DBMS > Atos Standard Common Repository vs. HarperDB vs. IRONdb vs. MySQL

System Properties Comparison Atos Standard Common Repository vs. HarperDB vs. IRONdb vs. MySQL

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

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

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Ultra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.

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

Widely used open source RDBMS

Primary database model

Document store
Key-value store

Document store

Time Series DBMS

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Document store
Spatial DBMS

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

Score	0.60
Rank	#244	Overall
	#38	Document stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

www.harperdb.io

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

www.mysql.com

Technical documentation

docs.harperdb.io/docs

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

dev.mysql.com/doc

Developer

Atos Convergence Creators

HarperDB

Circonus LLC.

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2016

2017

1995

Current release

1703

3.1, August 2021

V0.10.20, January 2018

8.4.0, April 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Node.js

C and C++

Server operating systems

Linux

Linux
OS X

Linux

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

Schema and schema-less with LDAP views

dynamic schema

schema-free

yes

Typing

predefined data types such as float or date

optional

yes

JSON data types

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

SQL-like data manipulation statements

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

yes

with proprietary extensions

APIs and other access methods

LDAP

JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket

HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages with LDAP bindings

.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift

.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

Server-side scripts

Stored procedures

Custom Functions

since release 3.1

yes, in Lua

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

A table resides as a whole on one (or more) nodes in a cluster

Automatic, metric affinity per node

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

yes

the nodes on which a table resides can be defined

configurable replication factor, datacenter aware

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic execution of specific operations

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

yes, using LMDB

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

LDAP bind authentication

Access rights for users and roles

Users with fine-grained authorization concept

no user groups or roles

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