DBMS > ClickHouse vs. Ingres vs. MarkLogic vs. OrientDB

System Properties Comparison ClickHouse vs. Ingres vs. MarkLogic vs. OrientDB

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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.

Well established RDBMS

Operational and transactional Enterprise NoSQL database

Multi-model DBMS (Document, Graph, Key/Value)

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store
Graph DBMS
Key-value store

Secondary database models

Time Series DBMS

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	3.25
Rank	#89	Overall
	#16	Document stores
	#6	Graph DBMS
	#13	Key-value stores

Website

clickhouse.com

www.actian.com/databases/ingres

www.progress.com/marklogic

orientdb.org

Technical documentation

clickhouse.com/docs

docs.actian.com/ingres

www.progress.com/marklogic/documentation

www.orientdb.com/docs/last/index.html

Developer

Clickhouse Inc.

Actian Corporation

MarkLogic Corp.

OrientDB LTD; CallidusCloud; SAP

Initial release

2016

1974

originally developed at University Berkely in early 1970s

2001

2010

Current release

v24.4.2.141-stable, June 2024

11.2, May 2022

11.0, December 2022

3.2.29, March 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

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.

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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.

Implementation language

C++

Java

Server operating systems

FreeBSD
Linux
macOS

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Linux
OS X
Windows

All OS with a Java JDK (>= JDK 6)

Data scheme

yes

schema-free

Schema can be enforced

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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.

but tools for importing/exporting data from/to XML-files available

yes

Secondary indexes

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

yes

SQL92

SQL-like query language, no joins

APIs and other access methods

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

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

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

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

Java, Javascript

Triggers

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

key based and custom

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

Ingres Replicator

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

can act as a resource manager in an XA/JTA transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

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

yes, with Range Indexes

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.

fine grained access rights according to SQL-standard

Role-based access control at the document and subdocument levels

Access rights for users and roles; record level security configurable

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