DBMS > AgensGraph vs. Apache Drill vs. ClickHouse vs. Graph Engine

System Properties Comparison AgensGraph vs. Apache Drill vs. ClickHouse vs. Graph Engine

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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

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

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 in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Primary database model

Graph DBMS
Relational DBMS

Document store
Relational DBMS

Relational DBMS

Graph DBMS
Key-value store

Secondary database models

Time Series DBMS

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

Score	0.16
Rank	#333	Overall
	#30	Graph DBMS
	#146	Relational DBMS

Score	2.28
Rank	#119	Overall
	#23	Document stores
	#56	Relational DBMS

Score	16.20
Rank	#37	Overall
	#23	Relational DBMS

Score	0.62
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Website

bitnine.net/agensgraph

drill.apache.org

clickhouse.com

www.graphengine.io

Technical documentation

bitnine.net/documentation

drill.apache.org/docs

clickhouse.com/docs

www.graphengine.io/docs/manual

Developer

Bitnine Global Inc.

Apache Software Foundation

Clickhouse Inc.

Microsoft

Initial release

2016

2012

2016

2010

Current release

2.1, December 2018

1.20.3, January 2023

v24.3.2.23-lts, April 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache Version 2

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Database as a Service

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Implementation language

C++

.NET and C

Server operating systems

Linux
OS X
Windows

FreeBSD
Linux
macOS

.NET

Data scheme

depending on used data model

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL SELECT statement is SQL:2003 compliant

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

Cypher Query Language
JDBC

JDBC
ODBC
RESTful HTTP API

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

RESTful HTTP API

Supported programming languages

C
Java
JavaScript
Python

C++

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++
F#
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

key based and custom

horizontal partitioning

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.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

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

Depending on the underlying data source

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

Depending on the underlying data source

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Depending on the underlying data source

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.

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