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DBMS > Apache Spark (SQL) vs. Citus

System Properties Comparison Apache Spark (SQL) vs. Citus

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Our visitors often compare Apache Spark (SQL) and Citus with PostgreSQL, TimescaleDB and ClickHouse.

Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonCitus  Xexclude from comparison
DescriptionApache Spark SQL is a component on top of 'Spark Core' for structured data processingScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL
Primary database modelRelational DBMSRelational DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.80
Rank#31  Overall
#19  Relational DBMS
Score2.04
Rank#114  Overall
#55  Relational DBMS
Websitespark.apache.org/­sqlwww.citusdata.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.citusdata.com
DeveloperApache Software Foundation
Initial release20142010
Current release3.5.0 ( 2.13), September 20238.1, December 2018
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoAGPL, commercial license also available
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC
Server operating systemsLinux
OS X
Windows
Linux
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
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 infospecific XML type available, but no XML query functionality
Secondary indexesnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infostandard, with numerous extensions
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesJava
Python
R
Scala
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.
Triggersnoyes
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replication infoother methods possible by using 3rd party extensions
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nono
User concepts infoAccess controlnofine grained access rights according to SQL-standard

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More resources
Apache Spark (SQL)Citus
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