DBMS > Apache Phoenix vs. IRONdb vs. MySQL vs. SAP Advantage Database Server vs. Yanza

System Properties Comparison Apache Phoenix vs. IRONdb vs. MySQL vs. SAP Advantage Database Server vs. Yanza

Editorial information provided by DB-Engines

Name

IRONdb seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

SAP Advantage Database Server seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Widely used open source RDBMS

Low-cost RDBMS with access to ISAM and FoxPro data structures

Time Series DBMS for IoT Applications

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Time Series DBMS

Secondary database models

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	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

phoenix.apache.org

www.circonus.com/solutions/time-series-database/

www.mysql.com

www.sap.com/products/advantage-database-server.html

yanza.com

Technical documentation

phoenix.apache.org

docs.circonus.com/irondb/category/getting-started

dev.mysql.com/doc

Developer

Apache Software Foundation

Circonus LLC.

Oracle

since 2010, originally MySQL AB, then Sun

Sybase, SAP

Yanza

Initial release

2014

2017

1995

1993

2015

Current release

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

V0.10.20, January 2018

8.3.0, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

free version available

Cloud-based only

Only available as a cloud service

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

Java

C and C++

Server operating systems

Linux
Unix
Windows

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Linux
Windows

Windows

Data scheme

yes

late-bound, schema-on-read capabilities

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

text, numeric, histograms

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (Circonus Analytics Query Language: CAQL)

yes

with proprietary extensions

yes

APIs and other access methods

JDBC

HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

.NET Client API
ADO.NET
JDBC
ODBC
OLE DB

HTTP API

Supported programming languages

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala

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

Delphi
Perl
PHP

any language that supports HTTP calls

Server-side scripts

Stored procedures

user defined functions

yes, in Lua

yes

proprietary syntax

yes

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic, metric affinity per node

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

configurable replication factor, datacenter aware

Multi-source replication
Source-replica replication

Multi-source replication

CONFLICT Trigger to resolve replication conflicts
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 or Eventual Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

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

caching of (temporary) tables can be configured

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

fine grained access rights according to SQL-standard

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