DBMS > Apache Phoenix vs. MySQL vs. Percona Server for MongoDB vs. Solr

System Properties Comparison Apache Phoenix vs. MySQL vs. Percona Server for MongoDB vs. Solr

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Editorial information provided by DB-Engines

Name

Description

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

Widely used open source RDBMS

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Relational DBMS

Key/Value like access via memcached API

Document store

Search engine

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	2.02
Rank	#130	Overall
	#63	Relational DBMS

Score	1087.72
Rank	#2	Overall
	#2	Relational DBMS

Score	0.55
Rank	#253	Overall
	#39	Document stores

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

phoenix.apache.org

www.mysql.com

www.percona.com/mongodb/software/percona-server-for-mongodb

solr.apache.org

Technical documentation

phoenix.apache.org

dev.mysql.com/doc

docs.percona.com/percona-distribution-for-mongodb

solr.apache.org/resources.html

Developer

Apache Software Foundation

Oracle

since 2010, originally MySQL AB, then Sun

Percona

Apache Software Foundation

Initial release

2014

1995

2015

2006

Current release

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

8.3.0, January 2024

3.4.10-2.10, November 2017

9.5.0, February 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

GPL Version 2

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C and C++

C++

Java

Server operating systems

Linux
Unix
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

Solr Parallel SQL Interface

APIs and other access methods

JDBC

ADO.NET
JDBC
ODBC
Proprietary native API

proprietary protocol using JSON

Java API
RESTful HTTP/JSON API

Supported programming languages

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

JavaScript

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

yes

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

not for MyISAM storage engine

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

via In-Memory Engine

yes

User concepts

Access control

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

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and roles

yes

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