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

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

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Editorial information provided by DB-Engines
NameBrytlyt  Xexclude from comparisonKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionScalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQLFully vectorized database across both GPUs and CPUsSpark 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 DBMSRelational DBMSRelational DBMSKey-value store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.29
Rank#288  Overall
#131  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.04
Rank#360  Overall
#52  Key-value stores
Websitebrytlyt.iowww.kinetica.comspark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4
Technical documentationdocs.brytlyt.iodocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperBrytlytKineticaApache Software FoundationSTS Soft SC
Initial release2016201220142011
Current release5.0, August 20237.1, August 20213.5.0 ( 2.13), September 20234.0.8, September 2015
License infoCommercial or Open SourcecommercialcommercialOpen 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++ and CUDAC, C++ScalaC#
Server operating systemsLinux
OS X
Windows
LinuxLinux
OS X
Windows
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes 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 functionality.nono
Secondary indexesyesyesnono
SQL infoSupport of SQLyesSQL-like DML and DDL statementsSQL-like DML and DDL statementsno
APIs and other access methodsADO.NET
JDBC
native C library
ODBC
streaming API for large objects
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
.NET Client API
Supported programming languages.Net
C
C++
Delphi
Java
Perl
Python
Tcl
C++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C#
Java
Server-side scripts infoStored proceduresuser defined functions infoin PL/pgSQLuser defined functionsnono
Triggersyesyes infotriggers when inserted values for one or more columns fall within a specified rangenono
Partitioning methods infoMethods for storing different data on different nodesShardingyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replicationnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnonono
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 RAMno
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users and roles on table levelnono

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More resources
BrytlytKineticaSpark SQLSTSdb
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