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DBMS > Kinetica vs. Prometheus vs. Spark SQL vs. STSdb

System Properties Comparison Kinetica vs. Prometheus vs. Spark SQL vs. STSdb

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonPrometheus  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsOpen-source Time Series DBMS and monitoring systemSpark 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 DBMSTime Series DBMSRelational DBMSKey-value store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.69
Rank#234  Overall
#107  Relational DBMS
Score7.92
Rank#51  Overall
#2  Time Series DBMS
Score19.15
Rank#33  Overall
#20  Relational DBMS
Score0.06
Rank#365  Overall
#55  Key-value stores
Websitewww.kinetica.comprometheus.iospark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4
Technical documentationdocs.kinetica.comprometheus.io/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaApache Software FoundationSTS Soft SC
Initial release2012201520142011
Current release7.1, August 20213.5.0 ( 2.13), September 20234.0.8, September 2015
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open 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 languageC, C++GoScalaC#
Server operating systemsLinuxLinux
Windows
Linux
OS X
Windows
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesNumeric data onlyyesyes 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.nono infoImport of XML data possibleno
Secondary indexesyesnonono
SQL infoSupport of SQLSQL-like DML and DDL statementsnoSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
RESTful HTTP/JSON APIJDBC
ODBC
.NET Client API
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
Java
Python
R
Scala
C#
Java
Server-side scripts infoStored proceduresuser defined functionsnonono
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenonono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationyes infoby Federationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationnone
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
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.yes infoGPU vRAM or System RAMnono
User concepts infoAccess controlAccess rights for users and roles on table levelnonono

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KineticaPrometheusSpark SQLSTSdb
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