DBMS > Amazon Redshift vs. Apache Phoenix vs. Blueflood vs. Sphinx vs. YDB

System Properties Comparison Amazon Redshift vs. Apache Phoenix vs. Blueflood vs. Sphinx vs. YDB

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

Scalable TimeSeries DBMS based on Cassandra

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

A distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API

Primary database model

Relational DBMS

Time Series DBMS

Search engine

Document store
Relational DBMS

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	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	5.98
Rank	#56	Overall
	#5	Search engines

Score	0.35
Rank	#278	Overall
	#43	Document stores
	#127	Relational DBMS

Website

aws.amazon.com/redshift

phoenix.apache.org

blueflood.io

sphinxsearch.com

github.com/ydb-platform/ydb
ydb.tech

Technical documentation

docs.aws.amazon.com/redshift

phoenix.apache.org

github.com/rax-maas/blueflood/wiki

sphinxsearch.com/docs

ydb.tech/en/docs

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Rackspace

Sphinx Technologies Inc.

Yandex

Initial release

2012

2014

2013

2001

2019

Current release

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

3.5.1, February 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

GPL version 2, commercial licence available

Open Source

Apache 2.0; commercial license 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

Java

C++

Server operating systems

hosted

Linux
Unix
Windows

Linux
OS X

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

yes

late-bound, schema-on-read capabilities

predefined scheme

yes

Flexible Schema (defined schema, partial schema, 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

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language (SphinxQL)

SQL-like query language (YQL)

APIs and other access methods

JDBC
ODBC

JDBC

HTTP REST

Proprietary protocol

RESTful HTTP API (DynamoDB compatible)

Supported programming languages

All languages supporting JDBC/ODBC

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

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Go
Java
JavaScript (Node.js)
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

none

Active-passive shard replication

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

based on Cassandra
Immediate Consistency

based on Cassandra

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.

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

Access rights defined for Yandex Cloud users

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