DBMS > OrigoDB vs. Spark SQL vs. SpatiaLite vs. VoltDB

System Properties Comparison OrigoDB vs. Spark SQL vs. SpatiaLite vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

A fully ACID in-memory object graph database

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

Spatial extension of SQLite

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store
Object oriented DBMS

Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

origodb.com

spark.apache.org/sql

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

www.voltdb.com

Technical documentation

origodb.com/docs

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

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

docs.voltdb.com

Developer

Robert Friberg et al

Apache Software Foundation

Alessandro Furieri

VoltDB Inc.

Initial release

2009

under the name LiveDB

2014

2008

2010

Current release

3.5.0 ( 2.13), September 2023

5.0.0, August 2020

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

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

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

C++

Java, C++

Server operating systems

Linux
Windows

Linux
OS X
Windows

server-less

Linux
OS X

for development

Data scheme

yes

Typing

predefined data types such as float or date

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

.NET Client API
HTTP API
LINQ

JDBC
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

.Net

Java
Python
R
Scala

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

Java

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

client side managed; servers are not synchronized

yes, utilizing Spark Core

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Foreign keys

Referential integrity

depending on model

yes

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Write ahead log

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Role based authorization

Users and roles with access to stored procedures

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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