DBMS > Amazon Redshift vs. Apache Phoenix vs. Dragonfly vs. Sphinx vs. STSdb

System Properties Comparison Amazon Redshift vs. Apache Phoenix vs. Dragonfly vs. Sphinx vs. STSdb

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

A scale-out RDBMS with evolutionary schema built on Apache HBase

A drop-in Redis replacement that scales vertically to support millions of operations per second and terabyte sized workloads, all on a single instance

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	0.41
Rank	#266	Overall
	#38	Key-value stores

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

aws.amazon.com/redshift

phoenix.apache.org

github.com/dragonflydb/dragonfly
www.dragonflydb.io

sphinxsearch.com

github.com/STSSoft/STSdb4

Technical documentation

docs.aws.amazon.com/redshift

phoenix.apache.org

www.dragonflydb.io/docs

sphinxsearch.com/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

DragonflyDB team and community contributors

Sphinx Technologies Inc.

STS Soft SC

Initial release

2012

2014

2023

2001

2011

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

1.0, March 2023

3.5.1, February 2023

4.0.8, September 2015

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSL 1.1

Open Source

GPL version 2, commercial licence available

Open Source

GPLv2, commercial license available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

Linux

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Windows

Data scheme

yes

late-bound, schema-on-read capabilities

scheme-free

yes

Typing

predefined data types such as float or date

yes

strings, hashes, lists, sets, sorted sets, bit arrays

yes

primitive types and user defined types (classes)

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

JDBC

Proprietary protocol

RESP - REdis Serialization Protocol

Proprietary protocol

.NET Client API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

C
C#
C++
Clojure
D
Dart
Elixir
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
Objective-C
Perl
PHP
Python
R
Ruby
Rust
Scala
Swift
Tcl

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C#
Java

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Lua

Triggers

publish/subscribe channels provide some trigger functionality

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

Multi-source replication
Source-replica replication

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Eventual 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

Atomic execution of command blocks and scripts

Concurrency

Support for concurrent manipulation of data

yes

yes, strict serializability by the server

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

Access Control Lists (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

Password-based authentication

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