DBMS > GeoMesa vs. HBase vs. Kinetica vs. Microsoft SQL Server vs. MySQL

System Properties Comparison GeoMesa vs. HBase vs. Kinetica vs. Microsoft SQL Server vs. MySQL

Editorial information provided by DB-Engines

Name

Description

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Fully vectorized database across both GPUs and CPUs

Microsofts flagship relational DBMS

Widely used open source RDBMS

Primary database model

Spatial DBMS

Wide column store

Relational DBMS

Key/Value like access via memcached API

Secondary database models

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

Document store
Spatial DBMS

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

Score	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Website

www.geomesa.org

hbase.apache.org

www.kinetica.com

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

www.mysql.com

Technical documentation

www.geomesa.org/documentation/stable/user/index.html

hbase.apache.org/book.html

docs.kinetica.com

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

dev.mysql.com/doc

Developer

CCRi and others

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Kinetica

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Initial release

2014

2008

2012

1989

1995

Current release

4.0.5, February 2024

2.3.4, January 2021

7.1, August 2021

SQL Server 2022, November 2022

8.4.0, April 2024

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache version 2

commercial

restricted free version is available

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Cloud-based only

Only available as a cloud service

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Implementation language

Scala

Java

C, C++

C++

C and C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux

Linux
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

SQL-like DML and DDL statements

yes

with proprietary extensions

APIs and other access methods

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
RESTful HTTP API

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

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
JavaScript (Node.js)
Python

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Coprocessors in Java

user defined functions

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

yes

proprietary syntax

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

Sharding

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

Multi-source replication
Source-replica replication

Source-replica replication

yes, but depending on the SQL-Server Edition

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

depending on storage layer

Immediate Consistency or Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Single row ACID (across millions of columns)

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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.

depending on storage layer

yes

GPU vRAM or System RAM

yes

User concepts

Access control

yes

depending on the DBMS used for storage

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

no user groups or roles

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