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DBMS > Apache Spark (SQL) vs. Citus vs. Warp 10

System Properties Comparison Apache Spark (SQL) vs. Citus vs. Warp 10

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Editorial information provided by DB-Engines
NameApache Spark (SQL)  Xexclude from comparisonCitus  Xexclude from comparisonWarp 10  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 PostgreSQLTimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.39
Rank#32  Overall
#20  Relational DBMS
Score2.01
Rank#117  Overall
#57  Relational DBMS
Score0.06
Rank#355  Overall
#35  Time Series DBMS
Websitespark.apache.org/­sqlwww.citusdata.comwww.warp10.io
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.citusdata.comwww.warp10.io/­content/­02_Getting_started
DeveloperApache Software FoundationSenX
Initial release201420102015
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 availableOpen Source infoApache License 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaCJava
Server operating systemsLinux
OS X
Windows
LinuxLinux
OS X
Windows
Data schemeyesyesschema-free
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.noyes infospecific XML type available, but no XML query functionalityno
Secondary indexesnoyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsyes infostandard, with numerous extensionsno
APIs and other access methodsJDBC
ODBC
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
HTTP API
Jupyter
WebSocket
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.yes infoWarpScript
Triggersnoyesno
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark CoreShardingSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesnoneSource-replica replication infoother methods possible by using 3rd party extensionsselectable replication factor infobased on HBase
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency infobased on HBase
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDno
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.nonoyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardMandatory use of cryptographic tokens, containing fine-grained authorizations

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More resources
Apache Spark (SQL)CitusWarp 10
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