DBMS > Citus vs. FoundationDB vs. Lovefield vs. OushuDB vs. SpaceTime

System Properties Comparison Citus vs. FoundationDB vs. Lovefield vs. OushuDB vs. SpaceTime

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

Embeddable relational database for web apps written in pure JavaScript

A data warehouse powered by Apache HAWQ supporting descriptive analysis and advanced machine learning

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Relational DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Spatial DBMS

Secondary database models

Document store

Relational DBMS

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

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

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	0.04
Rank	#363	Overall
	#154	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Website

www.citusdata.com

github.com/apple/foundationdb

google.github.io/lovefield

www.oushu.com/product/oushuDB

www.mireo.com/spacetime

Technical documentation

docs.citusdata.com

apple.github.io/foundationdb

github.com/google/lovefield/blob/master/docs/spec_index.md

www.oushu.com/documentation

Developer

FoundationDB

Google

Oushu

Mireo

Initial release

2010

2013

2014

2020

Current release

8.1, December 2018

6.2.28, November 2020

2.1.12, February 2017

4.0.1, August 2020

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

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++

JavaScript

C++

Server operating systems

Linux

Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

Data scheme

yes

schema-free

some layers support schemas

yes

Typing

predefined data types such as float or date

yes

some layers support typing

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

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

supported in specific SQL layer only

SQL-like query language

via JavaScript builder pattern

Full-featured ANSI SQL support

A subset of ANSI SQL is implemented

APIs and other access methods

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

JDBC
ODBC

RESTful HTTP API

Supported programming languages

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

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

JavaScript

C
C++

C#
C++
Python

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

in SQL-layer only

yes

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

yes

none

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Linearizable consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

in SQL-layer only

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

fine grained access rights according to SQL-standard

Kerberos, SSL and role based access

yes

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