DBMS > Microsoft Azure SQL Database vs. openGemini vs. STSdb vs. Transwarp KunDB vs. Vertica

System Properties Comparison Microsoft Azure SQL Database vs. openGemini vs. STSdb vs. Transwarp KunDB vs. Vertica

Editorial information provided by DB-Engines

Name

Description

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

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

OLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Column oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	72.95
Rank	#16	Overall
	#11	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	0.03
Rank	#365	Overall
	#54	Key-value stores

Score	0.04
Rank	#362	Overall
	#154	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

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

www.opengemini.org
github.com/openGemini

github.com/STSSoft/STSdb4

www.transwarp.cn/en/product/kundb

www.vertica.com

Technical documentation

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

docs.opengemini.org/guide

vertica.com/documentation

Developer

Microsoft

Huawei and openGemini community

STS Soft SC

Transwarp

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2010

2022

2011

2005

Current release

V12

1.1, July 2023

4.0.8, September 2015

12.0.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPLv2, commercial license available

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

hosted

Linux
Windows

Windows

Linux

Data scheme

yes

schema-free

yes

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

yes

primitive types and user defined types (classes)

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

yes

SQL-like query language

yes

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

ADO.NET
JDBC
ODBC

HTTP REST

.NET Client API

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

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

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

C#
Java

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

Server-side scripts

Stored procedures

Transact SQL

yes

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with always 3 replicas available

yes

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

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

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

Administrators and common users accounts

yes

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2020
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12 December 2019, Paul Andlinger

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

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