DBMS > Citus vs. Oracle Rdb vs. Spark SQL vs. WakandaDB

System Properties Comparison Citus vs. Oracle Rdb vs. Spark SQL vs. WakandaDB

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Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Relational DBMS

Object oriented DBMS

Secondary database models

Document store

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

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.citusdata.com

www.oracle.com/database/technologies/related/rdb.html

spark.apache.org/sql

wakanda.github.io

Technical documentation

docs.citusdata.com

www.oracle.com/database/technologies/related/rdb-doc.html

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

wakanda.github.io/doc

Developer

Oracle, originally developed by Digital Equipment Corporation (DEC)

Apache Software Foundation

Wakanda SAS

Initial release

2010

1984

2014

2012

Current release

8.1, December 2018

7.4.1.1, 2021

3.5.0 ( 2.13), September 2023

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Open Source

Apache 2.0

Open Source

AGPLv3, extended commercial license available

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++, JavaScript

Server operating systems

Linux

HP Open VMS

Linux
OS X
Windows

Data scheme

yes

Flexible Schema (defined schema, partial schema, 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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

yes

standard, with numerous extensions

yes

SQL-like DML and DDL statements

APIs and other access methods

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

JDBC
ODBC

RESTful HTTP API

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Python
R
Scala

JavaScript

Server-side scripts

Stored procedures

user defined functions

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

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

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

yes, on a single node

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.

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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