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DBMS > Quasardb vs. Spark SQL vs. STSdb vs. Tkrzw

System Properties Comparison Quasardb vs. Spark SQL vs. STSdb vs. Tkrzw

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Editorial information provided by DB-Engines
NameQuasardb  Xexclude from comparisonSpark SQL  Xexclude from comparisonSTSdb  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionDistributed, high-performance timeseries databaseSpark 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 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 modelTime Series DBMSRelational DBMSKey-value storeKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.21
Rank#322  Overall
#29  Time Series DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.10
Rank#357  Overall
#51  Key-value stores
Score0.07
Rank#372  Overall
#57  Key-value stores
Websitequasar.aispark.apache.org/­sqlgithub.com/­STSSoft/­STSdb4dbmx.net/­tkrzw
Technical documentationdoc.quasar.ai/­masterspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperquasardbApache Software FoundationSTS Soft SCMikio Hirabayashi
Initial release2009201420112020
Current release3.14.1, January 20243.5.0 ( 2.13), September 20234.0.8, September 20150.9.3, August 2020
License infoCommercial or Open Sourcecommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licensesOpen Source infoApache 2.0Open Source infoGPLv2, commercial license availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++ScalaC#C++
Server operating systemsBSD
Linux
OS X
Windows
Linux
OS X
Windows
WindowsLinux
macOS
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyes infointeger and binaryyesyes infoprimitive types and user defined types (classes)no
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 indexesyes infowith tagsnono
SQL infoSupport of SQLSQL-like query languageSQL-like DML and DDL statementsnono
APIs and other access methodsHTTP APIJDBC
ODBC
.NET Client API
Supported programming languages.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Java
Python
R
Scala
C#
Java
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresnononono
Triggersnononono
Partitioning methods infoMethods for storing different data on different nodesSharding infoconsistent hashingyes, utilizing Spark Corenonenone
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication with selectable replication factornonenonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodswith Hadoop integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infoby using LevelDByesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infoTransient modenoyes infousing specific database classes
User concepts infoAccess controlCryptographically strong user authentication and audit trailnonono

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QuasardbSpark SQLSTSdbTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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