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DBMS > Firebase Realtime Database vs. Google Cloud Datastore vs. KeyDB vs. ObjectBox

System Properties Comparison Firebase Realtime Database vs. Google Cloud Datastore vs. KeyDB vs. ObjectBox

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Editorial information provided by DB-Engines
NameFirebase Realtime Database  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonKeyDB  Xexclude from comparisonObjectBox  Xexclude from comparison
DescriptionCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformAn ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocolsLightweight, fast on-device database for IoT, Mobile and Embedded devices, persisting and synchronising objects and vectors
Primary database modelDocument storeDocument storeKey-value storeObject oriented DBMS
Vector DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.64
Rank#39  Overall
#6  Document stores
Score4.36
Rank#72  Overall
#12  Document stores
Score0.70
Rank#229  Overall
#32  Key-value stores
Score1.29
Rank#166  Overall
#5  Object oriented DBMS
#7  Vector DBMS
Websitefirebase.google.com/­products/­realtime-databasecloud.google.com/­datastoregithub.com/­Snapchat/­KeyDB
keydb.dev
github.com/­objectbox
objectbox.io
Technical documentationfirebase.google.com/­docs/­databasecloud.google.com/­datastore/­docsdocs.keydb.devdocs.objectbox.io
DeveloperGoogle infoacquired by Google 2014GoogleEQ Alpha Technology Ltd.ObjectBox Limited
Initial release2012200820192017
Current release4.0 (May 2024)
License infoCommercial or Open SourcecommercialcommercialOpen Source infoBSD-3Bindings are released under Apache 2.0 infoApache License 2.0
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C and C++
Server operating systemshostedhostedLinuxAndroid
Any POSIX system
Docker
iOS
Linux
macOS
QNX
Windows
Data schemeschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyes, details herepartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexesyes, plus "flex" map-like types
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nononono
Secondary indexesyesyesyes infoby using the Redis Search moduleyes
SQL infoSupport of SQLnoSQL-like query language (GQL)nono
APIs and other access methodsAndroid
iOS
JavaScript API
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocol infoRESP - REdis Serialization ProtocoProprietary native API
Supported programming languagesJava
JavaScript
Objective-C
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
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++
Dart (Flutter)
Go
Java
Kotlin
Python
Swift
Server-side scripts infoStored procedureslimited functionality with using 'rules'using Google App EngineLuano
TriggersCallbacks are triggered when data changesCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosMulti-source replication
Source-replica replication
Data sync between devices allowing occasional connected databases to work completely offline
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Eventual Consistency
Strong eventual consistency with CRDTs
Immediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsOptimistic locking, atomic execution of commands blocks and scriptsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logsyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlyes, based on authentication and database rulesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)simple password-based access control and ACLyes
More information provided by the system vendor
Firebase Realtime DatabaseGoogle Cloud DatastoreKeyDBObjectBox
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More resources
Firebase Realtime DatabaseGoogle Cloud DatastoreKeyDBObjectBox
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