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DBMS > Citus vs. LeanXcale vs. Spark SQL vs. STSdb

System Properties Comparison Citus vs. LeanXcale vs. Spark SQL vs. STSdb

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Editorial information provided by DB-Engines
NameCitus  Xexclude from comparisonLeanXcale  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionScalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQLA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesSpark SQL is a component on top of 'Spark Core' for structured data processingKey-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelRelational DBMSKey-value store
Relational DBMS
Relational DBMSKey-value store
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
Score0.36
Rank#280  Overall
#40  Key-value stores
#129  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.10
Rank#357  Overall
#51  Key-value stores
Websitewww.citusdata.comwww.leanxcale.comspark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4
Technical documentationdocs.citusdata.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperLeanXcaleApache Software FoundationSTS Soft SC
Initial release2010201520142011
Current release8.1, December 20183.5.0 ( 2.13), September 20234.0.8, September 2015
License infoCommercial or Open SourceOpen Source infoAGPL, commercial license also availablecommercialOpen Source infoApache 2.0Open Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageCScalaC#
Server operating systemsLinuxLinux
OS X
Windows
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyes infoprimitive types and user defined types (classes)
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 functionalityno
Secondary indexesyesnono
SQL infoSupport of SQLyes infostandard, with numerous extensionsyes infothrough Apache DerbySQL-like DML and DDL statementsno
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
JDBC
ODBC
.NET Client API
Supported programming languages.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
Java
Scala
Java
Python
R
Scala
C#
Java
Server-side scripts infoStored proceduresuser defined functions inforealized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.nono
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication infoother methods possible by using 3rd party extensionsnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardnono

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More resources
CitusLeanXcaleSpark SQLSTSdb
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