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DBMS > Kinetica vs. RisingWave vs. Spark SQL

System Properties Comparison Kinetica vs. RisingWave vs. Spark SQL

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Editorial information provided by DB-Engines
NameKinetica  Xexclude from comparisonRisingWave  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFully vectorized database across both GPUs and CPUsA distributed RDBMS for stream processing, wire-compatible with PostgreSQLSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSRelational DBMSRelational 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
Score0.58
Rank#242  Overall
#111  Relational DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitewww.kinetica.comwww.risingwave.com/­databasespark.apache.org/­sql
Technical documentationdocs.kinetica.comdocs.risingwave.com/­docs/­current/­introspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperKineticaRisingWave LabsApache Software Foundation
Initial release201220222014
Current release7.1, August 20211.2, September 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, C++RustScala
Server operating systemsLinuxDocker
Linux
macOS
Linux
OS X
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesStandard SQL-types and JSONyes
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 indexesyesyesno
SQL infoSupport of SQLSQL-like DML and DDL statementsyesSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
PostgreSQL wire protocol
JDBC
ODBC
Supported programming languagesC++
Java
JavaScript (Node.js)
Python
Go
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsUDFs in Python or Javano
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 Core
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonono
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoGPU vRAM or System RAMyesno
User concepts infoAccess controlAccess rights for users and roles on table levelUsers and Rolesno

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More resources
KineticaRisingWaveSpark SQL
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