DBMS > Amazon Redshift vs. Firebolt vs. Memcached vs. Sphinx

System Properties Comparison Amazon Redshift vs. Firebolt vs. Memcached vs. Sphinx

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

In-memory key-value store, originally intended for caching

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

Primary database model

Relational DBMS

Key-value store

Search engine

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	1.49
Rank	#148	Overall
	#68	Relational DBMS

Score	16.84
Rank	#32	Overall
	#4	Key-value stores

Score	5.97
Rank	#56	Overall
	#5	Search engines

Website

aws.amazon.com/redshift

www.firebolt.io

www.memcached.org

sphinxsearch.com

Technical documentation

docs.aws.amazon.com/redshift

docs.firebolt.io

github.com/memcached/memcached/wiki

sphinxsearch.com/docs

Developer

Amazon (based on PostgreSQL)

Firebolt Analytics Inc.

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Sphinx Technologies Inc.

Initial release

2012

2020

2003

2001

Current release

1.6.29, June 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

BSD license

Open Source

GPL version 2, commercial licence 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++

Server operating systems

hosted

FreeBSD
Linux
OS X
Unix
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

schema-free

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

.Net
ODBC
RESTful HTTP API

Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC

Go
JavaScript (Node.js)
Python

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on storage layer

none

Repcached, a Memcached patch, provides this functionallity

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

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.

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

yes

using SASL (Simple Authentication and Security Layer) protocol

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