DBMS > Atos Standard Common Repository vs. ClickHouse vs. GeoSpock vs. Oracle Coherence vs. Snowflake

System Properties Comparison Atos Standard Common Repository vs. ClickHouse vs. GeoSpock vs. Oracle Coherence vs. Snowflake

Editorial information provided by DB-Engines

Name

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

GeoSpock seems to be discontinued. Therefore it will be excluded 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.

Spatial and temporal data processing engine for extreme data scale

Oracles in-memory data grid solution

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store

Relational DBMS

Secondary database models

Time Series DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	1.92
Rank	#130	Overall
	#22	Key-value stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

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

clickhouse.com

geospock.com

www.oracle.com/java/coherence

www.snowflake.com

Technical documentation

clickhouse.com/docs

docs.oracle.com/en/middleware/standalone/coherence

docs.snowflake.net/manuals/index.html

Developer

Atos Convergence Creators

Clickhouse Inc.

GeoSpock

Oracle

Snowflake Computing Inc.

Initial release

2016

2007

2014

Current release

1703

v24.4.1.2088-stable, May 2024

2.0, September 2019

14.1, August 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

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, Javascript

Java

Server operating systems

Linux

FreeBSD
Linux
macOS

hosted

All OS with a Java VM

hosted

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

temporal, categorical

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

LDAP

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

JDBC

JCache
JPA
RESTful HTTP API

CLI Client
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

.Net
C++
Java

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

Live Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

key based and custom

Automatic sharding

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes, with selectable consistency level

yes

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

Eventual Consistency
Immediate 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

configurable

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

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

Access rights for users can be defined per table

authentification to access the cache via certificates or http basic authentication

Users with fine-grained authorization concept, user roles and pluggable authentication

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