DBMS > HEAVY.AI vs. Oracle Berkeley DB vs. Riak KV vs. SpatiaLite

System Properties Comparison HEAVY.AI vs. Oracle Berkeley DB vs. Riak KV vs. SpatiaLite

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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

Widely used in-process key-value store

Distributed, fault tolerant key-value store

Spatial extension of SQLite

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Spatial DBMS

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	1.48
Rank	#146	Overall
	#70	Relational DBMS

Score	2.87
Rank	#107	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	6.29
Rank	#69	Overall
	#10	Key-value stores

Score	1.94
Rank	#127	Overall
	#3	Spatial DBMS

Website

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

www.oracle.com/technetwork/database/database-technologies/berkeleydb/overview/index.html

riak.com/products/riak-kv

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

docs.heavy.ai

docs.oracle.com/cd/E17076_05/html/index.html

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

HEAVY.AI, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Basho Technologies

Alessandro Furieri

Initial release

2016

1994

2009

2008

Current release

5.10, January 2022

18.1.40, May 2020

2.1.0, April 2015

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2; enterprise edition available

Open Source

commercial license available

Open Source

Apache version 2, commercial enterprise edition

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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

C, Java, C++ (depending on the Berkeley DB edition)

Erlang

C++

Server operating systems

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
OS X

server-less

Data scheme

yes

schema-free

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

only with the Berkeley DB XML edition

Secondary indexes

yes

restricted

yes

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

JDBC
ODBC
Thrift
Vega

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC/Thrift
Python

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

JavaScript and Erlang

Triggers

yes

only for the SQL API

yes

pre-commit hooks and post-commit hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

no "single point of failure"

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

yes

Transaction concepts

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

ACID

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

More information provided by the system vendor

Specific characteristics

Optimizes memory and compute layers to deliver unprecedented performance. Natively...
» more

Competitive advantages

Only HEAVY.AI allows users to visually and interactively discover high-value patterns,...
» more

Typical application scenarios

Advanced analytics, geospatial and time-series analytics, accelerated data science,...
» more

Licensing and pricing models

HEAVY.AI Free (Free Forever), Foundations ($2k/month), and Enterprise Licensure (Inquire...
» more

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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