DBMS > Amazon Redshift vs. Kinetica vs. Sphinx vs. SwayDB

System Properties Comparison Amazon Redshift vs. Kinetica vs. Sphinx vs. SwayDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Fully vectorized database across both GPUs and CPUs

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

Search engine

Key-value store

Secondary database models

Spatial DBMS
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	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

aws.amazon.com/redshift

www.kinetica.com

sphinxsearch.com

swaydb.simer.au

Technical documentation

docs.aws.amazon.com/redshift

docs.kinetica.com

sphinxsearch.com/docs

Developer

Amazon (based on PostgreSQL)

Kinetica

Sphinx Technologies Inc.

Simer Plaha

Initial release

2012

2001

2018

Current release

7.1, August 2021

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

GPL version 2, commercial licence available

Open Source

GNU Affero GPL V3.0

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, C++

C++

Scala

Server operating systems

hosted

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
JavaScript (Node.js)
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

Sharding

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Immediate Consistency or Eventual Consistency depending on configuration

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

Atomic execution of operations

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

GPU vRAM or System RAM

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

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