DBMS > Amazon Redshift vs. FoundationDB vs. IBM Cloudant vs. ObjectBox vs. Spark SQL

System Properties Comparison Amazon Redshift vs. FoundationDB vs. IBM Cloudant vs. ObjectBox vs. Spark SQL

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

Large scale data warehouse service for use with business intelligence tools

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

Database as a Service offering based on Apache CouchDB

Extremely fast embedded database for small devices, IoT and Mobile

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Document store

Object oriented DBMS

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

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

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	1.20
Rank	#170	Overall
	#5	Object oriented DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

github.com/apple/foundationdb

www.ibm.com/products/cloudant

objectbox.io

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

apple.github.io/foundationdb

cloud.ibm.com/docs/Cloudant

docs.objectbox.io

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon (based on PostgreSQL)

FoundationDB

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

ObjectBox Limited

Apache Software Foundation

Initial release

2012

2013

2010

2017

2014

Current release

6.2.28, November 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Apache License 2.0

Open Source

Apache 2.0

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++

Erlang

C and C++

Scala

Server operating systems

hosted

Linux
OS X
Windows

hosted

Android
iOS
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

some layers support schemas

schema-free

yes

Typing

predefined data types such as float or date

yes

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

supported in specific SQL layer only

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

Proprietary native API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

C#
Java
JavaScript
Objective-C
PHP
Ruby

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

in SQL-layer only

View functions (Map-Reduce) in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

online/offline synchronization between client and server

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Linearizable consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

in SQL-layer only

yes

Transaction concepts

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

ACID

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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 rights for users can be defined per database

yes

More information provided by the system vendor

News

The on-device Vector Database for Android and Java
29 May 2024

Vector search: making sense of search queries
29 May 2024

Python on-device Vector and Object Database for Local AI
28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.