DBMS > FoundationDB vs. Fujitsu Enterprise Postgres vs. Google BigQuery vs. H2 vs. Hazelcast

System Properties Comparison FoundationDB vs. Fujitsu Enterprise Postgres vs. Google BigQuery vs. H2 vs. Hazelcast

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Large scale data warehouse service with append-only tables

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

A widely adopted in-memory data grid

Primary database model

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

Document store

JSON support with IMDG 3.12

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

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

Score	0.37
Rank	#278	Overall
	#128	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	5.46
Rank	#61	Overall
	#7	Key-value stores

Website

github.com/apple/foundationdb

www.postgresql.fastware.com

cloud.google.com/bigquery

www.h2database.com

hazelcast.com

Technical documentation

apple.github.io/foundationdb

www.postgresql.fastware.com/product-manuals

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

hazelcast.org/imdg/docs

Developer

FoundationDB

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

Google

Thomas Mueller

Hazelcast

Initial release

2013

2010

2005

2008

Current release

6.2.28, November 2020

Fujitsu Enterprise Postgres 14, January 2022

2.2.220, July 2023

5.3.6, November 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

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

Open Source

Apache Version 2; commercial licenses available

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

Server operating systems

Linux
OS X
Windows

Linux
Windows

hosted

All OS with a Java VM

Data scheme

schema-free

some layers support schemas

yes

schema-free

Typing

predefined data types such as float or date

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

the object must implement a serialization strategy

Secondary indexes

yes

SQL

Support of SQL

supported in specific SQL layer only

yes

SQL-like query language

APIs and other access methods

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

RESTful HTTP/JSON API

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

Supported programming languages

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

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

Server-side scripts

Stored procedures

in SQL-layer only

user defined functions

in JavaScript

Java Stored Procedures and User-Defined Functions

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

partitioning by range, list and by hash

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

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

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Linearizable consistency

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Foreign keys

Referential integrity

in SQL-layer only

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

one or two-phase-commit; repeatable reads; read commited

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Role-based access control

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