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DBMS > Spark SQL vs. Tkrzw

System Properties Comparison Spark SQL vs. Tkrzw

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Editorial information provided by DB-Engines
NameSpark SQL  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionSpark SQL is a component on top of 'Spark Core' for structured data processingA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet
Primary database modelRelational DBMSKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score20.15
Rank#36  Overall
#22  Relational DBMS
Score0.00
Rank#389  Overall
#63  Key-value stores
Websitespark.apache.org/­sqldbmx.net/­tkrzw
Technical documentationspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationMikio Hirabayashi
Initial release20142020
Current release3.3.0 ( 2.13), June 20220.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaC++
Server operating systemsLinux
OS X
Windows
Linux
macOS
Data schemeyesschema-free
Typing infopredefined data types such as float or dateyesno
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 indexesno
SQL infoSupport of SQLSQL-like DML and DDL statementsno
APIs and other access methodsJDBC
ODBC
Supported programming languagesJava
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnono
Triggersnono
Partitioning methods infoMethods for storing different data on different nodesyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency
Foreign keys infoReferential integritynono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infousing specific database classes
User concepts infoAccess controlnono

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More resources
Spark SQLTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
Recent citations in the news

An Overview of Spark SQL
30 November 2015, insideBIGDATA

Apache Spark SQL - Market Share, Competitor Insights in Big Data Analytics
19 March 2023, 6Sense

Start Spark session with HUDI · GitHub
22 March 2023, gist.github.com

Spark SQL Explained with Examples
19 December 2022, sparkbyexamples.com

Troubleshoot Spark SQL queries in Amazon EMR
25 January 2023, AWS Blog

provided by Google News

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