DBMS > ClickHouse vs. JanusGraph vs. MonetDB vs. searchxml

System Properties Comparison ClickHouse vs. JanusGraph vs. MonetDB vs. searchxml

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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.

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

A relational database management system that stores data in columns

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Native XML DBMS
Search engine

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Native XML DBMS
	#25	Search engines

Website

clickhouse.com

janusgraph.org

www.monetdb.org

www.searchxml.net/category/products

Technical documentation

clickhouse.com/docs

docs.janusgraph.org

www.monetdb.org/Documentation

www.searchxml.net/support/handouts

Developer

Clickhouse Inc.

Linux Foundation; originally developed as Titan by Aurelius

MonetDB BV

informationpartners gmbh

Initial release

2016

2017

2004

2015

Current release

v24.4.1.2088-stable, May 2024

0.6.3, February 2023

Dec2023 (11.49), December 2023

1.0

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

Mozilla Public License 2.0

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.

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.
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

C++

Server operating systems

FreeBSD
Linux
macOS

Linux
OS X
Unix
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Windows

Data scheme

yes

schema-free

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

SQL 2003 with some extensions

APIs and other access methods

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

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

RESTful HTTP API
WebDAV
XQuery
XSLT

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

Clojure
Java
Python

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

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

yes

yes, in SQL, C, R

yes

on the application server

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

none

Source-replica replication available in experimental status

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

ACID

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

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

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.

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Domain, group and role-based access control at the document level and for application services

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