DBMS > AgensGraph vs. ClickHouse vs. Dgraph vs. MarkLogic

System Properties Comparison AgensGraph vs. ClickHouse vs. Dgraph vs. MarkLogic

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

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.

Distributed and scalable native Graph DBMS

Operational and transactional Enterprise NoSQL database

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Graph DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Secondary database models

Time Series DBMS

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

Score	0.17
Rank	#320	Overall
	#29	Graph DBMS
	#143	Relational DBMS

Score	15.88
Rank	#37	Overall
	#22	Relational DBMS

Score	1.39
Rank	#155	Overall
	#15	Graph DBMS

Score	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Website

bitnine.net/agensgraph

clickhouse.com

dgraph.io

www.progress.com/marklogic

Technical documentation

bitnine.net/documentation

clickhouse.com/docs

dgraph.io/docs

www.progress.com/marklogic/documentation

Developer

Bitnine Global Inc.

Clickhouse Inc.

Dgraph Labs, Inc.

MarkLogic Corp.

Initial release

2016

2001

Current release

2.1, December 2018

v24.6.2.17-stable, July 2024

11.0, December 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

restricted free version is available

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++

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
macOS

Linux
OS X
Windows

Data scheme

depending on used data model

yes

schema-free

Schema can be enforced

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

yes

Close to ANSI SQL (SQL/JSON + extensions)

yes

SQL92

APIs and other access methods

Cypher Query Language
JDBC

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

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

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

Supported programming languages

C
Java
JavaScript
Python

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++
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

key based and custom

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

Synchronous replication via Raft

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

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

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

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

yes, with Range Indexes

User concepts

Access control

fine grained access rights according to SQL-standard

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.

Planned for future releases

Role-based access control at the document and subdocument levels

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