DBMS > Amazon Redshift vs. Citus vs. Dgraph vs. FoundationDB vs. Spark SQL

System Properties Comparison Amazon Redshift vs. Citus vs. Dgraph vs. FoundationDB 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Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market.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

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Distributed and scalable native Graph DBMS

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

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

Primary database model

Relational DBMS

Graph DBMS

Document store

supported via specific layer
Key-value store
Relational DBMS

supported via specific SQL-layer

Relational DBMS

Secondary database models

Document store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	1.45
Rank	#156	Overall
	#15	Graph 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

Website

aws.amazon.com/redshift

www.citusdata.com

dgraph.io

github.com/apple/foundationdb

spark.apache.org/sql

Technical documentation

docs.aws.amazon.com/redshift

docs.citusdata.com

dgraph.io/docs

apple.github.io/foundationdb

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

Developer

Amazon (based on PostgreSQL)

Dgraph Labs, Inc.

FoundationDB

Apache Software Foundation

Initial release

2012

2010

2016

2013

2014

Current release

8.1, December 2018

6.2.28, November 2020

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

AGPL, commercial license also available

Open Source

Apache 2.0

Open Source

Apache 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++

Scala

Server operating systems

hosted

Linux

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

standard, with numerous extensions

supported in specific SQL layer only

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

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

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C++
Go
Java
JavaScript

Node.js
PHP
Python
Ruby
Swift

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

in SQL-layer only

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

other methods possible by using 3rd party extensions

Synchronous replication via Raft

yes

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

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

in SQL-layer only

Transaction concepts

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

ACID

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

User concepts

Access control

fine grained access rights according to SQL-standard

Planned for future releases

More information provided by the system vendor

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.