

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

manual creation possible, using inversions based on multi-value capability

yes

SQL

Support of SQL

yes

SQL-like query language (RQL)

APIs and other access methods

JDBC
ODBC
Thrift
Vega

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

.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

Java

.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

none

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

READ-COMMITTED or SERIALIZED

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

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

Optimistic locking for transactions; no isolation for bulk loads

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

Authorization levels configured per client per database

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