DBMS > Apache Phoenix vs. HyperSQL vs. Manticore Search vs. MySQL vs. Prometheus

System Properties Comparison Apache Phoenix vs. HyperSQL vs. Manticore Search vs. MySQL vs. Prometheus

Editorial information provided by DB-Engines

Name

Description

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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Multi-storage database for search, including full-text search.

Widely used open source RDBMS

Open-source Time Series DBMS and monitoring system

Primary database model

Relational DBMS

Search engine

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Spatial DBMS

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

Score	1.97
Rank	#126	Overall
	#59	Relational DBMS

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Website

Technical documentation

phoenix.apache.org

hsqldb.org/web/hsqlDocsFrame.html

manual.manticoresearch.com

dev.mysql.com/doc

prometheus.io/docs

Developer

Apache Software Foundation

Manticore Software

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2014

2001

2017

1995

2015

Current release

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

2.7.2, June 2023

6.0, February 2023

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

based on BSD license

Open Source

GPL version 2

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

C and C++

Server operating systems

Linux
Unix
Windows

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

FreeBSD
Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

Data scheme

yes

late-bound, schema-on-read capabilities

yes

Fixed schema

yes

Typing

predefined data types such as float or date

yes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

yes

Numeric data only

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.

Can index from XML

yes

Import of XML data possible

Secondary indexes

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

JDBC

HTTP API

JDBC via HTTP
JDBC
ODBC

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP/JSON API

Supported programming languages

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

All languages supporting JDBC/ODBC
Java

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

Java, SQL

user defined functions

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

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

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

none

Synchronous replication based on Galera library

Multi-source replication
Source-replica replication

yes

by Federation

MapReduce

Offers an API for user-defined Map/Reduce methods

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

none

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

not for MyISAM storage engine

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

The original contents of fields are not stored in the Manticore 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

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

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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