DBMS > Atos Standard Common Repository vs. ClickHouse vs. Datomic vs. InterSystems IRIS vs. openGauss

System Properties Comparison Atos Standard Common Repository vs. ClickHouse vs. Datomic vs. InterSystems IRIS vs. openGauss

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

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

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

A containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalability

An enterprise-class RDBMS compatible with high-performance, high-availability and high-performance originally developed by Huawei

Primary database model

Document store
Key-value store

Relational DBMS

Document store
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	3.54
Rank	#84	Overall
	#14	Document stores
	#10	Key-value stores
	#1	Object oriented DBMS
	#45	Relational DBMS

Score	1.06
Rank	#184	Overall
	#84	Relational DBMS

Website

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

clickhouse.com

www.datomic.com

www.intersystems.com/products/intersystems-iris

gitee.com/opengauss
opengauss.org

Technical documentation

clickhouse.com/docs

docs.datomic.com

docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls

docs.opengauss.org/en
gitee.com/opengauss/docs

Developer

Atos Convergence Creators

Clickhouse Inc.

Cognitect

InterSystems

Huawei and openGauss community

Initial release

2016

2012

2018

2019

Current release

1703

v24.4.1.2088-stable, May 2024

1.0.6735, June 2023

2023.3, June 2023

3.0, March 2022

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

limited edition free

commercial

Open Source

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.

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Implementation language

Java

C++

Java, Clojure

C, C++, Java

Server operating systems

Linux

FreeBSD
Linux
macOS

All OS with a Java VM

AIX
Linux
macOS
Ubuntu
Windows

Linux

Data scheme

Schema and schema-less with LDAP views

yes

depending on used data model

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

Close to ANSI SQL (SQL/JSON + extensions)

yes

ANSI SQL 2011

APIs and other access methods

LDAP

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

Clojure
Java

.Net
C++
Java
JavaScript (Node.js)
Python

C
C++
Java

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Triggers

yes

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

key based and custom

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning (by range, list and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

none

But extensive use of caching in the application peers

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic execution of specific operations

ACID

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

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

yes

Access rights for users, groups and roles according to SQL-standard

More information provided by the system vendor

Specific characteristics

InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
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