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DBMS > Apache Spark (SQL) vs. Citus vs. TimescaleDB

System Properties Comparison Apache Spark (SQL) vs. Citus vs. TimescaleDB

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonCitus  Xexclude from comparisonTimescaleDB  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 PostgreSQLA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score18.45
Rank#31  Overall
#19  Relational DBMS
Score1.98
Rank#114  Overall
#55  Relational DBMS
Score3.62
Rank#73  Overall
#5  Time Series DBMS
Websitespark.apache.org/­sqlwww.citusdata.comwww.timescale.com
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.citusdata.comdocs.timescale.com
DeveloperApache Software FoundationTimescale
Initial release201420102017
Current release3.5.0 ( 2.13), September 20238.1, December 20182.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoAGPL, commercial license also availableOpen 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 languageScalaCC
Server operating systemsLinux
OS X
Windows
LinuxLinux
OS X
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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 functionalityyes
Secondary indexesnoyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infostandard, with numerous extensionsyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
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
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnouser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnoyesyes
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreShardingyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replication infoother methods possible by using 3rd party extensionsSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID
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 controlnofine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache Spark (SQL)CitusTimescaleDB
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