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DBMS > FoundationDB vs. Kinetica vs. Spark SQL

System Properties Comparison FoundationDB vs. Kinetica vs. Spark SQL

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Editorial information provided by DB-Engines
NameFoundationDB  Xexclude from comparisonKinetica  Xexclude from comparisonSpark SQL  Xexclude from comparison
Created as commercial project in 2013, FoundationDB has been acquired by Apple in March 2015 and was withdrawn from the market. As a consequence, the product was removed from the DB-Engines ranking. In April 2018, Apple open-sourced FoundationDB and it therefore reappears in the ranking.
DescriptionOrdered key-value store. Core features are complimented by layers.Fully vectorized database across both GPUs and CPUsSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelDocument store infosupported via specific layer
Key-value store
Relational DBMS infosupported via specific SQL-layer
Relational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.50
Rank#166  Overall
#29  Document stores
#27  Key-value stores
#76  Relational DBMS
Score0.74
Rank#229  Overall
#105  Relational DBMS
Score19.56
Rank#34  Overall
#21  Relational DBMS
Websitegithub.com/­apple/­foundationdbwww.kinetica.comspark.apache.org/­sql
Technical documentationapple.github.io/­foundationdbdocs.kinetica.comspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperFoundationDBKineticaApache Software Foundation
Initial release201320122014
Current release6.2.28, November 20207.1, August 20213.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen 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, C++Scala
Server operating systemsLinux
OS X
Windows
LinuxLinux
OS X
Windows
Data schemeschema-free infosome layers support schemasyesyes
Typing infopredefined data types such as float or dateno infosome layers support typingyesyes
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
Secondary indexesnoyesno
SQL infoSupport of SQLsupported in specific SQL layer onlySQL-like DML and DDL statementsSQL-like DML and DDL statements
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
JDBC
ODBC
Supported programming languages.Net
C
C++
Go
Java
JavaScript infoNode.js
PHP
Python
Ruby
Swift
C++
Java
JavaScript (Node.js)
Python
Java
Python
R
Scala
Server-side scripts infoStored proceduresin SQL-layer onlyuser defined functionsno
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemLinearizable consistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityin SQL-layer onlyyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyes
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 RAMno
User concepts infoAccess controlnoAccess rights for users and roles on table levelno

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More resources
FoundationDBKineticaSpark SQL
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