DBMS > Amazon Redshift vs. Brytlyt vs. NCache vs. Sphinx vs. VelocityDB

System Properties Comparison Amazon Redshift vs. Brytlyt vs. NCache vs. Sphinx vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Open-Source and Enterprise in-memory Key-Value Store

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

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Key-value store

Search engine

Graph DBMS
Object oriented DBMS

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.05
Rank	#358	Overall
	#36	Graph DBMS
	#16	Object oriented DBMS

Website

aws.amazon.com/redshift

brytlyt.io

www.alachisoft.com/ncache

sphinxsearch.com

velocitydb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.brytlyt.io

www.alachisoft.com/resources/docs

sphinxsearch.com/docs

velocitydb.com/UserGuide

Developer

Amazon (based on PostgreSQL)

Brytlyt

Alachisoft

Sphinx Technologies Inc.

VelocityDB Inc

Initial release

2012

2016

2005

2001

2011

Current release

5.0, August 2023

5.3.3, April 2024

3.5.1, February 2023

7.x

License

Commercial or Open Source

commercial

Open Source

Enterprise Edition available

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, C++ and CUDA

C#, .NET, .NET Core, Java

C++

Server operating systems

hosted

Linux
OS X
Windows

Linux
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Any that supports .NET

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

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

full-text index on all search fields

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

SQL-like query language (SphinxQL)

APIs and other access methods

JDBC
ODBC

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

IDistributedCache
JCache
LINQ
Proprietary native API

Proprietary protocol

.Net

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

.Net

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

in PL/pgSQL

support for stored procedures with SQL-Server CLR

Triggers

yes

Notifications

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

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

optimistic locking and pessimistic locking

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

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

Based on Windows Authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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