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DBMS > Faircom DB vs. FoundationDB vs. Google Cloud Bigtable vs. SQL.JS

System Properties Comparison Faircom DB vs. FoundationDB vs. Google Cloud Bigtable vs. SQL.JS

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

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

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Port of SQLite to JavaScript

Primary database model

Key-value store
Relational DBMS

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Key-value store
Wide column store

Relational DBMS

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

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	1.06
Rank	#185	Overall
	#31	Document stores
	#28	Key-value stores
	#85	Relational DBMS

Score	3.15
Rank	#95	Overall
	#14	Key-value stores
	#8	Wide column stores

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Website

www.faircom.com/products/faircom-db

github.com/apple/foundationdb

cloud.google.com/bigtable

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

apple.github.io/foundationdb

cloud.google.com/bigtable/docs

sql.js.org/documentation/index.html

Developer

FairCom Corporation

FoundationDB

Google

Alon Zakai

enhancements implemented by others

Initial release

1979

2013

2015

2012

Current release

V12, November 2020

6.2.28, November 2020

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

Apache 2.0

commercial

Open Source

Cloud-based only

Only available as a cloud service

no

no

yes

no

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

C++

JavaScript

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
OS X
Windows

hosted

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

Data scheme

schema free, schema optional, schema required, partial schema,

schema-free

some layers support schemas

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

no

some layers support typing

no

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.

no

no

no

Secondary indexes

yes

no

no

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

supported in specific SQL layer only

no

yes

SQL-92 is not fully supported

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

JavaScript API

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

C#
C++
Go
Java
JavaScript (Node.js)
Python

JavaScript

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

in SQL-layer only

no

no

Triggers

yes

no

no

no

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

Sharding

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

none

MapReduce

Offers an API for user-defined Map/Reduce methods

no

no

yes

no

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Linearizable consistency

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

none

Foreign keys

Referential integrity

yes

in SQL-layer only

no

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

ACID

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

yes

yes

no

except by serializing a db to a file

In-memory capabilities

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

yes

no

yes

User concepts

Access control

Fine grained access rights according to SQL-standard with additional protections for files

no

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

no

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More resources

DB-Engines blog posts

New kids on the block: database management systems implemented in JavaScript
1 December 2014, Matthias Gelbmann

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