DBMS > FoundationDB vs. H2GIS vs. MySQL vs. PostGIS vs. TimescaleDB

System Properties Comparison FoundationDB vs. H2GIS vs. MySQL vs. PostGIS vs. TimescaleDB

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

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

Spatial extension of H2

Widely used open source RDBMS

Spatial extension of PostgreSQL

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Spatial DBMS

Relational DBMS

Key/Value like access via memcached API

Spatial DBMS

Time Series DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS

Relational DBMS

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

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

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

github.com/apple/foundationdb

Technical documentation

apple.github.io/foundationdb

www.h2gis.org/docs/home

dev.mysql.com/doc

postgis.net/documentation

docs.timescale.com

Developer

FoundationDB

CNRS

Oracle

since 2010, originally MySQL AB, then Sun

Timescale

Initial release

2013

1995

2005

2017

Current release

6.2.28, November 2020

8.4.0, April 2024

3.4.2, February 2024

2.15.0, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

LGPL 3.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL v2.0

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

C++

Java

C and C++

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

schema-free

some layers support schemas

yes

Typing

predefined data types such as float or date

some layers support typing

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

Secondary indexes

yes

SQL

Support of SQL

supported in specific SQL layer only

yes

with proprietary extensions

yes

full PostgreSQL SQL syntax

APIs and other access methods

ADO.NET
JDBC
ODBC
Proprietary native API

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

Supported programming languages

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

in SQL-layer only

yes

based on H2

yes

proprietary syntax

user defined functions

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

based on PostgreSQL

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

Multi-source replication
Source-replica replication

yes

based on PostgreSQL

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Linearizable consistency

Immediate Consistency

Foreign keys

Referential integrity

in SQL-layer only

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

table locks or row locks depending on storage engine

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

yes

based on H2

Users with fine-grained authorization concept

no user groups or roles

yes

based on PostgreSQL

fine grained access rights according to SQL-standard

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