DBMS > Apache Doris vs. ClickHouse vs. HugeGraph vs. TDSQL for MySQL

System Properties Comparison Apache Doris vs. ClickHouse vs. HugeGraph vs. TDSQL for MySQL

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Editorial information provided by DB-Engines

Name

Description

An MPP-based analytics DBMS embracing the MySQL protocol

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 fast-speed and highly-scalable Graph DBMS

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	0.60
Rank	#247	Overall
	#113	Relational DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.17
Rank	#335	Overall
	#31	Graph DBMS

Score	0.85
Rank	#207	Overall
	#97	Relational DBMS

Website

doris.apache.org
github.com/apache/doris

clickhouse.com

github.com/hugegraph
hugegraph.apache.org

www.tencentcloud.com/products/dcdb

Technical documentation

github.com/apache/doris/wiki

clickhouse.com/docs

hugegraph.apache.org/docs

www.tencentcloud.com/document/product/1042

Developer

Apache Software Foundation, originally contributed from Baidu

Clickhouse Inc.

Baidu

Tencent

Initial release

2017

2016

2018

2013

Current release

1.2.2, February 2023

v24.4.1.2088-stable, May 2024

0.9

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

C++

Java

Server operating systems

Linux

FreeBSD
Linux
macOS

Linux
macOS
Unix

hosted

Data scheme

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

Close to ANSI SQL (SQL/JSON + extensions)

yes

APIs and other access methods

JDBC
MySQL client

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

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC

Supported programming languages

Java

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

Groovy
Java
Python

C
C#
C++
Java
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

yes

asynchronous Gremlin script jobs

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

key based and custom

yes

depending on used storage backend, e.g. Cassandra and HBase

Automatic sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

yes

depending on used storage backend, e.g. Cassandra and HBase

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

edges in graph

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

In-memory capabilities

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

yes

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.

Users, roles and permissions

Users with fine-grained authorization concept

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