DBMS > EsgynDB vs. FoundationDB vs. NuoDB vs. Oracle Berkeley DB vs. Prometheus

System Properties Comparison EsgynDB vs. FoundationDB vs. NuoDB vs. Oracle Berkeley DB vs. Prometheus

Editorial information provided by DB-Engines

Name

Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Ordered key-value store. Core features are complimented by layers.

NuoDB is a webscale distributed database that supports SQL and ACID transactions

Widely used in-process key-value store

Open-source Time Series DBMS and monitoring system

Primary database model

Relational DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Time Series DBMS

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

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.03
Rank	#190	Overall
	#31	Document stores
	#28	Key-value stores
	#89	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

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

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

www.esgyn.cn

github.com/apple/foundationdb

www.3ds.com/nuodb-distributed-sql-database

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

prometheus.io

Technical documentation

apple.github.io/foundationdb

doc.nuodb.com

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

prometheus.io/docs

Developer

Esgyn

FoundationDB

Dassault Systèmes

originally NuoDB, Inc.

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Initial release

2015

2013

1994

2015

Current release

6.2.28, November 2020

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

limited edition free

Open Source

commercial license available

Open Source

Apache 2.0

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++, Java

C++

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

Server operating systems

Linux

Linux
OS X
Windows

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux
Windows

Data scheme

yes

schema-free

some layers support schemas

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

some layers support typing

yes

Numeric data only

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

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes

supported in specific SQL layer only

yes

SQL interfaced based on SQLite is available

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

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

.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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

Java Stored Procedures

in SQL-layer only

Java, SQL

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

data is dynamically stored/cached on the nodes where it is read/written

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

Managed transparently by NuoDB

Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Linearizable consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

in SQL-layer only

yes

Transaction concepts

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

ACID

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

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

Temporary table

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Standard SQL roles/ privileges, Administrative Users

More information provided by the system vendor

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