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DBMS > Kinetica vs. Quasardb vs. Spark SQL vs. Transwarp KunDB

System Properties Comparison Kinetica vs. Quasardb vs. Spark SQL vs. Transwarp KunDB

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonQuasardb  Xexclude from comparisonSpark SQL  Xexclude from comparisonTranswarp KunDB  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsDistributed, high-performance timeseries databaseSpark SQL is a component on top of 'Spark Core' for structured data processingOLTP DBMS based on a distributed architecture and highly compatible with MySQL and Oracle
Primary database modelRelational DBMSTime Series DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.66
Rank#234  Overall
#107  Relational DBMS
Score0.21
Rank#322  Overall
#29  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.14
Rank#341  Overall
#149  Relational DBMS
Websitewww.kinetica.comquasar.aispark.apache.org/­sqlwww.transwarp.cn/­en/­product/­kundb
Technical documentationdocs.kinetica.comdoc.quasar.ai/­masterspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaquasardbApache Software FoundationTranswarp
Initial release201220092014
Current release7.1, August 20213.14.1, January 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open Sourcecommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen Source infoApache 2.0commercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++C++Scala
Server operating systemsLinuxBSD
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes infointeger and binaryyesyes
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 indexesyesyes infowith tagsnoyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query languageSQL-like DML and DDL statementsyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
HTTP APIJDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsnonoyes
Triggersyes infotriggers when inserted values for one or more columns fall within a specified rangenonoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoconsistent hashingyes, utilizing Spark Corehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationSource-replica replication with selectable replication factornone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnowith Hadoop integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infoby using LevelDByesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMyes infoTransient modeno
User concepts infoAccess controlAccess rights for users and roles on table levelCryptographically strong user authentication and audit trailnoyes

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More resources
KineticaQuasardbSpark SQLTranswarp KunDB
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