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DBMS > Firebase Realtime Database vs. Oracle Berkeley DB vs. Spark SQL vs. Sphinx vs. TerarkDB

System Properties Comparison Firebase Realtime Database vs. Oracle Berkeley DB vs. Spark SQL vs. Sphinx vs. TerarkDB

Editorial information provided by DB-Engines
NameFirebase Realtime Database  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSpark SQL  Xexclude from comparisonSphinx  Xexclude from comparisonTerarkDB  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.Widely used in-process key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processingOpen source search engine for searching in data from different sources, e.g. relational databasesA key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB
Primary database modelDocument storeKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMSSearch engineKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score14.29
Rank#39  Overall
#6  Document stores
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score5.98
Rank#56  Overall
#5  Search engines
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitefirebase.google.com/­products/­realtime-databasewww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sqlsphinxsearch.comgithub.com/­bytedance/­terarkdb
Technical documentationfirebase.google.com/­docs/­databasedocs.oracle.com/­cd/­E17076_05/­html/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlsphinxsearch.com/­docsbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperGoogle infoacquired by Google 2014Oracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software FoundationSphinx Technologies Inc.ByteDance, originally Terark
Initial release20121994201420012016
Current release18.1.40, May 20203.5.0 ( 2.13), September 20233.5.1, February 2023
License infoCommercial or Open SourcecommercialOpen Source infocommercial license availableOpen Source infoApache 2.0Open Source infoGPL version 2, commercial licence availablecommercial inforestricted open source version available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, Java, C++ (depending on the Berkeley DB edition)ScalaC++C++
Server operating systemshostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Windows
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeschema-freeschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyesnoyesnono
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.noyes infoonly with the Berkeley DB XML editionnono
Secondary indexesyesyesnoyes infofull-text index on all search fieldsno
SQL infoSupport of SQLnoyes infoSQL interfaced based on SQLite is availableSQL-like DML and DDL statementsSQL-like query language (SphinxQL)no
APIs and other access methodsAndroid
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
Proprietary protocolC++ API
Java API
Supported programming languagesJava
JavaScript
Objective-C
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Java
Python
R
Scala
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C++
Java
Server-side scripts infoStored procedureslimited functionality with using 'rules'nononono
TriggersCallbacks are triggered when data changesyes infoonly for the SQL APInonono
Partitioning methods infoMethods for storing different data on different nodesnoneyes, utilizing Spark CoreSharding infoPartitioning is done manually, search queries against distributed index is supportednone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnonenonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesACIDnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlyes, based on authentication and database rulesnononono

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More resources
Firebase Realtime DatabaseOracle Berkeley DBSpark SQLSphinxTerarkDB
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