DBMS > Atos Standard Common Repository vs. HyperSQL vs. KairosDB vs. Trafodion vs. Vitess

System Properties Comparison Atos Standard Common Repository vs. HyperSQL vs. KairosDB vs. Trafodion vs. Vitess

Editorial information provided by DB-Engines

Name

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

Apache Trafodion has been retired in 2021. 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

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Distributed Time Series DBMS based on Cassandra or H2

Transactional SQL-on-Hadoop DBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

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

hsqldb.org

github.com/kairosdb/kairosdb

trafodion.apache.org

vitess.io

Technical documentation

hsqldb.org/web/hsqlDocsFrame.html

kairosdb.github.io

trafodion.apache.org/documentation.html

vitess.io/docs

Developer

Atos Convergence Creators

Apache Software Foundation, originally developed by HP

The Linux Foundation, PlanetScale

Initial release

2016

2001

2013

2014

2013

Current release

1703

2.7.2, June 2023

1.2.2, November 2018

2.3.0, February 2019

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++, Java

Server operating systems

Linux

All OS with a Java VM

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

Linux
OS X
Windows

Linux

Docker
Linux
macOS

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

Typing

predefined data types such as float or date

optional

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

with proprietary extensions

APIs and other access methods

LDAP

HTTP API

JDBC via HTTP
JDBC
ODBC

Graphite protocol
HTTP REST
Telnet API

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

All languages with LDAP bindings

All languages supporting JDBC/ODBC
Java

Java
JavaScript

Node.js
PHP
Python

All languages supporting JDBC/ODBC/ADO.Net

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

Java, SQL

Java Stored Procedures

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

based on Cassandra

yes, via HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

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

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

User concepts

Access control

LDAP bind authentication

fine grained access rights according to SQL-standard

simple password-based access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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