DBMS > ClickHouse vs. GBase vs. HugeGraph vs. Microsoft Azure SQL Database vs. Microsoft SQL Server

System Properties Comparison ClickHouse vs. GBase vs. HugeGraph vs. Microsoft Azure SQL Database vs. Microsoft SQL Server

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.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

A fast-speed and highly-scalable Graph DBMS

Database as a Service offering with high compatibility to Microsoft SQL Server

Microsofts flagship relational DBMS

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	16.34
Rank	#38	Overall
	#23	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

clickhouse.com

www.gbase.cn

github.com/hugegraph
hugegraph.apache.org

azure.microsoft.com/en-us/products/azure-sql/database

www.microsoft.com/en-us/sql-server

Technical documentation

clickhouse.com/docs

hugegraph.apache.org/docs

docs.microsoft.com/en-us/azure/azure-sql

learn.microsoft.com/en-US/sql/sql-server

Developer

Clickhouse Inc.

General Data Technology Co., Ltd.

Baidu

Microsoft

Initial release

2016

2004

2018

2010

1989

Current release

v24.4.1.2088-stable, May 2024

GBase 8a, GBase 8s, GBase 8c

0.9

V12

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

C++

C, Java, Python

Java

C++

Server operating systems

FreeBSD
Linux
macOS

Linux

Linux
macOS
Unix

hosted

Linux
Windows

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.

yes

Secondary indexes

yes

also supports composite index and range index

yes

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

Standard with numerous extensions

yes

APIs and other access methods

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

ADO.NET
C API
JDBC
ODBC

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

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

Groovy
Java
Python

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

user defined functions

asynchronous Gremlin script jobs

Transact SQL

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

horizontal partitioning (by range, list and hash) and vertical partitioning

yes

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

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

yes, with always 3 replicas available

yes, but depending on the SQL-Server Edition

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

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

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.

yes

Users, roles and permissions

fine grained access rights according to SQL-standard

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