DBMS > Atos Standard Common Repository vs. Datomic vs. Solr

System Properties Comparison Atos Standard Common Repository vs. Datomic vs. Solr

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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.

Description

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

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Primary database model

Document store
Key-value store

Relational DBMS

Search engine

Secondary database models

Spatial DBMS

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

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	44.28
Rank	#24	Overall
	#3	Search engines

Website

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

www.datomic.com

solr.apache.org

Technical documentation

docs.datomic.com

solr.apache.org/resources.html

Developer

Atos Convergence Creators

Cognitect

Apache Software Foundation

Initial release

2016

2012

2006

Current release

1703

1.0.6735, June 2023

9.5.0, February 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2

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

Java, Clojure

Java

Server operating systems

Linux

All OS with a Java VM

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

Data scheme

Schema and schema-less with LDAP views

yes

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

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

Secondary indexes

yes

All search fields are automatically indexed

SQL

Support of SQL

Solr Parallel SQL Interface

APIs and other access methods

LDAP

RESTful HTTP API

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages with LDAP bindings

Clojure
Java

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

Server-side scripts

Stored procedures

yes

Transaction Functions

Java plugins

Triggers

yes

By using transaction functions

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

none

But extensive use of caching in the application peers

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes

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

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

LDAP bind authentication

yes

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