DBMS > H2GIS vs. Microsoft SQL Server vs. Snowflake vs. Spark SQL

System Properties Comparison H2GIS vs. Microsoft SQL Server vs. Snowflake vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Spatial extension of H2

Microsofts flagship relational DBMS

Cloud-based data warehousing service for structured and semi-structured data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	0.08
Rank	#368	Overall
	#7	Spatial DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.h2gis.org

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

www.snowflake.com

spark.apache.org/sql

Technical documentation

www.h2gis.org/docs/home

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

docs.snowflake.net/manuals/index.html

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

CNRS

Microsoft

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2013

1989

2014

Current release

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

LGPL 3.0

commercial

restricted free version is available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Scala

Server operating systems

Linux
Windows

hosted

Linux
OS X
Windows

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

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

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

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

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Java

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

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

based on H2

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

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

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

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

yes, but depending on the SQL-Server Edition

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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, multi-version concurrency control (MVCC)

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

yes

based on H2

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept, user roles and pluggable authentication

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