DBMS > FatDB vs. GeoMesa vs. Ingres vs. PostgreSQL vs. Spark SQL

System Properties Comparison FatDB vs. GeoMesa vs. Ingres vs. PostgreSQL vs. Spark SQL

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

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

Well established RDBMS

Widely used open source RDBMS

Developed as objectoriented DBMS (Postgres), gradually enhanced with 'standards' like SQL

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

Primary database model

Document store
Key-value store

Spatial DBMS

Relational DBMS

with object oriented extensions, e.g.: user defined types/functions and inheritance. Handling of key/value pairs with hstore module.

Relational DBMS

Secondary database models

Document store
Graph DBMS

with Apache Age
Spatial DBMS
Vector DBMS

with pgvector extension

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

Score	0.86
Rank	#205	Overall
	#4	Spatial DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Score	636.25
Rank	#4	Overall
	#4	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

www.geomesa.org

www.actian.com/databases/ingres

www.postgresql.org

spark.apache.org/sql

Technical documentation

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

docs.actian.com/ingres

www.postgresql.org/docs

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

Developer

FatCloud

CCRi and others

Actian Corporation

PostgreSQL Global Development Group

www.postgresql.org/developer

Apache Software Foundation

Initial release

2012

2014

1974

originally developed at University Berkely in early 1970s

1989

1989: Postgres, 1996: PostgreSQL

2014

Current release

5.0.0, May 2024

11.2, May 2022

16.3, May 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

commercial

Open Source

BSD

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

Server operating systems

Windows

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

FreeBSD
HP-UX
Linux
NetBSD
OpenBSD
OS X
Solaris
Unix
Windows

Linux
OS X
Windows

Data scheme

schema-free

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.

but tools for importing/exporting data from/to XML-files available

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

Via inetgration in SQL Server

yes

standard with numerous extensions

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java

JDBC
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

depending on storage layer

horizontal partitioning

Ingres Star to access multiple databases simultaneously

partitioning by range, list and (since PostgreSQL 11) by hash

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

depending on storage layer

Ingres Replicator

Source-replica replication

other methods possible by using 3rd party extensions

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

depending on storage layer

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

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.

depending on storage layer

User concepts

Access control

Can implement custom security layer via applications

yes

depending on the DBMS used for storage

fine grained access rights according to SQL-standard

More information provided by the system vendor

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