DBMS > Amazon Redshift vs. FoundationDB vs. Spark SQL vs. Vitess vs. WakandaDB

System Properties Comparison Amazon Redshift vs. FoundationDB vs. Spark SQL vs. Vitess vs. WakandaDB

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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

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

Scalable, distributed, cloud-native DBMS, extending MySQL

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Object oriented DBMS

Secondary database models

Document store
Spatial 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	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

aws.amazon.com/redshift

github.com/apple/foundationdb

spark.apache.org/sql

vitess.io

wakanda.github.io

Technical documentation

docs.aws.amazon.com/redshift

apple.github.io/foundationdb

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

vitess.io/docs

wakanda.github.io/doc

Developer

Amazon (based on PostgreSQL)

FoundationDB

Apache Software Foundation

The Linux Foundation, PlanetScale

Wakanda SAS

Initial release

2012

2013

2014

2013

2012

Current release

6.2.28, November 2020

3.5.0 ( 2.13), September 2023

15.0.2, December 2022

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2.0, commercial licenses available

Open Source

AGPLv3, extended commercial license 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++

Scala

C++, JavaScript

Server operating systems

hosted

Linux
OS X
Windows

Docker
Linux
macOS

Linux
OS X
Windows

Data scheme

yes

schema-free

some layers support schemas

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

yes

with proprietary extensions

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

Java
Python
R
Scala

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript

Server-side scripts

Stored procedures

user defined functions

in Python

in SQL-layer only

yes

proprietary syntax

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Linearizable consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

in SQL-layer only

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Users with fine-grained authorization concept

no user groups or roles

yes

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