DBMS > H2GIS vs. Microsoft SQL Server vs. OpenMLDB vs. TinkerGraph vs. Vertica

System Properties Comparison H2GIS vs. Microsoft SQL Server vs. OpenMLDB vs. TinkerGraph vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Spatial extension of H2

Microsofts flagship relational DBMS

An open-source machine learning database that provides a feature platform for training and inference

A lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API

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

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

Spatial DBMS
Time Series 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	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	0.13
Rank	#345	Overall
	#35	Graph DBMS

Score	10.06
Rank	#42	Overall
	#26	Relational DBMS

Website

www.h2gis.org

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

openmldb.ai

tinkerpop.apache.org/docs/current/reference/#tinkergraph-gremlin

www.vertica.com

Technical documentation

www.h2gis.org/docs/home

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

openmldb.ai/docs/zh/main

vertica.com/documentation

Developer

CNRS

Microsoft

4 Paradigm Inc.

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2013

1989

2020

2009

2005

Current release

SQL Server 2022, November 2022

2024-2 February 2024

12.0.3, January 2023

License

Commercial or Open Source

Open Source

LGPL 3.0

commercial

restricted free version is available

Open Source

Apache 2.0

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

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

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Implementation language

Java

C++

C++, Java, Scala

Java

C++

Server operating systems

Linux
Windows

Linux

Data scheme

yes

Fixed schema

schema-free

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

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

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

APIs and other access methods

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

JDBC
SQLAlchemy

TinkerPop 3

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

character-based, interactive, front-end utility

Supported programming languages

Java

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

C++
Go
Java
Python
Scala

Groovy
Java

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

Server-side scripts

Stored procedures

yes

based on H2

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

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

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

horizontal partitioning

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

yes, but depending on the SQL-Server Edition

Source-replica replication

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

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

optional

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

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

More information provided by the system vendor

Specific characteristics

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Licensing and pricing models

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