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

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

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparisonYDB  Xexclude from comparison
DescriptionFully 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 methodA distributed fault-tolerant database service, with high availability, scalability, immediate consistency and ACID transactions and providing an Amazon DynamoDB compatible API
Primary database modelRelational DBMSRelational DBMSKey-value storeDocument store
Relational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
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
Score0.35
Rank#278  Overall
#43  Document stores
#127  Relational DBMS
Websitewww.kinetica.comspark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4github.com/­ydb-platform/­ydb
ydb.tech
Technical documentationdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.htmlydb.tech/­en/­docs
DeveloperKineticaApache Software FoundationSTS Soft SCYandex
Initial release2012201420112019
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 infoGPLv2, commercial license availableOpen Source infoApache 2.0; 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++ScalaC#
Server operating systemsLinuxLinux
OS X
Windows
WindowsLinux
Data schemeyesyesyesFlexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyesyesyes infoprimitive types and user defined types (classes)yes
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.nonono
Secondary indexesyesnonoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like DML and DDL statementsnoSQL-like query language (YQL)
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
.NET Client APIRESTful HTTP API (DynamoDB compatible)
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
C#
Java
Go
Java
JavaScript (Node.js)
PHP
Python
Server-side scripts infoStored proceduresuser defined functionsnonono
Triggersyes 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 CorenoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnonenoneActive-passive shard replication
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 configurationImmediate Consistency
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
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 levelnonoAccess rights defined for Yandex Cloud users

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More resources
KineticaSpark SQLSTSdbYDB
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