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DBMS > Citus vs. gStore vs. Spark SQL

System Properties Comparison Citus vs. gStore vs. Spark SQL

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Editorial information provided by DB-Engines
NameCitus  Xexclude from comparisongStore  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLA native Graph DBMS to store and maintain very large RDF datasets.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSGraph DBMS
RDF store
Relational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.15
Rank#117  Overall
#56  Relational DBMS
Score0.14
Rank#342  Overall
#34  Graph DBMS
#16  RDF stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websitewww.citusdata.comen.gstore.cnspark.apache.org/­sql
Technical documentationdocs.citusdata.comen.gstore.cn/­#/­enDocsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation
Initial release201020162014
Current release8.1, December 20181.2, November 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availableOpen Source infoBSDOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCC++Scala
Server operating systemsLinuxLinuxLinux
OS X
Windows
Data schemeyesschema-free and OWL/RDFS-schema supportyes
Typing infopredefined data types such as float or dateyesyesyes
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 functionalitynono
Secondary indexesyesno
SQL infoSupport of SQLyes infostandard, with numerous extensionsnoSQL-like DML and DDL statements
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
HTTP API
SPARQL 1.1
JDBC
ODBC
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C++
Java
JavaScript (Node.js)
PHP
Python
Java
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.yesno
Triggersyesno
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integrityyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonono
User concepts infoAccess controlfine grained access rights according to SQL-standardUsers, roles and permissions, Role-Based Access Control (RBAC) supportedno

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More resources
CitusgStoreSpark SQL
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