DBMS > Amazon Redshift vs. Citus vs. Drizzle vs. Spark SQL vs. Valentina Server

System Properties Comparison Amazon Redshift vs. Citus vs. Drizzle vs. Spark SQL vs. Valentina Server

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines 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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

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

Object-relational database and reports server

Primary database model

Relational DBMS

Secondary database models

Document store

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	2.15
Rank	#117	Overall
	#56	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

aws.amazon.com/redshift

www.citusdata.com

spark.apache.org/sql

www.valentina-db.net

Technical documentation

docs.aws.amazon.com/redshift

docs.citusdata.com

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

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Amazon (based on PostgreSQL)

Drizzle project, originally started by Brian Aker

Apache Software Foundation

Paradigma Software

Initial release

2012

2010

2008

2014

1999

Current release

8.1, December 2018

7.2.4, September 2012

3.5.0 ( 2.13), September 2023

5.7.5

License

Commercial or Open Source

commercial

Open Source

AGPL, commercial license also available

Open Source

GNU GPL

Open Source

Apache 2.0

commercial

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

FreeBSD
Linux
OS X

Linux
OS X
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

yes

with proprietary extensions

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

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

JDBC

JDBC
ODBC

ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C++
Java
PHP

Java
Python
R
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

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.

yes

Triggers

yes

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

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

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

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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