DBMS > GeoSpock vs. Oracle vs. Spark SQL

System Properties Comparison GeoSpock vs. Oracle vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Spatial and temporal data processing engine for extreme data scale

Widely used RDBMS

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

Primary database model

Relational DBMS

Secondary database models

Time Series DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph

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

Score	0.38
Rank	#324	Overall
	#144	Relational DBMS

Score	1231.48
Rank	#1	Overall
	#1	Relational DBMS

Score	20.07
Rank	#35	Overall
	#21	Relational DBMS

Website

geospock.com

www.oracle.com/database

spark.apache.org/sql

Technical documentation

docs.geospock.com/Content/Home.htm

docs.oracle.com/en/database

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

Developer

GeoSpock

Oracle

Apache Software Foundation

Initial release

1980

2014

Current release

2.0, September 2019

21c, January 2021

3.4.0 ( 2.13), April 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java, Javascript

C and C++

Scala

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Linux
OS X
Windows

Data scheme

yes

Schemaless in JSON and XML columns

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.

yes

Secondary indexes

temporal, categorical

yes

SQL

Support of SQL

ANSI SQL for query only (using Presto)

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

JDBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Java
Python
R
Scala

Server-side scripts

Stored procedures

PL/SQL

also stored procedures in Java possible

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic sharding

Sharding, horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

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

isolation level can be parameterized

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

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Access rights for users can be defined per table

fine grained access rights according to SQL-standard

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