DBMS > Firebase Realtime Database vs. KeyDB vs. MarkLogic vs. RethinkDB

System Properties Comparison Firebase Realtime Database vs. KeyDB vs. MarkLogic vs. RethinkDB

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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.

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Operational and transactional Enterprise NoSQL database

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Primary database model

Document store

Key-value store

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Document store

Secondary database models

Spatial DBMS

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

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	0.63
Rank	#232	Overall
	#32	Key-value stores

Score	4.15
Rank	#70	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#8	Search engines

Score	2.58
Rank	#105	Overall
	#20	Document stores

Website

firebase.google.com/products/realtime-database

github.com/Snapchat/KeyDB
keydb.dev

www.progress.com/marklogic

rethinkdb.com

Technical documentation

firebase.google.com/docs/database

docs.keydb.dev

www.progress.com/marklogic/documentation

rethinkdb.com/docs

Developer

Google

acquired by Google 2014

EQ Alpha Technology Ltd.

MarkLogic Corp.

The Linux Foundation

since July 2017

Initial release

2012

2019

2001

2009

Current release

11.0, December 2022

2.4.1, August 2020

License

Commercial or Open Source

commercial

Open Source

BSD-3

commercial

restricted free version is available

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++

Server operating systems

hosted

Linux

Linux
OS X
Windows

Data scheme

schema-free

Schema can be enforced

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

yes

string, binary, float, bool, date, geometry

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

by using the Redis Search module

yes

SQL

Support of SQL

yes

SQL92

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

Proprietary protocol

RESP - REdis Serialization Protoco

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

Supported programming languages

Java
JavaScript
Objective-C

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

limited functionality with using 'rules'

Lua

yes

via XQuery or JavaScript

Triggers

Callbacks are triggered when data changes

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

Sharding

range based

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Optimistic locking, atomic execution of commands blocks and scripts

ACID

can act as a resource manager in an XA/JTA transaction

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

In-memory capabilities

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

yes

yes, with Range Indexes

User concepts

Access control

yes, based on authentication and database rules

simple password-based access control and ACL

Role-based access control at the document and subdocument levels

yes

users and table-level permissions

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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Meet some database management systems you are likely to hear more about in the future
4 August 2014, Paul Andlinger

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Conferences, events and webinars

more DBMS events

Progress MarkLogic World
Washington DC, 23-25 September 2024 (finished)

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