DBMS > FoundationDB vs. Google BigQuery vs. H2 vs. Hazelcast vs. NCache

System Properties Comparison FoundationDB vs. Google BigQuery vs. H2 vs. Hazelcast vs. NCache

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

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

Open-Source and Enterprise in-memory Key-Value Store

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

Spatial DBMS

Document store

JSON support with IMDG 3.12

Document store
Search engine

Using distributed Lucene

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	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

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Website

github.com/apple/foundationdb

cloud.google.com/bigquery

www.h2database.com

hazelcast.com

www.alachisoft.com/ncache

Technical documentation

apple.github.io/foundationdb

cloud.google.com/bigquery/docs

www.h2database.com/html/main.html

hazelcast.org/imdg/docs

www.alachisoft.com/resources/docs

Developer

FoundationDB

Google

Thomas Mueller

Hazelcast

Alachisoft

Initial release

2013

2010

2005

2008

2005

Current release

6.2.28, November 2020

2.2.220, July 2023

5.3.6, November 2023

5.3.3, April 2024

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

Open Source

Enterprise Edition 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

C#, .NET, .NET Core, Java

Server operating systems

Linux
OS X
Windows

hosted

All OS with a Java VM

Linux
Windows

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

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC

JCache
JPA
Memcached protocol
RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java

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

.Net
.Net Core
C#
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

support for stored procedures with SQL-Server CLR

Triggers

yes

Events

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

Replicated Map

yes, with selectable consistency level

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

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

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

optimistic locking and pessimistic locking

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 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

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

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Typical application scenarios

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Market metrics

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