DBMS > ClickHouse vs. DataFS vs. Datomic vs. OpenEdge

System Properties Comparison ClickHouse vs. DataFS vs. Datomic vs. OpenEdge

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Editorial information provided by DB-Engines

Name

Description

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.

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Application development environment with integrated database management system

Primary database model

Relational DBMS

Object oriented DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Graph DBMS

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Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	3.45
Rank	#85	Overall
	#46	Relational DBMS

Website

clickhouse.com

newdatabase.com

www.datomic.com

www.progress.com/openedge

Technical documentation

clickhouse.com/docs

dev.mobiland.com/Overview.xsp

docs.datomic.com

documentation.progress.com/output/ua/OpenEdge_latest

Developer

Clickhouse Inc.

Mobiland AG

Cognitect

Progress Software Corporation

Initial release

2016

2018

2012

1984

Current release

v24.4.1.2088-stable, May 2024

1.1.263, October 2022

1.0.7075, December 2023

OpenEdge 12.2, March 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

limited edition free

commercial

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.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.

Implementation language

C++

Java, Clojure

Server operating systems

FreeBSD
Linux
macOS

Windows

All OS with a Java VM

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Typing

predefined data types such as float or date

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

close to SQL 92

APIs and other access methods

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

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP API

JDBC
ODBC

Supported programming languages

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#
C++
VB.Net

Clojure
Java

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

Triggers

no, except callback-events from server when changes happened

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Proprietary Sharding system

none

But extensive use of caching in the application peers

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

User concepts

Access control

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.

Windows-Profile

Users and groups

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