DBMS > CouchDB vs. DolphinDB vs. Firebase Realtime Database vs. Oracle vs. Spark SQL

System Properties Comparison CouchDB vs. DolphinDB vs. Firebase Realtime Database vs. Oracle vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Widely used RDBMS

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

Primary database model

Secondary database models

Spatial DBMS

using the Geocouch extension

Relational DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

couchdb.apache.org

www.dolphindb.com

firebase.google.com/products/realtime-database

www.oracle.com/database

spark.apache.org/sql

Technical documentation

docs.couchdb.org/en/stable

docs.dolphindb.cn/en/help200/index.html

firebase.google.com/docs/database

docs.oracle.com/en/database

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

DolphinDB, Inc

Google

acquired by Google 2014

Oracle

Apache Software Foundation

Initial release

2005

2018

2012

1980

2014

Current release

3.3.3, December 2023

v2.00.4, January 2022

23c, September 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache version 2

commercial

free community version available

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

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Implementation language

Erlang

C++

C and C++

Scala

Server operating systems

Android
BSD
Linux
OS X
Solaris
Windows

Linux
Windows

hosted

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

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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

yes

via views

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

Android
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

Java
JavaScript
Objective-C

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

View functions in JavaScript

yes

limited functionality with using 'rules'

PL/SQL

also stored procedures in Java possible

Triggers

yes

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

horizontal partitioning

Sharding, horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

atomic operations within a single document possible

yes

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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 database

Administrators, Users, Groups

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

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