DBMS > Fujitsu Enterprise Postgres vs. HEAVY.AI vs. Oracle Berkeley DB vs. SpatiaLite vs. SQLite

System Properties Comparison Fujitsu Enterprise Postgres vs. HEAVY.AI vs. Oracle Berkeley DB vs. SpatiaLite vs. SQLite

Editorial information provided by DB-Engines

Name

Description

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

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

Spatial extension of SQLite

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Spatial DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

Relational DBMS

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

Score	0.31
Rank	#285	Overall
	#129	Relational DBMS

Score	1.77
Rank	#141	Overall
	#65	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.postgresql.fastware.com

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

www.oracle.com/database/technologies/related/berkeleydb.html

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

www.sqlite.org

Technical documentation

www.postgresql.fastware.com/product-manuals

docs.heavy.ai

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

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

www.sqlite.org/docs.html

Developer

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

HEAVY.AI, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Alessandro Furieri

Dwayne Richard Hipp

Initial release

2016

1994

2008

2000

Current release

Fujitsu Enterprise Postgres 14, January 2022

5.10, January 2022

18.1.40, May 2020

5.0.0, August 2020

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; enterprise edition available

Open Source

commercial license available

Open Source

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

Open Source

Public Domain

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)

C++

Server operating systems

Linux
Windows

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

server-less

Data scheme

yes

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

SQL

Support of SQL

yes

SQL interfaced based on SQLite is available

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC
Thrift
Vega

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

user defined functions

Triggers

yes

only for the SQL API

yes

Partitioning methods

Methods for storing different data on different nodes

partitioning by range, list and by hash

Sharding

Round robin

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

via file-system locks

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

100% compatible with community PostgreSQL
» more

Competitive advantages

Built-in TDE and Data Masking security. In-Memory Columnar Index, and a high speed...
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Typical application scenarios

Transactional payments applications, reporting and mixed workloads.
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Market metrics

Over 30 years experience in database technology. Over 20 years in Postgres development...
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Licensing and pricing models

Core based licensing
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