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

System Properties Comparison Amazon Redshift vs. Drizzle vs. Spark SQL vs. Sphinx 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

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

Open source search engine for searching in data from different sources, e.g. relational databases

Object-relational database and reports server

Primary database model

Relational DBMS

Search engine

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

aws.amazon.com/redshift

spark.apache.org/sql

sphinxsearch.com

www.valentina-db.net

Technical documentation

docs.aws.amazon.com/redshift

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

sphinxsearch.com/docs

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

Developer

Amazon (based on PostgreSQL)

Drizzle project, originally started by Brian Aker

Apache Software Foundation

Sphinx Technologies Inc.

Paradigma Software

Initial release

2012

2008

2014

2001

1999

Current release

7.2.4, September 2012

3.5.0 ( 2.13), September 2023

3.5.1, February 2023

5.7.5

License

Commercial or Open Source

commercial

Open Source

GNU GPL

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

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

C++

Server operating systems

hosted

FreeBSD
Linux
OS X

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

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.

Secondary indexes

restricted

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

with proprietary extensions

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

yes

APIs and other access methods

JDBC
ODBC

JDBC

JDBC
ODBC

Proprietary protocol

ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

Java
Python
R
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

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

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

The original contents of fields are not stored in the Sphinx index.

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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