DBMS > etcd vs. Firebase Realtime Database vs. Oracle vs. Oracle Rdb vs. Solr

System Properties Comparison etcd vs. Firebase Realtime Database vs. Oracle vs. Oracle Rdb vs. Solr

Editorial information provided by DB-Engines

Name

Description

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

A widely used distributed, scalable search engine based on Apache Lucene

Primary database model

Secondary database models

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

Spatial DBMS

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

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

etcd.io
github.com/etcd-io/etcd

firebase.google.com/products/realtime-database

www.oracle.com/database

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

solr.apache.org

Technical documentation

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

firebase.google.com/docs/database

docs.oracle.com/en/database

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

solr.apache.org/resources.html

Developer

Google

acquired by Google 2014

Oracle

Oracle, originally developed by Digital Equipment Corporation (DEC)

Apache Software Foundation

Initial release

2012

1980

1984

2006

Current release

3.4, August 2019

23c, September 2023

7.4.1.1, 2021

9.6.0, April 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

Open Source

Apache Version 2

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

C and C++

Java

Server operating systems

FreeBSD
Linux
Windows

experimental

hosted

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

HP Open VMS

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

Flexible Schema (defined schema, partial schema, schema free)

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

All search fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

yes

Solr Parallel SQL Interface

APIs and other access methods

gRPC
JSON over HTTP

Android
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Java API
RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

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

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

limited functionality with using 'rules'

PL/SQL

also stored procedures in Java possible

Java plugins

Triggers

yes, watching key changes

Callbacks are triggered when data changes

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

isolation level can be parameterized

yes, on a single node

optimistic locking

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'

yes

User concepts

Access control

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

yes

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