DBMS > FoundationDB vs. Google Cloud Datastore vs. H2 vs. Postgres-XL vs. SWC-DB

System Properties Comparison FoundationDB vs. Google Cloud Datastore vs. H2 vs. Postgres-XL vs. SWC-DB

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

A high performance, scalable Wide Column DBMS

Primary database model

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Document store

Relational DBMS

Wide column store

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

Time Series 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	4.47
Rank	#76	Overall
	#12	Document stores

Score	8.13
Rank	#49	Overall
	#31	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

github.com/apple/foundationdb

cloud.google.com/datastore

www.h2database.com

www.postgres-xl.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

apple.github.io/foundationdb

cloud.google.com/datastore/docs

www.h2database.com/html/main.html

www.postgres-xl.org/documentation

Developer

FoundationDB

Google

Thomas Mueller

Alex Kashirin

Initial release

2013

2008

2005

2014

since 2012, originally named StormDB

2020

Current release

6.2.28, November 2020

2.2.220, July 2023

10 R1, October 2018

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

Mozilla public license

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Java

C++

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

Linux
macOS

Linux

Data scheme

schema-free

some layers support schemas

schema-free

yes

schema-free

Typing

predefined data types such as float or date

some layers support typing

yes, details here

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

supported in specific SQL layer only

SQL-like query language (GQL)

yes

distributed, parallel query execution

SQL-like query language

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

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

Proprietary protocol
Thrift

Supported programming languages

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

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

Java

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

C++

Server-side scripts

Stored procedures

in SQL-layer only

using Google App Engine

Java Stored Procedures and User-Defined Functions

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

With clustering: 2 database servers on different computers operate on identical copies of a database

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Linearizable consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

in SQL-layer only

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

MVCC

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (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

User concepts

Access control

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

fine grained access rights according to SQL-standard

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