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

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

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTerarkDB  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityCloud-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 processingA 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 modelRelational DBMSDocument storeKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
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
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitewww.datomic.comfirebase.google.com/­products/­realtime-databasewww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sqlgithub.com/­bytedance/­terarkdb
Technical documentationdocs.datomic.comfirebase.google.com/­docs/­databasedocs.oracle.com/­cd/­E17076_05/­html/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlbytedance.larkoffice.com/­docs/­doccnZmYFqHBm06BbvYgjsHHcKc
DeveloperCognitectGoogle infoacquired by Google 2014Oracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software FoundationByteDance, originally Terark
Initial release20122012199420142016
Current release1.0.6735, June 202318.1.40, May 20203.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialOpen Source infocommercial license availableOpen Source infoApache 2.0commercial inforestricted open source version available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureC, Java, C++ (depending on the Berkeley DB edition)ScalaC++
Server operating systemsAll OS with a Java VMhostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Windows
Data schemeyesschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesnoyesno
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.nonoyes infoonly with the Berkeley DB XML editionnono
Secondary indexesyesyesyesnono
SQL infoSupport of SQLnonoyes infoSQL interfaced based on SQLite is availableSQL-like DML and DDL statementsno
APIs and other access methodsRESTful HTTP APIAndroid
iOS
JavaScript API
RESTful HTTP API
JDBC
ODBC
C++ API
Java API
Supported programming languagesClojure
Java
Java
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++
Java
Server-side scripts infoStored proceduresyes infoTransaction Functionslimited functionality with using 'rules'nonono
TriggersBy using transaction functionsCallbacks are triggered when data changesyes infoonly for the SQL APInono
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersnoneyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersSource-replica replicationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual 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 dataACIDyesACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentyesnoyes
User concepts infoAccess controlnoyes, based on authentication and database rulesnonono

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More resources
DatomicFirebase Realtime DatabaseOracle Berkeley DBSpark SQLTerarkDB
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