DBMS > Sadas Engine vs. SiriDB vs. Spark SQL vs. SpatiaLite

System Properties Comparison Sadas Engine vs. SiriDB vs. Spark SQL vs. SpatiaLite

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Editorial information provided by DB-Engines

Name

Description

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open Source Time Series DBMS

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

Spatial extension of SQLite

Primary database model

Secondary database models

Relational DBMS

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

Score	0.07
Rank	#373	Overall
	#157	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.63
Rank	#146	Overall
	#3	Spatial DBMS

Website

www.sadasengine.com

siridb.com

spark.apache.org/sql

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

docs.siridb.com

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

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

SADAS s.r.l.

Cesbit

Apache Software Foundation

Alessandro Furieri

Initial release

2006

2017

2014

2008

Current release

8.0

3.5.0 ( 2.13), September 2023

5.0.0, August 2020

License

Commercial or Open Source

commercial

free trial version available

Open Source

MIT License

Open Source

Apache 2.0

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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

C++

Scala

C++

Server operating systems

AIX
Linux
Windows

Linux

Linux
OS X
Windows

server-less

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data

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

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol

HTTP API

JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
Groovy
Java
PHP
Python

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

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

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights for users, groups and roles according to SQL-standard

simple rights management via user accounts

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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