DBMS > Atos Standard Common Repository vs. HyperSQL vs. Postgres-XL vs. Solr vs. Tkrzw

System Properties Comparison Atos Standard Common Repository vs. HyperSQL vs. Postgres-XL vs. Solr vs. Tkrzw

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed 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

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Document store
Key-value store

Relational DBMS

Search engine

Key-value store

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

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

Score	3.49
Rank	#87	Overall
	#47	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

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

Technical documentation

hsqldb.org/web/hsqlDocsFrame.html

www.postgres-xl.org/documentation

solr.apache.org/resources.html

Developer

Atos Convergence Creators

Apache Software Foundation

Mikio Hirabayashi

Initial release

2016

2001

2014

since 2012, originally named StormDB

2006

2020

Current release

1703

2.7.2, June 2023

10 R1, October 2018

9.6.0, April 2024

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

based on BSD license

Open Source

Mozilla public license

Open Source

Apache Version 2

Open Source

Apache Version 2.0

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++

Server operating systems

Linux

All OS with a Java VM

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

Linux
macOS

All OS with a Java VM

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

Linux
macOS

Data scheme

Schema and schema-less with LDAP views

yes

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

schema-free

Typing

predefined data types such as float or date

optional

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

XML type, but no XML query functionality

yes

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

distributed, parallel query execution

Solr Parallel SQL Interface

APIs and other access methods

LDAP

HTTP API

JDBC via HTTP
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

All languages supporting JDBC/ODBC
Java

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

Java, SQL

user defined functions

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

horizontal partitioning

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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 depending on configuration

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

MVCC

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

using specific database classes

User concepts

Access control

LDAP bind authentication

fine grained access rights according to SQL-standard

yes

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More resources

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Recent citations in the news

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