DBMS > FoundationDB vs. Google Cloud Datastore vs. Machbase Neo vs. Postgres-XL

System Properties Comparison FoundationDB vs. Google Cloud Datastore vs. Machbase Neo vs. Postgres-XL

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

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

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

TimeSeries DBMS for AIoT and BigData

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

Primary database model

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Document store

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.07
Rank	#188	Overall
	#31	Document stores
	#28	Key-value stores
	#86	Relational DBMS

Score	4.49
Rank	#79	Overall
	#12	Document stores

Score	0.22
Rank	#324	Overall
	#29	Time Series DBMS

Score	0.52
Rank	#256	Overall
	#117	Relational DBMS

Website

github.com/apple/foundationdb

cloud.google.com/datastore

machbase.com

www.postgres-xl.org

Technical documentation

apple.github.io/foundationdb

cloud.google.com/datastore/docs

machbase.com/dbms

www.postgres-xl.org/documentation

Developer

FoundationDB

Google

Machbase

Initial release

2013

2008

2013

2014

since 2012, originally named StormDB

Current release

6.2.28, November 2020

V8.0, August 2023

10 R1, October 2018

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

free test version available

Open Source

Mozilla public license

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++

Server operating systems

Linux
OS X
Windows

hosted

Linux
macOS
Windows

Linux
macOS

Data scheme

schema-free

some layers support schemas

schema-free

yes

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)

SQL-like query language

yes

distributed, parallel query execution

APIs and other access methods

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

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

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

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Server-side scripts

Stored procedures

in SQL-layer only

using Google App Engine

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

selectable replication factor

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

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

volatile and lookup table

User concepts

Access control

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

simple password-based access control

fine grained access rights according to SQL-standard

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More resources

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