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DBMS > Citus vs. Firebase Realtime Database vs. Hazelcast vs. HEAVY.AI vs. Splice Machine

System Properties Comparison Citus vs. Firebase Realtime Database vs. Hazelcast vs. HEAVY.AI vs. Splice Machine

Editorial information provided by DB-Engines
NameCitus  Xexclude from comparisonFirebase Realtime Database  Xexclude from comparisonHazelcast  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLCloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.A widely adopted in-memory data gridA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSDocument storeKey-value storeRelational DBMSRelational DBMS
Secondary database modelsDocument storeDocument store infoJSON support with IMDG 3.12Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.21
Rank#118  Overall
#56  Relational DBMS
Score14.29
Rank#39  Overall
#6  Document stores
Score5.97
Rank#57  Overall
#6  Key-value stores
Score1.77
Rank#141  Overall
#65  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websitewww.citusdata.comfirebase.google.com/­products/­realtime-databasehazelcast.comgithub.com/­heavyai/­heavydb
www.heavy.ai
splicemachine.com
Technical documentationdocs.citusdata.comfirebase.google.com/­docs/­databasehazelcast.org/­imdg/­docsdocs.heavy.aisplicemachine.com/­how-it-works
DeveloperGoogle infoacquired by Google 2014HazelcastHEAVY.AI, Inc.Splice Machine
Initial release20102012200820162014
Current release8.1, December 20185.3.6, November 20235.10, January 20223.1, March 2021
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availablecommercialOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache Version 2; enterprise edition availableOpen Source infoAGPL 3.0, commercial license 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 languageCJavaC++ and CUDAJava
Server operating systemsLinuxhostedAll OS with a Java VMLinuxLinux
OS X
Solaris
Windows
Data schemeyesschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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.yes infospecific XML type available, but no XML query functionalitynoyes infothe object must implement a serialization strategyno
Secondary indexesyesyesyesnoyes
SQL infoSupport of SQLyes infostandard, with numerous extensionsnoSQL-like query languageyesyes
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Android
iOS
JavaScript API
RESTful HTTP API
JCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
ODBC
Thrift
Vega
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Java
JavaScript
Objective-C
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
All languages supporting JDBC/ODBC/Thrift
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.limited functionality with using 'rules'yes infoEvent Listeners, Executor Servicesnoyes infoJava
TriggersyesCallbacks are triggered when data changesyes infoEventsnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoRound robinShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsyes infoReplicated MapMulti-source replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesnoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infoif the client is offline
Immediate Consistency infoif the client is online
Immediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesone or two-phase-commit; repeatable reads; read commitednoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardyes, based on authentication and database rulesRole-based access controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard

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More resources
CitusFirebase Realtime DatabaseHazelcastHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Splice Machine
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