DBMS > GeoMesa vs. Spark SQL vs. ToroDB vs. Transbase

System Properties Comparison GeoMesa vs. Spark SQL vs. ToroDB vs. Transbase

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Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

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

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

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

Score	0.81
Rank	#214	Overall
	#4	Spatial DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

www.geomesa.org

spark.apache.org/sql

github.com/torodb/server

www.transaction.de/en/products/transbase.html

Technical documentation

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

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

www.transaction.de/en/products/transbase/features.html

Developer

CCRi and others

Apache Software Foundation

8Kdata

Transaction Software GmbH

Initial release

2014

2016

1987

Current release

4.0.5, February 2024

3.5.0 ( 2.13), September 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache 2.0

Open Source

AGPL-V3

commercial

free development license

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Scala

Java

C and C++

Server operating systems

Linux
OS X
Windows

All OS with a Java 7 VM

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java
Python
R
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

none

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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.

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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