DBMS > Atos Standard Common Repository vs. ClickHouse vs. etcd vs. HBase

System Properties Comparison Atos Standard Common Repository vs. ClickHouse vs. etcd vs. HBase

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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

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.

A distributed reliable key-value store

Wide-column store based on Apache Hadoop and on concepts of BigTable

Primary database model

Document store
Key-value store

Relational DBMS

Key-value store

Wide column store

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	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Website

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

clickhouse.com

etcd.io
github.com/etcd-io/etcd

hbase.apache.org

Technical documentation

clickhouse.com/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

hbase.apache.org/book.html

Developer

Atos Convergence Creators

Clickhouse Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2016

2008

Current release

1703

v24.4.1.2088-stable, May 2024

3.4, August 2019

2.3.4, January 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Apache Version 2.0

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.

ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

Java

C++

Java

Server operating systems

Linux

FreeBSD
Linux
macOS

FreeBSD
Linux
Windows

experimental

Linux
Unix
Windows

using Cygwin

Data scheme

Schema and schema-less with LDAP views

yes

schema-free

schema-free, schema definition possible

Typing

predefined data types such as float or date

optional

yes

options to bring your own types, AVRO

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)

APIs and other access methods

LDAP

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

gRPC
JSON over HTTP

Java API
RESTful HTTP API
Thrift

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
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

yes

Coprocessors in Java

Triggers

yes

yes, watching key changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

key based and custom

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomic execution of specific operations

Single row ACID (across millions of columns)

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

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 Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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